Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To evaluate the efficacy and prognostic factors of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with secondary acute myeloid leukemia (sAML) .Methods:In this multicenter, retrospective clinical study, adult patients aged ≥18 years who underwent allo-HSCT for sAML at four centers of the Zhejiang Hematopoietic Stem Cell Transplantation Collaborative Group from January 2014 to November 2022 were included, and the efficacy and prognostic factors of allo-HSCT were analyzed.Results:A total of 95 patients were enrolled; 66 (69.5%) had myelodysplastic syndrome-acute myeloid leukemia (MDS-AML) , 4 (4.2%) had MDS/MPN-AML, and 25 (26.3%) had therapy-related AML (tAML) . The 3-year CIR, LFS, and overall survival (OS) rates were 18.6% (95% CI 10.2%-27.0%) , 70.6% (95% CI 60.8%-80.4%) , and 73.3% (95% CI 63.9%-82.7%) , respectively. The 3-year CIRs of the M-AML group (including MDS-AML and MDS/MPN-AML) and the tAML group were 20.0% and 16.4%, respectively ( P=0.430) . The 3-year LFSs were 68.3% and 75.4%, respectively ( P=0.176) . The 3-year OS rates were 69.7% and 75.4%, respectively ( P=0.233) . The 3-year CIRs of the groups with and without TP53 mutations were 60.0% and 13.7%, respectively ( P=0.003) ; the 3-year LFSs were 20.0% and 76.5%, respectively ( P=0.002) ; and the 3-year OS rates were 40.0% and 77.6%, respectively ( P=0.002) . According to European LeukmiaNet 2022 (ELN2022) risk stratification, the 3-year CIRs of patients in the low-, intermediate-, and high-risk groups were 8.3%, 17.8%, and 22.6%, respectively ( P=0.639) . The three-year LFSs were 91.7%, 69.5%, and 65.6%, respectively ( P=0.268) . The 3-year OS rates were 91.7%, 71.4%, and 70.1%, respectively ( P=0.314) . Multivariate analysis revealed that advanced disease at allo-HSCT and TP53 mutations were independent risk factors for CIR, LFS, and OS. Conclusion:There was no significant difference in the prognosis of patients who underwent allo-HSCT among the MDS-AML, MDS/MPN-AML, and tAML groups. Advanced disease at transplantation and TP53 mutations were poor prognostic factors. ELN2022 risk stratification had limited value for predicting the prognosis of patients with sAML following allo-HSCT.

Objective:To study the distribution and antibiotics resistance of the main pathogens of neonatal purulent meningitis in different regions of China.Methods:A retrospective descriptive clinical epidemiological study was conducted in children with neonatal purulent meningitis which admitted to 18 tertiary hospitals in different regions of China between January 2015 to December 2019. The test results of blood and cerebrospinal fluid, and drug sensitivity test results of the main pathogens were collected. The distributions of pathogenic bacteria in children with neonatal purulent meningitis in preterm and term infants, early and late onset infants, in Zhejiang Province and other regions outside Zhejiang Province, and in Wenzhou region and other regions of Zhejiang Province were analyzed. The chi-square test was used for statistical analysis.Results:A total of 210 neonatal purulent meningitis cases were collected. The common pathogens were Escherichia coli ( E. coli)(41.4%(87/210)) and Streptococcus agalactiae ( S. agalactiae)(27.1%(57/210)). The proportion of Gram-negative bacteria in preterm infants (77.6%(45/58)) with neonatal purulent meningitis was higher than that in term infants (47.4%(72/152)), and the difference was statistically significant ( χ2=15.54, P=0.001). There were no significant differences in the constituent ratios of E. coli (36.5%(31/85) vs 44.8%(56/125)) and S. agalactiae (24.7%(21/85) vs 28.8%(36/125)) between early onset and late onset cases (both P>0.05). The most common pathogen was E. coli in different regions, with 46.7%(64/137) in Zhejiang Province and 31.5%(23/73) in other regions outside Zhejiang Province. In Zhejiang Province, S. agalactiae was detected in 49 out of 137 cases (35.8%), which was significantly higher than other regions outside Zhejiang Province (11.0%(8/73)). The proportions of Klebsiella pneumoniae, and coagulase-negative Staphylococcus in other regions outside Zhejiang Province (17.8%(13/73) and 16.4%(12/73)) were both higher than those in Zhejiang Province (2.9%(4/137) and 5.1%(7/137)). The differences were all statistically significant ( χ2=14.82, 12.26 and 7.43, respectively, all P<0.05). The proportion of Gram-positive bacteria in Wenzhou City (60.8%(31/51)) was higher than that in other regions in Zhejiang Province (38.4%(33/86)), and the difference was statistically significant ( χ2=6.46, P=0.011). E. coli was sensitive to meropenem (0/45), and 74.4%(32/43) of them were resistant to ampicillin. E. coli had different degrees of resistance to other common cephalosporins, among which, cefotaxime had the highest resistance rate of 41.8%(23/55), followed by ceftriaxone (32.4%(23/71)). S. agalactiae was sensitive to penicillin, vancomycin and linezolid. Conclusions:The composition ratios of pathogenic bacteria of neonatal purulent meningitis are different in different regions of China. The most common pathogen is E. coli, which is sensitive to meropenem, while it has different degrees of resistance to other common cephalosporins, especially to cefotaxime.

OBJECTIVE: To detect variants of IVD gene among 4 neonates with suspected isovalerate acidemia in order to provide a guidance for clinical treatment. METHODS: 111 986 newborns and 7461 hospitalized children with suspected metabolic disorders were screened for acyl carnitine by tandem mass spectrometry. Those showing a significant increase in serum isovaleryl carnitine (C5) were analyzed for urinary organic acid and variants of the IVD gene. RESULTS: Four cases of isovalerate acidemia were detected, which included 2 asymptomatic newborns (0.018‰, 2/111 986) and 2 children suspected for metabolic genetic diseases (0.268‰, 2/7461). The formers had no obvious clinical symptoms. Analysis of acyl carnitine has suggested a significant increase in C5, and urinary organic acid analysis has shown an increase in isovaleryl glycine and 3-hydroxyisovalerate. Laboratory tests of the two hospitalized children revealed high blood ammonia, hyperglycemia, decreased red blood cells, white blood cells, platelets and metabolic acidosis. The main clinical manifestations have included sweaty foot-like odor, feeding difficulty, confusion, drowsiness, and coma. Eight variants (5 types) were detected, which included c.158G>A (p.Arg53His), c.214G>A (p.Asp72Asn), c.548C>T (p.Ala183Val), c.757A>G (p.Thr253Ala) and 1208A>G (p.Tyr403Cys). Among these, c.548C>T and c.757A>G were unreported previously. None of the variants was detected by next generation sequencing of 2095 healthy newborns, and all variants were predicted to be likely pathogenic based on the guidelines from the American College of Medical Genetics and Genomics. CONCLUSION The incidence of isovalerate acidemia in Liuzhou area is quite high. Screening of metabolic genetic diseases is therefore recommended for newborns with abnormal metabolism. The discovery of novel variants has enriched the mutational spectrum of the IVD gene.
Infant, Newborn ; Child ; Humans ; Acidosis ; Carnitine ; Erythrocytes ; High-Throughput Nucleotide Sequencing

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*

Objective: To identify potential variant in a child diagnosed as infantile neuroaxonal dystrophy. Methods: Genomic DNA was extracted from peripheral blood samples from the patient and his parents and subjected to next generation sequencing. Suspected variant was verified by PCR and Sanger sequencing. Pathogenicity of the mutation was predicted by using bioinformatic software including SIFT and PolyPhen-2. Results: The child was found to carry compound heterozygous variations c. 668C>A (p.Pro223Gln) and c. 2266C>T (p.Gln756Ter) of the PLA2G6 gene, which were respectively inherited from his father and mother. c. 2266C>T has changed codon 756 (glutamine) into a stop codon, resulting premature termination of peptide chain synthesis. c. 2266C>T has not been reported previously and was predicted to be harmful. Conclusion The compound variants of c. 668C>A (p.Pro223Gln) and c. 2266C>T (p.Gln756Ter) of the PLA2G6 gene probably underlies the disease in the child. Above finding has enriched the variant spectrum of the PLA2G6 gene.

Objective: To analyze the influencing factors for bladder cancer in Taizhou,and to provide evidence for strengthening the prevention and treatment of bladder cancer in Taizhou. Methods : A total of 500 cases of bladder cancer diagnosed in Taizhou Hospital from 2012 to 2017 were selected as a case group,and 504 patients without tumor or urinary system diseases during the same period were selected as a control group. A structured questionnaire was used to retrospectively investigate the demographic information,occupational exposure（whether they were exposed to aromatic amine,polycyclic aromatic hydrocarbons,tobacco,tobacco smoke or heavy metals at work）,healthy behaviors and diets of the two groups one year before admission. A Logistic regression model was used to analyze the influencing factors for bladder cancer. Results : There was no significant differences in sex,age,ethnicity,education and marital status between the case group and the control group（P > 0.05）. Patients with occupational exposure history accounting for 31.60% in the case group and 24.60% in the control group,for overweight/obesity were 37.60% and 31.74%,for smoking were 55.80% and 46.23%,for high vegetable intake frequency were 43.80% and 52.58%,for high fruit intake frequency were 55.40% and 62.70%,for physical activity were 24.60% and 31.75%,respectively. The results of multivariate logistic regression analysis showed that occupational exposure（OR=1.861,95%CI：1.229-2.836）,overweight/obesity（OR=1.374,95%CI：1.021-1.863）,current smoking（OR=1.664,95%CI：1.101-2.503）and previous smoking（OR=1.454,95%CI：1.016-2.066）were the risk factors for bladder cancer. High vegetable intake frequency（OR=0.731,95%CI：0.566-0.947）,high fruit intake frequency（OR=0.659,95%CI：0.463-0.927）and vigorous physical activity（OR=0.566,95%CI：0.403-0.798）were the protective factors for bladder cancer. Conclusion Occupational exposure,overweight/obesity,current smoking,previous smoking were the risk factors for bladder cancer. High vegetables intake frequency,high fruit intake frequency and vigorous physical activity were the protective factors for bladder cancer.

Objective: To explore the relationship between selenium and the risk for oral cancer. Methods: We performed a case-control study in 325 cases of newly diagnosed primary oral cancer from the First Affiliated Hospital of Fujian Medical University and 650 controls from the same hospital and community. Unconditional logistic regression and stratification analyses were used to explore the association between selenium and oral cancer. Adjusted OR and corresponding 95%CI were calculated. The analyses on multiple interactions between selenium and smoking or drinking status, and fruit or fish intake frequencies were conducted. Results: The level of serum selenium was 112.42 (80.98-145.06) μg/L in the case group, which was lower than 164.85 (144.44-188.53) μg/L in control group, the difference was statistical significant (P<0.01). There was a negative correlation between serum selenium level and the risk for oral cancer regardless of smoking and drinking status, and fruits and fish intake frequencies (P<0.05). There were multiple interactions between serum selenium level and smoking or drinking status, and fruit and fish intakes. Conclusions The high level of serum selenium is a protective factor for the incidence of oral cancer, and serum selenium has multiple interactions with smoking or drinking status, and fruit and fish intakes. Therefore, reducing tobacco use and alcohol consumption and increasing the intakes of fruit and fish can reduce the risk for oral cancer to some extent.

Objective: To analyze variations of TYR and P genes among 14 patients with clinically diagnosed oculocutaneous albinism. Methods: Potential variations of the TYR and P genes were detected by Sanger sequencing. Novel variations were predicted with bioinformatics software including SIFT and PolyPhen-2. Results: No variation was found in the TYR gene, while 9 types of variations were found in the P gene among the 14 patients, which included c. 803-3C>G (7/26), c. 1327G>A (p.Val443Ile) (5/26), c. 632C>T (p.Pro211Leu) (4/26), c. 1832T>C (p.Leu611Pro) (3/26), c. 1349C>A (p.Thr450Lys) (2/26), c. 2363C>T (p.Ser788Leu) (2/26), c. 2228C>T (p.Pro743Leu) (1/26), c. 1525A>G(p.Thr509Ala) (1/26), and c. 1349C>T(p.Thr450Met) (1/26). Only 1 heterozygous variation was detected in 2 families. c. 2363C>T (p.Ser788Leu), c. 1832T>C (p.Leu611Pro) and c. 1525A>G (p.Thr509Ala) were not reported previously and predicted as "harmful" to the protein function. Conclusion The main type of ocular albinism is oculocutaneous albinism type Ⅱ in Liuzhou region, where the most common variations of the P gene were c. 803-3C>G and c. 1327G>A (p.Val443Ile). Above finding has enriched the variation spectrum of the P gene.