Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

DiGeorge(DGS)syndrome is an autosomal dominant disorder caused by 22q11.2 microdeletions,most patients developed the disease in childhood.22q11.2 deletion syndrome,and the mutation types are dominated by haploid deletion of this gene.We report a young patient with hypoparathyroidism(parathyroidism)induced by DGS syndrome combined with hypocalcemic heart failure.Genetic testing revealed pathogenic copy number variants associated with the clinical phenotype of the subject.About 2 674 kb of deletion variation was detected at q11.21 position on chromosome 22,which contained the TBX1 gene and was a pathogenic mutation.This paper discusses the clinical features,pathogenesis and current treatment of DGS,and emphasizes the importance of early screening,early diagnosis and treatment,and regular follow-up of heart failure,aiming to enhance the awareness of clinicians and geneticists on DGS syndrome.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

DiGeorge(DGS)syndrome is an autosomal dominant disorder caused by 22q11.2 microdeletions,most patients developed the disease in childhood.22q11.2 deletion syndrome,and the mutation types are dominated by haploid deletion of this gene.We report a young patient with hypoparathyroidism(parathyroidism)induced by DGS syndrome combined with hypocalcemic heart failure.Genetic testing revealed pathogenic copy number variants associated with the clinical phenotype of the subject.About 2 674 kb of deletion variation was detected at q11.21 position on chromosome 22,which contained the TBX1 gene and was a pathogenic mutation.This paper discusses the clinical features,pathogenesis and current treatment of DGS,and emphasizes the importance of early screening,early diagnosis and treatment,and regular follow-up of heart failure,aiming to enhance the awareness of clinicians and geneticists on DGS syndrome.

Objective To study the mechanism of polyene phosphatidylcholine in improving metabolic disorders and fatty liver induced by high fat diet.Methods Thirty-two C57BL/6 mice were randomly divided into blank group,control group,model group and experimental group.The blank group was fed with low-fat diet and intraperitoneal injection of 10％glucose 200 μL twice a week.Control group was fed with low-fat diet twice a week and intraperitoneally injected 10％glucose solution 200 μL containing polyene phosphatidylcholine(PPC)20 μg.Model group was fed with high-fat diet and intraperitoneal injection of 10％glucose 200 μL twice a week.Experimental group was fed with high-fat diet twice a week and intraperitoneally injected 10％glucose solution 200 μL containing PPC 20 μg.The body weight of the mice was measured,blood glucose test strips and insulin resistance was analyzed.The levels of triglyceride(TG),high density lipoprotein(HDL),low density lipoprotein(LDL),glutamic oxalic aminotransferase(GOT)and glutamic pyruvic aminotransferase(GPT)in serum and liver were analyzed by biochemical method.The levels of tumor necrosis factor-α(TNF-α),interleukin-6 and IL-8 were detected by enzyme-linked immunosorbent assay(ELISA).Results The serum TG levels of blank group,control group,model group and experimental group were(0.15±0.01),(0.11±0.01),(0.21±0.01)and(0.12±0.01)mmol·L-1;LDL levels were(0.41±0.01),(0.25±0.01),(0.71±0.02)and(0.49±0.01)mmol·L-1;GOT levels were(30.30±0.89),(31.39±1.18),(43.04±2.82)and(25.64±0.72)mmol·L-1;GPT levels were(9.15±0.45),(7.39±1.88),(12.87±1.81)and(7.96±1.64)mmol·L-1;fasting blood glucose levels were(4.97±0.08),(6.08±0.18),(8.12±0.20)and(7.29±0.02)mmol·L-1;fasting insulin levels were(6.52±1.11),(5.45±0.28),(54.83±4.32)and(30.55±2.73)mU·L-1;the levels of TNF-α in liver tissues were(3.98±0.63),(3.95±0.98),(20.55±4.71)and(15.28±1.73)pg·g-1;IL-6 levels were(18.93±8.56),(17.64±3.29),(59.40±4.63)and(37.54±7.33)pg·g-1;IL-8 levels were(67.16±12.37),(59.44±3.58),(198.40±9.27)and(132.10±7.04)pg·g-1.The difference of above indicatory between experimental group and model group was statistically significant(all P＜0.05).Conclusion Polyene phosphatidylcholine may inhibit the expression of TNF-α,IL-6 and IL-8 inflammatory factors by mediating the inhibition of inflammation on liver tissue and then improve metabolic disorders.

AIM To evaluate the quality of Yanyangke Mixture.METHODS The HPLC fingerprints were established,after which cluster analysis,principal component analysis and partial least squares discriminant analysis were performed.The contents of liquiritin,rosmarinic acid,sheganoside,irisgenin,honokiol,monoammonium glycyrrhizinate,irisflorentin,isoliquiritin and magnolol were determined,the analysis was performed on a 35 ℃ thermostatic Agilent ZORBAX SB-C18 column(5 μm,250 mmx4.6 mm),with the mobile phase comprising of 0.1％phosphoric acid-acetonitrile flowing at 1 mL/min in a gradient elution manner,and multi-wavelength detection was adopted.RESULTS There were ten common peaks in the fingerprints for twelve batches of samples with the similarities of more than 0.9.Various batches of samples were clustered into three types,three principal components displayed the acumulative variance contribution rate of 87.448％,peaks 5、14(honokiol),3(liquiritin),11(monoammonium glycyrrhizinate)and 15(asarinin)were quality markers.Nine constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 98.5％-103.6％with the RSDs of 0.92％-1.7％.CONCLUSION This stable and reliable method can provide a basis for the quality control of Yanyangke Mixture.

Objective: To investigate the lifespan of erythrocytes in megaloblastic anemia (MA) patients. Methods: A prospective cohort study analysis. Clinical data from 42 MA patients who were newly diagnosed at the Department of Hematology, Lanzhou University Second Hospital from January 2021 to August 2021 were analyzed, as were control data from 24 healthy volunteers acquired during the same period. The carbon monoxide breath test was used to measure erythrocyte lifespan, and correlations between erythrocyte lifespan and laboratory test indexes before and after treatment were calculated. Statistical analysis included the t-test and Pearson correlation. Results: The mean erythrocyte lifespan in the 42 newly diagnosed MA patients was (49.05±41.60) d, which was significantly shorter than that in the healthy control group [(104.13±42.62) d; t=5.13,P=0.001]. In a vitamin B₁₂-deficient subset of MA patients the mean erythrocyte lifespan was (30.09±15.14) d, and in a folic acid-deficient subgroup it was (72.00±51.44) d, and the difference between these two MA subsets was significant (t=3.73, P=0.001). The mean erythrocyte lifespan after MA treatment was (101.28±33.02) d, which differed significantly from that before MA treatment (t=4.72, P=0.001). In MA patients erythrocyte lifespan was positively correlated with hemoglobin concentration (r=0.373), and negatively correlated with total bilirubin level (r=-0.425), indirect bilirubin level (r=-0.431), and lactate dehydrogenase level (r=-0.504) (all P<0.05). Conclusions: Erythrocyte lifespan was shortened in MA patients, and there was a significant difference between a vitamin B₁₂-deficient group and a folic acid-deficient group. After treatment the erythrocyte lifespan can return to normal. Erythrocyte lifespan is expected to become an informative index for the diagnosis and treatment of MA.
Humans ; Longevity ; Clinical Relevance ; Prospective Studies ; Erythrocytes ; Anemia, Megaloblastic ; Folic Acid ; Bilirubin ; Vitamins

Circular RNAs (circRNAs) are highly conserved and ubiquitously expressed noncoding RNAs that participate in multiple reproduction-related diseases. However, the expression pattern and potential functions of circRNAs in the testes of patients with non-obstructive azoospermia (NOA) remain elusive. In this study, according to a circRNA array, a total of 37 881 circRNAs were identified that were differentially expressed in the testes of NOA patients compared with normal controls, including 19 874 upregulated circRNAs and 18 007 downregulated circRNAs. Using quantitative real-time polymerase chain reaction (qRT-PCR) analysis, we confirmed that the change tendency of some specific circRNAs, including hsa_circ_0137890, hsa_circ_0136298, and hsa_circ_0007273, was consistent with the microarray data in another larger sample. The structures and characteristics of these circRNAs were confirmed by Sanger sequencing, and fluorescence in situ hybridization revealed that these circRNAs were primarily expressed in the cytoplasm. Bioinformatics analysis was used to construct the competing endogenous RNA (ceRNA) network, and numerous miRNAs that could be paired with circRNAs validated in this study were reported to be vital for spermatogenesis regulation. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that genes involved in axoneme assembly, microtubule-based processes, and cell proliferation were significantly enriched. Our data suggest that there are aberrantly expressed circRNA profiles in patients with NOA and that these circRNAs may help identify key diagnostic and therapeutic molecular biomarkers for NOA patients.
Male ; Humans ; RNA, Circular/genetics* ; Azoospermia/genetics* ; In Situ Hybridization, Fluorescence ; MicroRNAs/metabolism*

Objective: To analyze the clinical epidemiological characteristics including composition of pathogens , clinical characteristics, and disease prognosis acute bacterial meningitis (ABM) in Chinese children. Methods: A retrospective analysis was performed on the clinical and laboratory data of 1 610 children <15 years of age with ABM in 33 tertiary hospitals in China from January 2019 to December 2020. Patients were divided into different groups according to age,<28 days group, 28 days to <3 months group, 3 months to <1 year group, 1-<5 years of age group, 5-<15 years of age group; etiology confirmed group and clinically diagnosed group according to etiology diagnosis. Non-numeric variables were analyzed with the Chi-square test or Fisher's exact test, while non-normal distrituction numeric variables were compared with nonparametric test. Results: Among 1 610 children with ABM, 955 were male and 650 were female (5 cases were not provided with gender information), and the age of onset was 1.5 (0.5, 5.5) months. There were 588 cases age from <28 days, 462 cases age from 28 days to <3 months, 302 cases age from 3 months to <1 year of age group, 156 cases in the 1-<5 years of age and 101 cases in the 5-<15 years of age. The detection rates were 38.8% (95/245) and 31.5% (70/222) of Escherichia coli and 27.8% (68/245) and 35.1% (78/222) of Streptococcus agalactiae in infants younger than 28 days of age and 28 days to 3 months of age; the detection rates of Streptococcus pneumonia, Escherichia coli, and Streptococcus agalactiae were 34.3% (61/178), 14.0% (25/178) and 13.5% (24/178) in the 3 months of age to <1 year of age group; the dominant pathogens were Streptococcus pneumoniae and the detection rate were 67.9% (74/109) and 44.4% (16/36) in the 1-<5 years of age and 5-<15 years of age . There were 9.7% (19/195) strains of Escherichia coli producing ultra-broad-spectrum β-lactamases. The positive rates of cerebrospinal fluid (CSF) culture and blood culture were 32.2% (515/1 598) and 25.0% (400/1 598), while 38.2% (126/330)and 25.3% (21/83) in CSF metagenomics next generation sequencing and Streptococcus pneumoniae antigen detection. There were 4.3% (32/790) cases of which CSF white blood cell counts were normal in etiology confirmed group. Among 1 610 children with ABM, main intracranial imaging complications were subdural effusion and (or) empyema in 349 cases (21.7%), hydrocephalus in 233 cases (14.5%), brain abscess in 178 cases (11.1%), and other cerebrovascular diseases, including encephalomalacia, cerebral infarction, and encephalatrophy, in 174 cases (10.8%). Among the 166 cases (10.3%) with unfavorable outcome, 32 cases (2.0%) died among whom 24 cases died before 1 year of age, and 37 cases (2.3%) had recurrence among whom 25 cases had recurrence within 3 weeks. The incidences of subdural effusion and (or) empyema, brain abscess and ependymitis in the etiology confirmed group were significantly higher than those in the clinically diagnosed group (26.2% (207/790) vs. 17.3% (142/820), 13.0% (103/790) vs. 9.1% (75/820), 4.6% (36/790) vs. 2.7% (22/820), χ²=18.71, 6.20, 4.07, all P<0.05), but there was no significant difference in the unfavorable outcomes, mortility, and recurrence between these 2 groups (all P>0.05). Conclusions: The onset age of ABM in children is usually within 1 year of age, especially <3 months. The common pathogens in infants <3 months of age are Escherichia coli and Streptococcus agalactiae, and the dominant pathogen in infant ≥3 months is Streptococcus pneumoniae. Subdural effusion and (or) empyema and hydrocephalus are common complications. ABM should not be excluded even if CSF white blood cell counts is within normal range. Standardized bacteriological examination should be paid more attention to increase the pathogenic detection rate. Non-culture CSF detection methods may facilitate the pathogenic diagnosis.
Adolescent ; Brain Abscess ; Child ; Child, Preschool ; Escherichia coli ; Female ; Humans ; Hydrocephalus ; Infant ; Infant, Newborn ; Male ; Meningitis, Bacterial/epidemiology* ; Retrospective Studies ; Streptococcus agalactiae ; Streptococcus pneumoniae ; Subdural Effusion ; beta-Lactamases