Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

Objective:To investigate the changes of mortality，causes of death，and cause-specific mortality rate（CMR）of hospitalized neonates in NICU of the First Affiliated Hospital of Sun Yat-sen University.Method:A retrospective study was performed to compare the mortality，cause of death，and CMR of hospitalized neonates in period Ⅰ（2005-2009），period Ⅱ（2010-2014）and period Ⅲ（2015-2020）.Result:The overall mortality of hospitalized neonates in NICU of our hospital was 0.51%（104/20 493）through 2005 to 2020. The mortality in period Ⅰ，Ⅱ and Ⅲ were 0.61%（48/7 855），0.43%（27/6 209），and 0.45%（29/6 429），respectively. Compared with period Ⅰ，the mortality of preterm infants decreased significantly in period Ⅱ（3.14% vs 1.24%， χ2=14.076， P<0.01）and in period Ⅲ（3.14% vs 0.90%， χ2=25.157， P<0.01）. Eighty-five（81.7%）neonates were premature，and ninety-one（89.2%）neonates had definite abnormal perinatal factors. The CMR of hospitalized neonates related to pulmonary hemorrhage，congenital anomalies，and NRDS were 1.22‰（25/20 493），0.93‰（19/20 493），and 0.59‰（12/20 493），respectively. The CMR of other causes were sepsis 0.44‰（9/20 493），extremely premature 0.34‰（7/20 493），and perinatal asphyxia 0.24‰（5/20 493），respectively. Compared with period Ⅰ，specific mortality of NRDS in period Ⅱ（1.27‰ vs 0.16‰， χ2=5.487， P=0.016）and period Ⅲ（1.27‰ vs 0.16‰， χ2=5.738， P=0.014）significantly decreased. The leading causes of neonatal death in period Ⅰ，period Ⅱ，and period Ⅲ were NRDS，pulmonary hemorrhage，and congenital anomalies，respectively.And 71.2%（74/104）of neonatal deaths occurred within 7 days after birth. Conclusion:The mortality of preterm infants and specific mortality of NRDS in NICU have significantly decreased over the past 16 years.Congenital anomalies and infections remain important causes of death，and further efforts are needed to improve perinatal care.

Objective:To investigate the perinatal prognosis and its impact factors for premature infants with twin-twin transfusion syndrome (TTTS) who were born at ≤34 weeks of gestation.Methods:A retrospective study was conducted on 68 pregnancies of TTTS with gestational age ≤34 weeks at delivery, among them 106 preterm infants (TTTS group) were admitted to the neonatal intensive care unit of the First Affiliated Hospital, Sun Yat-sen University from January 2003 to February 2019. During the same period, another 178 twins without TTTS, congenital malformation, and intrauterine intervention who matched the TTTS group in maternal age (differences within two years) and gestational age (differences within one week) were assigned as non-TTTS group. Perinatal prognosis of TTTS infants born at ≤34 weeks was analyzed by comparing the differences in postnatal early complications and perinatal outcomes (survival time morn than 28 days or not) between the TTTS and non-TTTS groups, recipient and donor twins, mild and severe TTTS infants, and among TTTS infants with different intrauterine interventions. The risk factors for perinatal survival in TTTS infants with gestational age ≤34 weeks were analyzed. Two independent samples t-test, one-way analysis of variance, rank-sum test, Chi-square test, and ordered logistic regression were used for statistical analysis. Results:(1) Among the 68 pregnancies, the overall perinatal survival rate of the neonates was 72.1% (98/136), the double-twin survival rate was 48.5% (33/68), and the rate of at least one survivor was 95.6% (65/68). (2) In the TTTS group, 62 were recipients and 44 were donors. Stage Ⅰ-Ⅱ TTTS was found in 41 cases (mild TTTS group) and stage Ⅲ-Ⅴ in 65 cases (severe TTTS group). (3) The rate of severe brain injury was higher in the severe-TTTS group than those in the mild-TTTS group [9.2% (6/65) vs. 0.0% (0/41), χ 2=4.01, P=0.045]. (4) Gestational age ≤28 weeks ( OR=101.90, 95% CI: 5.07-2 048.37), stage Ⅳ ( OR=14.04, 95% CI: 1.56-126.32) and stage Ⅴ TTTS ( OR=51.09, 95% CI: 3.58-728.81) were independent risk factors for death within 28 days (all P<0.05). (5) Compared with the non-TTTS group, the TTTS group had higher rates of neonatal anemia [51.9% (55/106) vs. 33.1% (59/178), χ 2=9.71], polycythemia [5.7% (6/106) vs. 0.6% (1/178), χ 2=7.18], neonatal persistent pulmonary hypertension [3.8% (4/106) vs. 0.0% (0/178), χ 2=6.81], sepsis [15.1% (16/106) vs. 7.3% (13/178), χ 2=4.40], state Ⅲ or higher retinopathy of prematurity [3.8% (4/106) vs. 0.0% (0/178), χ 2=6.81], congenital cardiac structural abnormality [19.8% (21/106) vs. 0.6% (1/178), χ 2=33.45], heart failure [8.5% (9/106) vs. 0.6% (1/178), χ 2=12.29], and renal insufficiency [14.2% (15/106) vs. 1.1% (2/178), χ 2=20.04] (all P<0.05). Conclusions:Compared with the twin premature infants without TTTS, those with TTTS and ≤34 gestational age were more likely to have cardiac, cerebral, and renal complications. The more severe the TTTS, the higher the incidence of severe brain injury. TTTS preterm infants with gestational age ≤28 weeks and stage Ⅳ or above have high risk of death.

Objective:To compare the results of invasive prenatal diagnosis and preimplantation genetic testing (PGT) and explore the underlying mechanism.Methods:Clinical data of pregnant women undergoing PGT and invasive prenatal diagnosis at the Sixth Affiliated Hospital of Sun Yat-sen University from January 2019 to December 2022 were collected. The results of PGT and invasive prenatal diagnosis were compared, and the outcomes of pregnancies were followed up. This study has been approved by the Medical Ethics Committee of the the Sixth Affiliated Hospital of Sun Yat-sen University (No. 2022SLYEC-491).Results:A total of 172 couples were included in this study, and 26 non-targeted variants were discovered upon prenatal diagnosis, including 10 cases (38.5%) by chromosomal karyotyping, 15 (57.7%) by chromosomal microarray analysis (CMA), and 1 (3.8%) by whole exome sequencing. The 10 karyotypic anomalies had included 6 chromosomal polymorphisms, 2 chromosomal mosaicisms, 1 paternally derived translocation, and 1 missed maternal chromosomal inversion. CMA has identified 15 copy number variations (CNVs), which included 11 microdeletions and microduplications, 3 loss of heterozygosity, and 1 low-level mosaicism of paternal uniparental disomy. One CNV was classified as pathogenic, and another one was likely pathogenic, whilst the remaining 13 were classified as variants of uncertain significance. Therefore, 8.7% of CNVs was detected by invasive prenatal diagnosis after PGT. 92.3% (24/26) of the non-targeted variants have been due to technological limitations of next-generation sequencing (NGS).Conclusion:Invasive prenatal diagnosis after PGT can detect non-targeted variants, which may further reduce the incidence of birth defects.

Complete androgen insensitivity syndrome(CAIS) is characterized by lack of androgen response in target organs due to androgen receptor dysfunction, resulting in feminized external genitalia. Individuals with CAIS are typically advised to live as females. This article reports a patient diagnosed with CAIS and gender dysphoria in adulthood. Following the removal of a left pelvic mass, pathology indicated cryptorchidism with a concurrent Leydig cell tumor. Genetic testing revealed a deletion mutation in exon 3 of androgen receptor gene. During follow-up, the patient underwent gender reassignment, transitioning socially from female to male. This case provides new insights into gender allocation for CAIS patients.

A 27-year-old male was admitted to the Xijing Hospital in August 2018 due to unprovoked severe thoracodynia with palpitations, shortness of breath and chest tightness. Computed tomography angiography showed a type A aortic dissection. Genetic testing based on next-generation sequencing for 15 genes associated with hereditary aortic diseases and Sanger sequencing validation revealed a heterozygous missense mutation c.998G>T (p.Gly333Val) in the COL3A1 gene. Sanger sequencing verification of family members confirmed that the mutation c.998G>T co-segregated with the patient′s phenotype in this family. That mutation was classified as "likely pathogenic" according to American College of Medical Genetics and Genomics standards and guidelines for genetic variant classification. Carriers of this mutation can be definitively diagnosed with "vascular Ehlers-Danlos syndrome". After the diagnosis was clarified, symptomatic treatment was given to the patient, but the disease progressed rapidly. The patient discontinued treatment and died shortly after being discharged. In this study, we found a new variant in the COL3A1 gene, expanding the mutation spectrum of this gene.

Objective To explore the application value of deep learning image reconstruction(DLIR)combined with Smart metal artifact reduction(Smart MAR)algorithm in maxillofacial CT images.Methods A total of 34 patients with maxillofacial lesions affected by oral metal implants who underwent maxillofacial enhanced CT scans were included.The images of four groups in venous phase were reconstructed with 50％adaptive statistical iterative reconstruction(ASIR-V)(IR group),50％ASIR-V combined with Smart MAR(IR+S group),DLIR(at medium strength)combined with Smart MAR(D-M+S group)and DLIR(at high strength)combined with Smart MAR(D-H+S group)respectively.The artifact index(AI)was worked out by measuring the standard deviation(SD)of CT values in maxillofacial lesions and longhead muscle.The subjective scores of overall image quality,lesion conspicuity and diagnostic confidence were assessed.The image quality of different algorithms was compared.Results Compared with IR+S group,the AI value of IR group was significantly increased(P＜0.05),while the noise had no significant difference(P＞0.05).Compared with IR+S group,the AI value and noise of D-M+S group and D-H+S group both were significantly decreased(P＜0.05),and the AI value of D-M+S group and D-H+S group reduced by 13.70％and 19.06％respectively,the noise reduced by 16.37％and 30.78％respectively.The subjective scores of overall image quality,lesion conspicuity and diagnostic confidence in IR+S group were significantly lower than those in D-M+S group and D-H+S group,but significantly higher than those in IR group(P＜0.05).There were 6 patients'(17.64％)lesions were detected only in the groups with Smart MAR algorithm,while 9 patients(26.47％)had introduced new artifacts in the tongue with Smart MAR algorithm.Conclusion DLIR combined with Smart MAR can improve the CT image quality of maxillofacial region,enhance the conspicuity and diagnosis confidence of maxillofacial lesions in patients with oral metal implants.Smart MAR algorithm may produce new artifacts that need to be analyzed along with the images not added Smart MAR algorithm.

Essential oils (EOs) are natural, volatile substances derived from aromatic plants. They exhibit multiple pharmacological effects, including antibacterial, anticancer, anti-inflammatory, and antioxidant properties, with broad application prospects in health care, food, and agriculture. However, the instability of volatile components, which are susceptible to deterioration under light, heat, and oxygen exposure, as well as limited water solubility, have significantly impeded the development and application of EOs. Porous nanoclays are natural clay minerals with a layered structure. They possess unique structural characteristics such as large pore size, regular distribution, and tunable particle size, which are extensively utilized in drug delivery, adsorption separation, reaction catalysis, and other fields. Natural-derived porous nanoclays have garnered considerable attention for the encapsulation and delivery of EOs. This review comprehensively summarizes the structure, types, and properties of natural-derived porous nanoclays, focusing on the structural characteristics of porous nanoclays such as montmorillonite, palygorskite, halloysite, kaolinite, vermiculite, and natural zeolite. It also examines research advances in their delivery of EOs and explores engineering strategies to enhance the delivery of EOs by natural-derived porous nanoclays. Finally, various applications of natural-derived porous nanoclays for EOs in antibacterial, food preservation, repellent, and insecticide aspects are presented, providing a reference for the development and application of EOs.
Humans ; Nanoparticles/chemistry* ; Oils, Volatile/administration & dosage* ; Porosity ; Nanoparticle Drug Delivery System/chemistry*

BACKGROUND: Congenital heart disease (CHD) is one of the most common congenital malformations in humans. Inconsistent results emerged in the existed studies on associations between air pollution and congenital heart disease. The purpose of this study was to evaluate the association of gestational exposure to air pollutants with congenital heart disease, and to explore the critical exposure windows for congenital heart disease. METHODS: The nested case-control study collected birth records and the following health data in Tianjin Women and Children's Health Center, China. All of the cases of congenital heart disease from 2013 to 2015 were selected matching five healthy controls for each case. Inverse distance weighting was used to estimate individual exposure based on daily air pollution data. Furthermore, the conditional logistic regression with distributed lag non-linear model was performed to identify the association between gestational exposure to air pollution and congenital heart disease. RESULTS: A total of 8,748 mother-infant pairs were entered into the analysis, of which 1,458 infants suffered from congenital heart disease. For each 10 µg/m³ increase of gestational exposure to PM_2.5, the ORs (95% confidence interval, 95%CI) ranged from 1.008 (1.001-1.016) to 1.013 (1.001-1.024) during the 1^st-2^nd gestation weeks. Similar weak but increased risks of congenital heart disease were associated with O₃ exposure during the 1^st week and SO₂ exposure during 6^th-7^th weeks in the first trimester, while no significant findings for other air pollutants. CONCLUSIONS This study highlighted that gestational exposure to PM_2.5, O₃, and SO₂ had lag effects on congenital heart disease. Our results support potential benefits for pregnancy women to the mitigation of air pollution exposure in the early stage, especially when a critical exposure time window of air pollutants may precede heart development.
Infant ; Pregnancy ; Child ; Humans ; Female ; Air Pollutants/analysis* ; Case-Control Studies ; Prenatal Exposure Delayed Effects/epidemiology* ; Heart Defects, Congenital/etiology* ; China/epidemiology* ; Particulate Matter/adverse effects* ; Maternal Exposure/adverse effects*

Objective:To construct a diabetic foot classification prediction model based on radiomics features of fundus photographs.Methods:A total of 2 035 fundus photographs of patients with type 2 diabetes diagnosed at Nanfang Hospital between December 2011 and December 2018 were retrospectively collected [282 photographs from patients with diabetic foot(DF), and 1 753 from patients with diabetes mellitus(DM)]. All fundus photographs were randomly divided into a training set(1 424 photos) and a test set(611 photos) using a computer generated random number at 7∶3. After pre-processing the fundus photographs, a total of 4 128 texture features based on the gray matrix were extracted by the Radiomic toolkit, and 11 339 other features were extracted using the ToolboxDESC toolkit. The LASSO algorithm was used to select the 30 features most relevant to DF, and then the Bootstrap + 0.632 self-sampling method was used to further select the 7 best combinations. Logistic regression analysis was used to obtain the regression coefficients and establish the final diabetic foot classification prediction model. ROC curve was drawn, and AUC, sensitivity, specificity, and accuracy of the training and test sets were calculated to verify its prediction performance. Results:We screened 7 fundus radiomics markers for diabetic foot patients, and based on this established a DF/DM classification prediction model. The AUC, sensitivity, specificity, and accuracy of the model were 0.958 6, 0.984 0, 0.920 0, and 0.928 0 in the training set, and 0.927 1, 0.988 9, 0.881 0, and 0.896 9 in the test set, respectively.Conclusion:In this study, seven DF fundus markers were screened using radiomics technology. Based on this, a highly accurate and easy-to-use DF/DM classification model was constructed. This technology has the potential to increase the efficiency of DF screening programs.