Automated analysis of fetal heart rate baseline/acceleration/deceleration using MTU-Net3 + model.

In clinical practice, obstetricians use visual interpretation of fetal heart rate (FHR) to diagnose fetal conditions, but inconsistencies among interpretations can hinder accuracy. This study introduces MTU-Net3+, a deep learning model designed for automated, multi-task FHR analysis, aiming to improve diagnostic accuracy and efficiency. The proposed MTU-Net3 + was built upon the UNet3 + architecture, incorporating an encoder, a decoder, full-scale skip connections, and a deep supervision module, and further integrates a self-attention mechanism and bidirectional Long Short-Term Memory layers to enhance its performance. The MTU-Net3 + model accepts the preprocessed 20-minute FHR signals as input, outputting categorical probabilities and baseline values for each time point. The proposed MTU-Net3 + model was trained on a subset of a public database, and was tested on the remaining data of the public database and a private database. In the remaining public datasets, this model achieved F1 scores of 84.21% for deceleration (F1.Dec) and 61.33% for acceleration (F1.Acc), with a Root Mean Square Baseline Difference (RMSD.BL) of 3.46 bpm, 0% of points with an absolute difference exceeding 15 bpm(D15bpm), a Synthetic Inconsistency Coefficient (SI) of 44.82%, and a Morphological Analysis Discordance Index (MADI) of 7.00%. On the private dataset, the model recorded an RMSD.BL of 1.37 bpm, 0% D15bpm, F1.Dec of 100%, F1.Acc of 87.50%, an SI of 12.20% and a MADI of 2.79%. The MTU-Net3 + model proposed in this study performed well in automated FHR analysis, demonstrating its potential as an effective tool in the field of fetal health assessment.

Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.

BACKGROUND: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system. METHODS: GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests. FINDINGS: Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860). CONCLUSIONS: With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures. FUNDING: This study was supported by the National Key R&D Program and the National Natural Science Foundation of China.

The causal relationship between thoracic aortic aneurysm and immune cells: a mendelian randomization study.

One of the pathogenic causes of thoracic aortic aneurysm (TAA), a dangerous vascular condition that can cause aortic rupture, is autoimmune disorders. Currently, immune cell clustering is becoming more and more refined, and the specific immune cell phenotypes involved are yet unknown. Here, we want to clarify the causal link between TAA risk and 731 immune cell traits. There was a Mendelian randomization analysis (MR). We discovered that the presence of TAA led to an increase in CD45 on CD33- HLA-DR- myeloid cells, an increase in CD45 on natural killer cells, and a decrease in FSC-A on granulocytes after applying FDR correction. Our research also revealed a strong correlation between the incidence of TAA and an increase in immune cells with CD3 on CD39+ CD4+, and CD25 on IgD- CD27- phenotypes. Through genetic techniques, our research has shown the intimate relationship between immune cells and TAA, offering direction for future clinical investigations.

Pregnancy and neonatal outcomes in 25 pregnant women diagnosed with new-onset acute myeloid leukemia during pregnancy.

PURPOSE: The aim was to analyze the pregnancy and neonatal outcomes of pregnant women with new- onset acute myeloid leukemia (AML) diagnosed during pregnancy. METHODS: In this retrospective study 25 pregnant women who were diagnosed with new-onset AML during pregnancy from January 2010 to January 2021 were enrolled. RESULTS: A total of 4, 13 and 8 pregnant women with new-onset AML were diagnosed during the first, second, and third trimesters, respectively. Twelve of the 25 pregnant women underwent therapeutic abortion and 13 gave birth (9 preterm and 4 full-term newborns). The gestational age at initial clinical manifestations (13.4 ± 3.7 vs. 27.7 ± 5.6 weeks, P < 0.01) and diagnosis (16.9 ± 4.4 vs. 29.7 ± 5.5 weeks, P < 0.01) was lower in the pregnant women who underwent therapeutic abortion than in those who gave birth. Eighty-four percent (21/25) of the pregnant women with new-onset AML during pregnancy survived and were in remission and all the newborns were born alive. Three of the 13 newborns were exposed to chemotherapy, but no congenital malformations were observed. Eight newborns were admitted to the neonatal intensive care unit (NICU), and all recovered. The complete blood counts and biochemical examinations of the 8 newborns were normal. CONCLUSIONS: New-onset AML during an earlier stage of pregnancy may increase the risk of poor pregnancy outcomes. The neonatal outcomes of pregnant women with new-onset AML during pregnancy are good with proper treatment.

Neuritin Alleviates Diabetic Retinopathy by Regulating Endoplasmic Reticulum Stress in Rats.

BACKGROUND: Neuritin, a small-molecule neurotrophic factor, maintains neuronal cell activity, inhibits apoptosis, promotes process growth, and regulates neural progenitor cell differentiation, migration, and synaptic maturation. Neuritin helps retinal ganglion cells (RGCs) survive optic nerve injury in rats and regenerate axons. However, the role of Neuritin in Diabetic retinopathy (DR) is unclear. OBJECTIVE: This study is intended to investigate the effect and mechanism of Neuritin in DR. For this purpose, we established DR rat models and injected Neuritin into them. This study provides a potential treatment for diabetic retinopathy. METHODS: The rat model of DR was established by streptozotocin (STZ) injection, and the effect of Neuritin on DR was detected by intravitreal injection. Histological analysis was performed by H&E and TUNEL methods. The mRNA and protein expressions of endoplasmic reticulum stress (ERS) pathway-related transcription factors were detected by qRT-PCR and western blot. The blood-retinal barrier (BRB) function was assessed using the patch-clamp technique and Evans blue leakage assay. RESULTS: Neuritin significantly improved the retinal structure, restrained the apoptosis of retinal cells, and protected the normal function of BRB in DR model rats. Mechanistically, Neuritin may function by inhibiting the expression of GRP78, ASK1, Caspase-12, VEGF, and so on. CONCLUSION: Our results indicate that Neuritin alleviates retinal damage in DR rats via the inactive endoplasmic reticulum pathway. Our study provides a potential treatment for DR.

Detecting Double Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning.

BACKGROUND: Double expression lymphoma (DEL) is a subtype of primary central nervous system lymphoma (PCNSL) that often has a poor prognosis. Currently, there are limited noninvasive ways to detect protein expression. PURPOSE: To detect DEL in PCNSL using multiparametric MRI-based machine learning. STUDY TYPE: Retrospective. POPULATION: Forty PCNSL patients were enrolled in the study among whom 17 were DEL (9 males and 8 females, 61.29 ± 14.14 years) and 23 were non-DEL (14 males and 9 females, 55.57 ± 14.16 years) with 59 lesions (28 DEL and 31 non-DEL). FIELD STRENGTH/SEQUENCE: ADC map derived from DWI (b = 0/1000 s/mm2 ), fast spin echo T2WI, T2FLAIR, and contrast-enhanced T1 weighted imaging (T1CE) were collected at 3.0 T. ASSESSMENT: Two raters manually segmented lesions by ITK-SNAP on ADC, T2WI, T2FLAIR and T1CE. A total of 2234 radiomics features from the tumor segmentation area were extracted. The t-test was conducted to filter the features, and elastic net regression algorithm combined with recursive feature elimination was used to calculate the essential features. Finally, 12 groups with combinations of different sequences were fitted to 6 classifiers, and the optimal models were selected. STATISTICAL TESTS: Continuous variables were assessed by the t-test, while categorical variables were assessed by the non-parametric test. Interclass correlation coefficient tested variables' consistency. Sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) were used to evaluate model performance. RESULTS: DEL status could be identified to varying degrees with 72 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Both SVMlinear and logistic regression (LR) combined with four sequence group had similar largest AUCmean (0.92 ± 0.09 vs. 0.92 ± 0.05), and SVMlinear was considered as the optimal model in this study since the F1-score of SVMlinear (0.88) was higher than that of LR (0.83). DATA CONCLUSION: Multiparametric MRI-based machine learning is promising in DEL detection. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY STAGE: 2.

Oxygen vacancy-rich Ag/CuO nanoarray mesh fabricated by laser ablation for efficient bacterial inactivation.

The contamination of drinking water by microbes is a critical health concern, underscoring the need for safe, reliable, and efficient methods to treat pathogenic microorganisms. While most sterilization materials are available in powder form, this presents safety risks and challenges in recycling. Herein, this study reports the preparation of an innovative copper oxide supported silver monolithic nanoarray mesh with abundant oxygen vacancies (Ag/CuO-VO) by laser ablation. The instantaneous high temperature caused by laser ablation preserves the material's original structure while generating oxygen vacancies on the CuO surface. The Ag/CuO-VO mesh demonstrated a remarkable ability to inactivate over 99% of Escherichia coli (E. Coli) within 20 min. The oxygen vacancies in the Ag/CuO-VO enhance interactions between oxygen species and the Ag/CuO-VO, leading to the accumulation of large amounts of reactive oxygen species (ROS). The generated ROS effectively disrupt both layers of the bacterial cell wall - the peptidoglycan and the phospholipid - as confirmed by Fourier Transform Infrared (FTIR) spectroscopy, culminating in cell death. This research presents a monolithic material capable of inactivating pathogenic microorganisms efficiently, offering a significant advancement in water sterilization technology.

Home monitoring for clinically suspected obstructive sleep apnea in pregnancy.

STUDY OBJECTIVES: To determine if a home sleep apnea test (HSAT) using a type III portable monitor (PM), Nox-T3 (Nox Medical, Inc., Reykjavik, Iceland), detects obstructive sleep apnea in pregnant women. METHODS: Ninety-two pregnant women (34.5 ± 4.3 years; gestational age 25.4 ± 8.9 weeks; body mass index 29.9 ± 4.7 kg/m2) with suspected obstructive sleep apnea underwent HSAT with the Nox-T3 PM followed by overnight polysomnography (PSG) and PM recording simultaneously in the laboratory within 1 week. PMs were scored automatically and manually using a 3% criteria and compared with PSGs scored by following guidelines. RESULTS: Apnea-hypopnea indexes were 8.56 ± 10.42, 8.19 ± 13.79, and 8.71 ± 14.19 events/h on HSAT, in-laboratory PM recording, and PSG (P = .955), respectively. Bland-Altman analysis of the apnea-hypopnea index on PSG vs HSAT showed a mean difference (95% confidence interval) of -0.15 (-1.83, 1.53); limits of agreement (± 2 SD) were -16.26 to 16.56 events/h. Based on a threshold apnea-hypopnea index ≥ 5 events/h, HSAT had 91% sensitivity, 85% specificity, 84% positive-predictive value, and 92% negative-predictive value compared with PSG. When comparing the simultaneous recordings, closer agreement was observed. Automated vs manual analysis of PM showed no significant difference. CONCLUSIONS: A type III PM had an acceptable failure rate and high diagnostic performance operating as a reasonable alternative for in-laboratory PSG in pregnant women. CITATION: Wang J, Zhang C, Xu L, et al. Home monitoring for clinically suspected obstructive sleep apnea in pregnancy. J Clin Sleep Med. 2023;19(11):1951-1960.

The prenatal diagnosis and prognosis of fetal right aortic arch and double aortic arch malformation: A single-center study.

AIM: This study aimed to characterize the pathological types, diagnosis, chromosomal abnormalities, and postnatal clinical manifestations of right and double aortic arch malformations in fetuses. METHODS: In this retrospective study, all fetuses diagnosed with right or double aortic arch anomalies for whom conventional two-dimensional echocardiography combined with spatio-temporal image correlation was performed at our tertiary referral center between December 2012 and December 2021 were included. RESULTS: In total, 234 fetuses with aortic arch abnormalities were identified. Forty-one cases lost to follow-up. One hundred ninety-three cases were included in this study. One hundred eighty-seven cases with right aortic arch. Six cases with double aortic arch. Most cases of right aortic arch with aberrant left subclavian artery (77/101, 76.2%) were isolated lesions, whereas most of those with mirror-image branching (45/75, 60%) were associated with intracardiac or extracardiac anomalies. Chromosomal abnormalities were screened prenatally in 113 fetuses with right aortic arch, among whom three with aberrant left subclavian artery (3/63, 4.8%) and eight with mirror-image branching (8/50, 16%) had chromosome anomalies (p < 0.05). Furthermore, three cases had microdeletion 22q11.2 and these were significantly associated with intracardiac malformations. CONCLUSIONS: Most cases of isolated right aortic arch do not present with clinical symptoms except isolated left subclavian artery and isolated left brachiocephalic trunk. In addition, the risk of chromosomal abnormalities in patients with isolated right aortic arch is very low. We recommend that pregnant women should be informed of the risks and benefits of undergoing invasive prenatal chromosomal detection.

Sex-dependent difference in the relationship between thyroid hormones and gallstone disease in euthyroid subjects.

The relationship between thyroid dysfunction and gallstone disease (GSD) has been examined by some observational studies. However, evidence about the relationship between thyroid function and GSD among euthyroid subjects was scarce. The aim of this study was to investigate the association between thyroid function and the presence of GSD in a large-sample euthyroid subjects. A total of 5476 euthyroid subjects who underwent health checkup were included. GSD was diagnosed by hepatic ultrasonography. Conventional risk factors for GSD were assessed as well as serum levels of TSH, TT3, TT4 and Log-transformed TT3/TT4 ratio. A total of 4958 subjects were finally included. Levels of TSH, TT3, TT4, and ln (TT3/TT4) were comparable between GSD and non-GSD group (TSH: 1.73 ±â€…1.07 vs 1.74 ±â€…1.07 mIU/L, P = .931; TT3: 1.55 ±â€…0.40 vs 1.54 ±â€…0.39 ng/mL, P = .797; TT4: 9.37 ±â€…2.07 vs 9.49 ±â€…2.06 ug/dL, P = .245, ln (TT3/TT4): -1.80 ±â€…0.23 vs -1.83 ±â€…0.23, P = .130, respectively). Multivariate logistic regression analysis among all subjects revealed that the thyroid function parameters did not reach significant difference. Subgroup analyses showed that the relationship between thyroid function and GSD was different according to gender, with negative association for ln (TT3/TT4) and (odds ratio:0.551, 95% CI: 0.306-0.992, P = .047) and positive association for TT4 (odds ratio:1.077, 1 95% CI: .001-1.158, P = .046) in men. None of the thyroid function parameters was significantly associated with GSD in women. Our findings indicated that low levels of TT3-to-TT4 ratio and high levels of TT4 were significantly and independently associated with GSD among euthyroid male subjects, but not female subjects.

Identification of an energy metabolism-related six-gene signature for distinguishing and forecasting the prognosis of low-grade gliomas.

Background: Low-grade gliomas (LGG) account for 20-25% of all gliomas. In this study, we assessed whether metabolic status was correlated with clinical outcomes in LGG patients using data from The Cancer Genome Atlas (TCGA). Methods: LGG patient data were collected from TCGA, and the Molecular Signature Database was used to extract gene sets related to energy metabolism. After performing a consensus-clustering algorithm, the LGG patients were divided into four clusters. We then compared the tumor prognosis, function, immune cell infiltration, checkpoint proteins, chemo-resistance, and cancer stem cells (CSC) between the two groups with the greatest prognostic difference. Using least absolute shrinkage and selection operator (LASSO) analysis, an energy metabolism-related signature was further developed. Results: Energy metabolism-related signatures were applied to identify four clusters (C1, C2, C3, and C4) using a consensus-clustering algorithm. C1 LGG patients were more related to the synapse and had higher CSC scores, more chemo-resistance, and a better prognosis. C4 LGG was observed to have more immune-related pathways and better immunity. We then identified six energy metabolism-related genes (PYGL, HS3ST3B, NNMT, FMOD, CHST6, and B3GNT7) that can accurately predict LGG prognosis not only as a whole but also based on the independent predictions of each of these six genes. Conclusions: The energy metabolism-related subtypes of LGG were identified, which were strongly related to the immune microenvironment, immune checkpoint proteins, CSCs, chemo-resistance, prognosis, and LGG advancement. A signature of genes involved in energy metabolism could help to distinguish and predict the prognosis of LGG patients, and a promising method to discover patients that may benefit from LGG therapy.

Circ_HIPK3 Inhibits H2O2-Induced Lens Epithelial Cell Injury in Age-Related Cataract Depending on the Regulation of miR-495-3p/HDAC4 Pathway.

Age-related cataract (ARC) is one of the most common chronic diseases. Circular RNA (circ)_HIPK3 is reported to be involved in the advancement of ARC, but its molecular mechanism has not been clarified. Our study provides a new perspective on the clinical treatment of ARC. Our data showed that the expression levels of circ_HIPK3 and histone deacetylase 4 (HDAC4) were downregulated, while microRNA (miR)-495-3p level was increased in ARC tissues and H2O2-induced SRA01/04 cells. Functional experiments showed that circ_HIPK3 and HDAC4 overexpression could inhibit H2O2-induced lens epithelial cell apoptosis and fibrosis. In terms of mechanism, we found that circ_HIPK3 could sponge miR-495-3p, miR-495-3p could target HDAC4. Besides, we confirmed that circ_HIPK3 sponged miR-495-3p to positively regulate HDAC4. Additionally, miR-495-3p overexpression or HDAC4 knockdown reversed the inhibition effect of circ_HIPK3 on H2O2-induced lens epithelial cell injury. In conclusion, our data showed that circ_HIPK3 suppressed H2O2-induced lens epithelial cell injury by regulating miR-495-3p/HDAC4 axis.

Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy.

Background: Hypertensive disorders in pregnancy (HDP) are diseases that coexist with pregnancy and hypertension. The pathogenesis of this disease is complex, and different physiological and pathological states can develop different subtypes of HDP. Objective: To investigate the predictive effects of different variable selection and modeling methods on four HDP subtypes: gestational hypertension, early-onset preeclampsia, late-onset preeclampsia, and chronic hypertension complicated with preeclampsia. Methods: This research was a retrospective study of pregnant women who attended antenatal care and labored at Beijing Maternity Hospital, Beijing Haidian District Maternal and Child Health Hospital, and Peking University People's Hospital. We extracted maternal demographic data and clinical characteristics for risk factor analysis and included gestational week as a parameter in this study. Finally, we developed a dynamic prediction model for HDP subtypes by nonlinear regression, support vector machine, stepwise regression, and Lasso regression methods. Results: The AUCs of the Lasso regression dynamic prediction model for each subtype were 0.910, 0.962, 0.859, and 0.955, respectively. The AUC of the Lasso regression dynamic prediction model was higher than those of the other three prediction models. The accuracy of the Lasso regression dynamic prediction model was above 85%, and the highest was close to 92%. For the four subgroups, the Lasso regression dynamic prediction model had the best comprehensive performance in clinical application. The placental growth factor was tested significant (P < 0.05) only in the stepwise regression dynamic prediction model for early-onset preeclampsia. Conclusion: The Lasso regression dynamic prediction model could accurately predict the risk of four HDP subtypes, which provided the appropriate guidance and basis for targeted prevention of adverse outcomes and improved clinical care.

Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm.

Pre-eclampsia (PE) is a type of hypertensive disorder during pregnancy, which is a serious threat to the life of mother and fetus. It is a placenta-derived disease that results in placental damage and necrosis due to systemic small vessel spasms that cause pathological changes such as ischemia and hypoxia and oxidative stress, which leads to fetal and maternal damage. In this study, four types of risk factors, namely, clinical epidemiology, hemodynamics, basic biochemistry, and biomarkers, were used for the initial selection of model parameters related to PE, and factors that were easily available and clinically recognized as being associated with a higher risk of PE were selected based on hospital medical record data. The model parameters were then further analyzed and screened in two subgroups: early-onset pre-eclampsia (EOPE) and late-onset pre-eclampsia (LOPE). Dynamic gestational week prediction model for PE using decision tree ID3 algorithm in machine learning. Performance of the model was: macro average (precision = 76%, recall = 73%, F1-score = 75%), weighted average (precision = 88%, recall = 89%, F1-score = 89%) and overall accuracy is 86%. In this study, the addition of the dynamic timeline parameter "gestational week" made the model more convenient for clinical application and achieved effective PE subgroup prediction.

Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm.

Background: This study analyzed the influencing factors of fetal growth restriction (FGR), and selected epidemiological and fetal parameters as risk factors for FGR. Objective: To establish a dynamic prediction model of FGR. Methods: This study used two methods, support vector machine (SVM) and multivariate logistic regression, to establish the prediction model of FGR at different gestational weeks. Results: At 20-24 weeks and 25-29 weeks of gestation, the effect of the multivariate Logistic method on model prediction was better. At 30-34 weeks of gestation, the prediction effect of FGR model using the SVM method is better. The ROC curve area was above 85%. Conclusions: The dynamic prediction model of FGR based on SVM and logistic regression is helpful to improve the sensitivity of FGR in pregnant women during prenatal screening. The establishment of prediction models at different gestational ages can effectively predict whether the fetus has FGR, and significantly improve the clinical treatment effect.

Accumulation of Co, Ni, Cu, Zn and Cd in Aboveground Organs of Chinese Winter Jujube from the Yellow River Delta, China.

In the present study, winter jujube organs including fruit, fruiting leaf and foliage leaf, and associated soils in 14 typical orchards in Binzhou City, Shandong Province, China were collected and determined for the mass fractions of Co, Ni, Cu, Zn, and Cd. The mass fractions of Co, Ni, Cu, Zn, and Cd in plant tissues generally showed an order of Cu > Zn > Ni > Co > Cd as well as those in the soils decreased as Zn > Cu > Ni > Co > Cd. The values of single factor index and Nemerow pollution index suggested the jujube fruits were not polluted by heavy metals. Values of estimated daily intake for all the elements were far below their associated acceptable reference values, indicating no health risks would be caused by a single trace element. The results of targeted hazard quotient (THQ) of the metals in the fruits decreased as Cu > Ni > Zn > Cd accompanying total THQ (TTHQ) lower than 1 showing no hazard would be caused by those metals. Correlation analysis showed soil might not be the main source of heavy metals in winter jujube organs. Bioaccumulation factors (BAFs) for Co, Ni, Zn and Cd in fruits and leaves were far below 1 suggesting their low bioavailablities. The relatively great BAFs of Cu in the leaves might be due to the application of fertilizers and pesticides containing great amounts of Cu through soil and foliar spraying. To sum up, heavy metals tended not to be a major threat to winter jujube cultivation, and winter jujube had great edible safety.

The Gut Mycobiome Characterization of Gestational Diabetes Mellitus and Its Association With Dietary Intervention.

Gestational diabetes mellitus (GDM) is a high-risk pregnancy complication that is associated with metabolic disorder phenotypes, such as abnormal blood glucose and obesity. The active interface between gut microbiota and diet contributes to metabolic homeostasis in GDM. However, the contributions of gut mycobiome have been neglected. Here, we profiled the gut fungi between GDM and healthy subjects at two time points and investigate whether variations in gut mycobiome correlate with key features of host metabolism and diet management in this observational study. We identified that Hanseniaspora, Torulaspora, Auricularia, Alternaria, and Candida contributed to GDM patient clustering, indicating that these fungal taxa are associated with abnormal blood glucose levels, and the causality needs to be further explored. While Penicillium, Ganoderma, Fusarium, Chaetomium, and Heterobasidion had significant explanatory effects on healthy subject clustering. In addition, spearman analysis further indicated that blood glucose levels were negatively correlated with polysaccharide-producing genera, Ganoderma, which could be reshaped by the short-term diet. The Penicillium which was negatively correlates with metabolic parameters, also exhibited the antimicrobial attribute by the fungal-bacterial interaction analysis. These data suggest that host metabolic homeostasis in GDM may be influenced by variability in the mycobiome and could be reshaped by the diet intervention. This work reveals the potential significance of the gut mycobiome in health and has implications for the beneficial effects of diet intervention on host metabolic homeostasis through regulating gut fungal abundance and metabolites.

Impacts of Delivery Mode and Maternal Factors on Neonatal Oral Microbiota.

Objectives: Initial oral microbial colonization has complicatedly interacted with growth and development. The aim of our study was to discover links between oral microbiota community structure and mode of delivery, maternal factors, such as systemic diseases, abortion history, and pregnancy complications. Methods: A total of 177 pregnant women and their neonates were enrolled at Peking university people's hospital. We collected oral samples, medical history, and development phenotype and used a 16S rRNA gene sequence to analyze microbial diversity at all taxonomic levels, network structure, and metabolic characteristics. Results: Firmicutes, Proteobacteria, and Actinobacteriota were the most predominant bacteria of neonatal oral samples among these phyla. Alpha-diversity of pregnant women with gestational diabetes mellitus (GDM), abortion history, and without immune diseases was higher than in control groups, and no significant dissimilarity in beta-diversity was observed between different maternal factors. Obvious separation or trend failed to be seen in different development phenotype groups. Besides, Oscillospirales were significantly more abundant in a natural delivery group than in the cesarean section group. Conclusion: Our study indicated that maternal factors and mode of delivery influenced the oral microbial structure, but longitudinal studies were indispensable for capturing the long-term effects on neonatal development phenotype and oral microbiota.

[Comparative Analysis of Thromboelastogram and Coagulation Items in Mongolian Patients with Thrombosis].

OBJECTIVE: To investigate the age distribution of Mongolian patients with cerebral infarction caused by thrombosis and the correlation and consistency between thromboelastography (TEG) and four parameters of coagulation. METHODS: The age distribution of 298 Mongolian patients with cerebral infarction treated in Affiliated Hospital of Inner Mongolia Minzu University from January 2020 to December 2021 and their TEG, four items of routin coagulation and platelet count were analyzed retrospectively. The correlation and consistency of above-mentioned two detection methods were statistically analyzed. RESULTS: The onset age of 298 Mongolian patients with cerebral infarction was mainly 61-70 years old, accounting for 38.3%, followed by 51-60 years old, accounting for 26.8%. The R time detected by TEG was linearly correlated with PT and APTT（r=0.186，r=0.152）. K value, MA value and α-Angle measured by TEG was linearly correlated with Fib (r=-0.364，r=0.616，r=0.359), K value, MA value and α-Angle measured by TEG was linearly correlated with Plt (r=0.318，r=0.519，r=0.301). The R time detected by TEG was consistent with PT and APTT, and the Kappa values were 0.252 (P<0.001), 0.336 (P<0.001). K, MA, and α-Angle measured by TEG was consistent with Fib, the Kappa values were 0.265 (P<0.001), 0.289 (P<0.001) and 0.290 (P<0.001), respectively; K、MA and α-Angle measured by TEG was consistent with Plt, the Kappa values were 0.276 (P<0.001), 0.285 (P<0.001) and 0.302 (P<0.001), respectively. CONCLUSION: The onset age of Mongolian patients with cerebral infarction caused by thrombosis is mainly 61-70 years old, followed by 51-60 years old. The onset age shows a younger trend. There is a significant correlation between TEG and coagulation, but the consistency is weak, therefore, the two methods can not be replaced each other.

Integration of network pharmacology and molecular docking to explore the molecular mechanism of Cordycepin in the treatment of Alzheimer's disease.

Background: Cordycepin is a nucleoside adenosine analog and an active ingredient isolated from the liquid fermentation of Cordyceps. This study sought to explore the mechanism underlying the therapeutic effect of Cordycepin against Alzheimer's disease using network pharmacology and molecular docking technology. Methods: TCMSP, SYMMAP, CTD, Super-pred, SEA, GeneCards, DisGeNET database, and STRING platform were used to screen and construct the target and protein interaction network of Cordycepin for Alzheimer's disease. The results of Gene Ontology annotation and KEGG pathway enrichment analysis were obtained based on the DAVID database. The Omicshare database was also applied in GO and KEGG pathway enrichment analysis of the key targets. The protein-protein interaction network was constructed using the STRING database, and the potential effective targets for AD were screened based on the degree values. The correlation between the potential targets of Cordycepin in the treatment of AD and APP, MAPT, and PSEN2 was analyzed using (GEPIA) databases. We obtained potential targets related to aging using the Aging Altas database. Molecular docking analysis was performed by AutoDock Vina and Pymol software. Finally, we validated the significant therapeutic targets in the Gene Expression Omnibus (GEO) database. Results: A total of 74 potential targets of Cordycepin for treating Alzheimer's disease were identified. The potential targets of Cordycepin for the treatment of AD mainly focused on Lipid and atherosclerosis (hsa05417), Platinum drug resistance (hsa01524), Apoptosis (hsa04210), and Pathways in cancer (hsa05200). Our findings suggest that the therapeutic effect of Cordycepin on AD is primarily associated with these biological processes. We obtained 12 potential therapeutic targets for AD using the degree value in Cytoscape. Interestingly, AKT1, MAPK8, BCL2L1, FOXO3, and CTNNB1 were not only significantly associated with pathogenic genes (APP, MAPT, and PSEN2) but also with longevity in Alzheimer's Disease. Thus we speculated that the five target genes were potential core targets mediating the therapeutic effect of Cordycepin against AD. Moreover, molecular docking results analysis showed good binding affinity between Cordycepin and the five core targets. Overall, MAPK8, FOXO3 and CTNNB1 may have significant clinical and treatment implications. Conclusion: Network pharmacology demonstrated that Cordycepin exerts a therapeutic effect against Alzheimer's disease via multiple targets and signaling pathways and has huge prospects for application in treating neurodegenerative diseases.