Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study.

BACKGROUND: The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection. METHODS: A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model. RESULTS: After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P < 0.05), compared to the pre-COVID-19 group. Moreover, age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, tumor necrosis factor α (TNF-α), and IL-10 were confirmed to be independently associated with severe H1N1 infection in post-COVID-19 era. A prediction model integrating these above eight variables was established, and this model had good discrimination, accuracy, and clinical practicability. CONCLUSIONS: Pediatric H1N1 infection during post-COVID-19 era showed a higher overall disease severity than the classical H1N1 infection in pre-COVID-19 period. Meanwhile, cough and seizures were more prominent in children with H1N1 infection during post-COVID-19 era. Clinicians should be aware of these changes in such patients in clinical work. Furthermore, a simple and practical prediction model was constructed and internally validated here, which showed a good performance for predicting severe H1N1 infection in post-COVID-19 era.

Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma.

PURPOSE: To explore the predictive potential of intratumoral and multiregion peritumoral radiomics features extracted from multiparametric MRI for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients. METHODS: A total of 265 patients with 277 HCCs (training cohort n = 193, validation cohort n = 84) who underwent preoperative MRI were retrospectively analyzed. The risk factors identified through stepwise regression analysis were utilized to construct a clinical model. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (entire tumor, Peri_5mm, Peri_10mm, Peri_20mm) were developed using the LASSO approach. The features obtained from the intratumoral region and the optimal peritumoral region were combined to design the IntraPeri fusion model. Model performance was assessed using the area under the curve (AUC). RESULTS: Larger size, non-smooth margins, and mosaic architecture were risk factors for poorly differentiated HCC (pHCC). The clinical model achieved AUCs of 0.77 and 0.73 in the training and validation cohorts, respectively, while the intratumoral model achieved corresponding AUC values of 0.92 and 0.82. The Peri_10mm model demonstrated superior performance to the Peri_5mm and Peri_20mm models, with AUC values of 0.87 vs. 0.84 vs. 0.73 in the training cohort and 0.80 vs. 0.77 vs. 0.68 in the validation cohort, respectively. The IntraPeri model exhibited remarkable AUC values of 0.95 and 0.86 in predicting pHCC in the training and validation cohorts, respectively. CONCLUSIONS: Our study highlights the potential of a multiparametric MRI-based radiomic model that integrates intratumoral and peritumoral features as a tool for predicting HCC differentiation. CRITICAL RELEVANCE STATEMENT: Both clinical and multiparametric MRI-based radiomic models, particularly the intratumoral radiomic model, are non-invasive tools for predicting HCC differentiation. Importantly, the IntraPeri fusion model exhibited remarkable predictiveness for individualized HCC differentiation. KEY POINTS: • Both the intratumoral radiomics model and clinical features were useful for predicting HCC differentiation. • The Peri_10mm radiomics model demonstrated better diagnostic ability than other peritumoral region-based models. • The IntraPeri radiomics fusion model outperformed the other models for predicting HCC differentiation.

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation.

Purpose: To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model. Methods: A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model. Results: The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively. Conclusion: CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning.

RATIONALE AND OBJECTIVES: To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC). METHODS: CEMRI images were retrospectively obtained from 277 HCCs in 265 patients. Habitat analysis and DL features were extracted from the CEMRI images and selected with the least absolute shrinkage and selection operator approach to develop ITH and DL models, respectively, and these robust features were then integrated to design a fusion model for predicting MVI and poorly differentiated HCC (pHCC). The predictive value of the three models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: The training and validation sets comprised 221 HCCs and 56 HCCs, respectively. The ITH and DL models presented AUC values of (0.90 vs. 0.87) for predicting MVI in the training set, with AUC values of 0.86 and 0.83 in the validation set. The AUC values of the ITH model to predict pHCC were 0.90 and 0.86 in the two sets, respectively; they were 0.84 and 0.80 for the DL model. The fusion model yielded the best performance for predicting MVI and pHCC in the training set (AUC=0.95, 0.90) and in the validation set (AUC=0.89, 0.87), respectively. CONCLUSION: A fusion model integrating ITH and DL features derived from CEMRI images can serve as an excellent imaging biomarker for predicting aggressive characteristics in HCC.

[High-value utilization technology and approach of Chinese medicinal residues under background of "Dual Carbon"].

In recent years, the traditional Chinese medicine(TCM)industry has experienced rapid development, resulting in a significant amount of Chinese medicinal residues generated during the industrial manufacturing process. Currently, the main methods of handling Chinese medicinal residues include stacking, landfilling, and incineration, which lead to substantial resource waste and potential environmental pollution. With "carbon peak" and "carbon neutrality"( "Dual Carbon")becoming national strategic goals, the TCM industry is ushering in a new wave of "low-carbon" trends, and the high-value utilization of Chinese medicinal residues has become a breakthrough for implementing a low-carbon economy in the TCM sector. From the perspective of a low-carbon economy, this article reviewed literature in China and abroad to summarize the microbial transformation technology, enzymatic conversion technology, biomass pyrolysis, gasification, hydrothermal liquefaction, and other high-value utilization technologies for Chinese medicinal residues. It also overviewed the applications of Chinese medicinal residue in feed additives, organic fertilizers, edible mushroom cultivation substrates, preparation of activated carbon for wastewater treatment, and new energy batteries. Considering the current status of resource utilization of Chinese medicinal residues, feasible strategies and suggestions for resource development and utilization were proposed to improve the quality and efficiency of the Chinese medicinal resource industry chain and promote green development, thereby providing research ideas and theoretical basis for achieving carbon peak and carbon neutrality goals.

Evaluation of febrile seizures in children infected with SARS-CoV-2 Omicron variant in Yunnan, China: a multi-center, retrospective observational study.

Background: The SARS-CoV-2 Omicron variant was reported to be linked to febrile seizures (FSs), but studies on FSs in children with Omicron infection remain relatively scarce, especially in the Chinese population. This study aimed to investigate the characteristics of children diagnosed with Omicron infection with FSs in Yunnan, China, and evaluate the potential association between FSs and Omicron infection. Methods: This study was conducted at four hospitals in Yunnan from December 8, 2022, to January 8, 2023, and consisted of 590 pediatric subjects. According to clinical characteristics, 85, 129 and 376 subjects were divided into the FS-only, Omicron-FS, and Omicron-only groups, respectively. Demographic, clinical and laboratory data were retrospectively collected for analysis. Results: The incidence of FSs in children with Omicron infection was 25.5% (129/505). Older age, stronger male predominance, as well as lower proportions of prior history and family history of seizures were observed in Omicron-FS and Omicron-only groups than in FS-only group, but there were no differences in these four above-mentioned events between these two Omicron-related groups. Compared to FS-only group, Omicron-FS group also had a shorter fever-to-seizure onset duration and more frequent seizures during a single course of fever. Moreover, higher levels of IL-6, TNF-α and ferritin as well as decreased counts of leukocytes and lymphocytes were confirmed in Omicron-FS group than in FS-only and Omicron-only groups. Regarding COVID-19 vaccination status, Omicron-FS group revealed a higher proportion of unvaccinated children and a lower proportion of three-dose vaccination than Omicron-only group. As for clinical outcomes, proportions of mechanical ventilation and intensive care unit admission observed in the two Omicron-related groups were notably higher than those in FS-only group. Meanwhile, Omicron-FS group showed the longest length of hospital stay, followed by Omicron-only group and FS-only group, in order. Finally, all patients but one who died of fulminant myocarditis had been successfully discharged. Conclusions: The incidence of FSs in children with Omicron infection was 25.5% in Yunnan. FSs might be a clinical sign deserving more attention in children with Omicron infection. Furthermore, COVID-19 vaccination is likely to provide effective protection against Omicron-related FSs in children.

[Measures for waste and by-product recycling and circular economy of whole industry chain of traditional Chinese medicine resources facing carbon peak and carbon neutrality (dual carbon) goals].

It has become a common consensus that resource conservation and intensive recycling for improving resource utilization efficiency is an important way to achieve carbon peak and carbon neutrality(dual carbon). Traditonal Chinese medicine(TCM)resources as national strategic resources are the material basis and fundamental guarantee for the development of TCM industry and health services. However, the rapid growth of China's TCM industry and the continuous expansion and extension of the industrial chain have exposed the low efficiency of TCM resources. Resource waste and environmental pollution caused by the treatment and discharge of TCM waste have emerged as major problems faced by the development of the industry, which has aroused wide concern. Considering the dual carbon goals, this paper expounds the role and potential of TCM resource recycling and circular economy industry development. Taking the typical model of TCM resource recycling as the case of circular economy industry in reducing carbon source and increasing carbon sink, this paper puts forward the suggestions for the TCM circular economy industry serving the double carbon goals. The suggestions mainly include strengthening the policy and strategic leading role of the double carbon goals, building an objective evaluation system of low-carbon emission reduction in the whole industrial chain of TCM resources, building an industrial demonstration park for the recycling of TCM resources, and promoting the establishment of a circular economy system of the whole industrial chain of TCM resources. These measures are expected to guide the green transformation of TCM resource industry from linear economic model to circular economy model, provide support for improving the utilization efficiency and sustainable development of TCM resources, and facilitate the low-carbon and efficient development of TCM resource industry and the achievement of the double carbon goals.

Doubly Screened Coulomb Correction Approach for Strongly Correlated Systems.

Strongly correlated systems containing d/f electrons present a challenge to conventional density functional theory such as the local density approximation or generalized gradient approximation. We developed a doubly screened Coulomb correction (DSCC) approach to perform on-site Coulomb interaction correction for strongly correlated materials. The on-site Coulomb interaction between localized d/f electrons is self-consistently determined from a model dielectric function that includes both the static dielectric and Thomas-Fermi screening. We applied DSCC to simulate the electronic and magnetic properties of typical 3d, 4f, and 5f strongly correlated systems. The accuracy of DSCC is comparable to that of hybrid functionals but an order of magnitude faster. In addition, DSCC can reflect the difference in the Coulomb interaction between metallic and insulating situations, similar to the popular but computationally expensive constrained random phase approximation approach. This feature suggests that DSCC is also a promising method for simulating Coulomb interaction parameters.

[Progress on the impact of adjacent vertebral arch after single open door laminoplasty].

Single-door laminoplasty has been widely used in the treatment of multisegment cervical myelopathy, with the clinical advantages of decompression of the spinal cord, relieving preoperative neurological symptoms or signs, and maintaining cervical mobility. However, in clinical work, patients with limited cervical spine activity after single open door laminoplasty are often encountered, and the direct contact with the adjacent vertebral arch can be observed in the postoperative X-ray of the anterior and lateral cervical spine, which is called the adjacent vertebral arch bone impact, which is one of the important causes of the limited cervical spine movement. In recent years, there have been many reports on the prevention of bone impact, although the short-term clinical effect is significant, but long-term clinical efficacy to be further study, and the cause and the pathogenesis of bone impact is no consensus, this paper on the surgery of adjacent vertebral arch impact epidemiology, biomechanics, clinical performance, surgical effect and improvement.

[Recent research on the application of defoamers in children undergoing digestive endoscopy]. / ç¥›æ³¡å‰‚åœ¨å„¿ç«¥æ¶ˆåŒ–å† é•œä¸­çš„åº”ç”¨è¿›å±•.

Endoscopy is a common tool for the diagnosis and treatment of gastrointestinal disorders in children. The presence of bubbles in the gastrointestinal tract is one of the important factors affecting the clarity of endoscopic visual field, and the application of defoamers can significantly reduce bubbles in the gastrointestinal tract, improve the quality of gastrointestinal preparation, and further increase disease detection rate. Various studies have been conducted on gastrointestinal preparation before endoscopy in children, but there still lacks a uniform protocol for the application of defoamers. This article summarizes the use of defoamers in children before digestive endoscopy and related research advances and points out that existing studies on defoamers have a small sample size and that there are still controversies over the selection and timing of administration, so as to provide a reference for in-depth research on defoamers in the future.

Comparative study of first-principles approaches for effective Coulomb interaction strength Ueff between localized f-electrons: Lanthanide metals as an example.

As correlation strength has a key influence on the simulation of strongly correlated materials, many approaches have been proposed to obtain the parameter using first-principles calculations. However, a comparison of the different Coulomb strengths obtained using these approaches and an investigation of the mechanisms behind them are still needed. Taking lanthanide metals as an example, we research the factors that affect the effective Coulomb interaction strength, Ueff, by local screened Coulomb correction (LSCC), linear response (LR), and constrained random-phase approximation (cRPA) in the Vienna Ab initio Simulation Package. The Ueff LSCC value increases from 4.75 to 7.78 eV, Ueff LR is almost stable at about 6.0 eV (except for Eu, Er, and Yb), and Ueff cRPA shows a two-stage decreasing trend in both light and heavy lanthanides. To investigate these differences, we establish a scheme to analyze the coexistence and competition between the orbital localization and the screening effect. We find that LSCC and cRPA are dominated by the orbital localization and the screening effect, respectively, whereas LR shows the balance of the competition between the two factors. Additionally, the performance of these approaches is influenced by different starting points from the Perdew-Burke-Ernzerhof (PBE) and PBE + U, especially for cRPA. Our results provide useful knowledge for understanding the Ueff of lanthanide materials, and similar analyses can also be used in the research of other correlation strength simulation approaches.

Virulence gene detection and antimicrobial resistance analysis of Enterococcus faecium in captive giant pandas (Ailuropoda melanoleuca) in China.

BACKGROUND: The emergence of multidrug resistance among enterococci makes effective treatment of enterococcal infections more challenging. Giant pandas (Ailuropoda melanoleuca) are vulnerable to oral trauma and lesions as they feast on bamboo. Enterococci may contaminate such oral lesions and cause infection necessitating treatment with antibiotics. However, few studies have focused on the virulence and drug resistance of oral-derived enterococci, including Enterococcus faecium, in giant pandas. In this study, we analyzed the prevalence of 8 virulence genes and 14 drug resistance genes in E. faecium isolates isolated from saliva samples of giant pandas held in captivity in China and examined the antimicrobial drug susceptibility patterns of the E. faecium isolates. RESULTS: Twenty-eight isolates of E. faecium were successfully isolated from the saliva samples. Four virulence genes were detected, with the acm gene showing the highest prevalence (89%). The cylA, cpd, esp, and hyl genes were not detected. The isolated E. faecium isolates possessed strong resistance to a variety of drugs; however, they were sensitive to high concentrations of aminoglycosides. The resistance rates to vancomycin, linezolid, and nitrofurantoin were higher than those previously revealed by similar studies in China and other countries. CONCLUSIONS: The findings of the present study indicate the drugs of choice for treatment of oral E. faecium infection in the giant panda.

Effect of Iron Deposition on Native T1 Mapping and Blood Oxygen Level Dependent for the Assessment of Liver Fibrosis in Rabbits With Carbon Tetrachloride Intoxication.

RATIONALE AND OBJECTIVES: This study aimed to explore the effect of iron deposition on native T1 mapping and blood oxygen level-dependent (BOLD) imaging in detecting liver fibrosis (LF) in a rabbit model. MATERIALS AND METHODS: An LF group (n = 100) was established by subcutaneously injecting 50% carbon tetrachloride (CCl4) oil solution, and 20 normal rabbits composed a control group. Native T1 mapping and BOLD were performed, and the T1native and R2* quantitative parameters were analyzed by receiver operating characteristic (ROC) and multiple logistic regression analyses, with histopathological results and liver iron content (LIC) serving as reference standards. RESULTS: In total, 18, 17, 16, 18, and 15 rabbits were histopathologically diagnosed with LF stages F0, F1, F2, F3, and F4, respectively. T1native (r = 0.47), R2* (r = 0.75) and LIC (r = 0.61) increased with LF stage progression (p < 0.001). Compared to T1native values, R2* performed better in diagnosing the LF stage, especially for distinguishing F1-F2 from F3-F4 (AUC = 0.66 vs. 0.91, p = 0.01). Combined with the LIC, both T1native and R2* showed improved diagnostic value in comparison to the individual imaging techniques, particularly for diagnosing F0 vs. F1-F2 and F0 vs. F1-F4, with AUC values of 0.90 vs. 0.70 (p = 0.01) and 0.93 vs. 0.77 (p = 0.01) for T1native + LIC vs. LIC, respectively. CONCLUSION: BOLD imaging performed better than native T1 mapping in predicting and diagnosing LF stage progression. The decrease in diagnostic accuracy caused by the deposition of liver iron is a potential pitfall in the assessment of LF with BOLD imaging and native T1 mapping.

Preoperative Nomogram Incorporating Clinical Factors, Serological Markers and LI-RADS MRI Features to Predict Early Recurrence of Hepatocellular Carcinoma Treated with Transarterial Chemoembolization.

PURPOSE: To develop and validate a preoperative nomogram model that incorporates clinical factors, serological markers and liver imaging reporting and data system (LI-RADS v2018) MRI features for predicting early recurrence (ER) of hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). METHODS: One hundred and fourteen patients with HCC who underwent MRI scanning before TACE were enrolled retrospectively and divided into a training cohort (n=80) and a test cohort (n=34). The clinical factors, serological markers and LI-RADS v2018 MRI features associated with ER were determined by univariable and multivariable analyses. A nomogram model predicting ER after TACE was developed, and its discriminatory ability, goodness-of-fit and clinical application were evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), respectively. RESULTS: In total, 74 (64.9%) patients were diagnosed with ER according to the follow-up results. Increased alpha fetoprotein (AFP) levels, larger tumor size, nonsmooth margin, mosaic architecture satellite nodules and corona enhancement were independent predictors associated with ER (p < 0.05). For the established nomogram model that incorporated these six significant predictors, the AUC values were 0.94 (95% CI: 0.89-0.99) and 0.95 (95% CI: 0.88-1.00) for predicting ER after TACE in the training and test cohorts, respectively. The calibration curve and DCA results demonstrate the good goodness-of-fit and clinical benefits of this nomogram. CONCLUSION: A preoperative nomogram model based on serological markers and LI-RADS v2018 MRI features could adequately predict ER in HCC patients after TACE, which may provide personalized guidance for predicting prognosis.

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria.

PURPOSE: To establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria. METHODS: In total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves. RESULTS: The risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78-0.92) and 0.85 (95% CI: 0.74-0.95), and those for the Rad-score were 0.82 (95% CI: 0.73-0.90) and 0.80 (95% CI: 0.67-0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81-0.94) in the training group and 0.89 (95% CI: 0.81-0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits. CONCLUSIONS: The nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.

Identification of potential hub genes of gastric cancer.

BACKGROUND: Gastric cancer (GC) is a malignant tumor originated from gastric mucosa epithelium. It is the third leading cause of cancer mortality in China. The early symptoms are not obvious. When it is discovered, it has developed to the advanced stage, and the prognosis is poor. In order to screen for potential genes for GC development, this study obtained GSE118916 and GSE109476 from the gene expression omnibus (GEO) database for bioinformatics analysis. METHODS: First, GEO2R was used to identify differentially expressed genes (DEG) and the functional annotation of DEGs was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Search Tool for the Retrieval of Interacting Genes (STRING) tool was used to construct protein-protein interaction (PPI) network and the most important modules and hub genes were mined. Real time quantitative polymerase chain reaction assay was performed to verify the expression level of hub genes. RESULTS: A total of 139 DEGs were identified. The functional changes of DEGs are mainly concentrated in the cytoskeleton, extracellular matrix and collagen synthesis. Eleven genes were identified as core genes. Bioinformatics analysis shows that the core genes are mainly enriched in many processes related to cell adhesion and collagen. CONCLUSION: In summary, the DEGs and hub genes found in this study may be potential diagnostic and therapeutic targets.

A novel de novo heterozygous variant of the KCNQ2 gene: Contribution to early­onset epileptic encephalopathy in a female infant.

Early­onset epileptic encephalopathy (EOEE) represents one of the most severe epilepsies, characterized by recurrent seizures during early infancy, electroencephalogram (EEG) abnormalities and varying degrees of neurodevelopmental delay. The KCNQ2 gene has been reported to have a major role in EOEE. In the present study, a 3­month­old female infant from the Chinese Lisu minority with EOEE was analyzed. Detailed clinical evaluations and next­generation sequencing were performed to investigate the clinical and genetic characteristics of this patient, respectively. Furthermore, the three­dimensional structure of the mutant protein was predicted by SWISS­Model and the expression of KCNQ2 protein in the patient was assessed by flow cytometry. It was observed that the patient presented with typical clinical features of EOEE, including repeated non­febrile seizures and significant EEG abnormalities. A novel heterozygous missense variant c.431G>C (p.R144P) in KCNQ2 was identified in the patient and the genotyping of KCNQ2 in the patient's parents suggested that this variant was de novo. Subsequently, the breakage of hydrogen bonds between certain amino acids was predicted by structural analysis of the mutant protein. Flow cytometric analysis detected a significant reduction buts not complete loss of native KCNQ2 protein expression in the patient (25.1%). In conclusion, a novel variant in KCNQ2 was confirmed as the genetic cause for EOEE in this patient. The present study expanded the pathogenic mutation spectrum of KCNQ2, enhanced the understanding of the molecular pathogenesis of EOEE and provided novel clues for research on the genotype­phenotype correlation in this disease.

Imaging Features Derived From Dynamic Contrast-Enhanced Magnetic Resonance Imaging to Differentiate Malignant From Benign Breast Lesions: A Systematic Review and Meta-Analysis.

MATERIALS AND METHODS: The PubMed, Web of Science, Embase, and the Cochrane Library databases were searched up to January 2021 to identify original studies that investigated the accuracy of individual DCE-MRI features in differentiating malignant from benign breast lesions. Pooled sensitivity, specificity, and area under the curve were calculated by STATA software based on the data extracted from included studies. Moreover, quality assessment, subgroup analysis, and publication bias evaluation were performed. RESULTS: Twenty-nine studies comprising 2976 patients and 3365 suspicious breast lesions were included. Malignant breast lesions tended to present irregular shapes (83.59%), noncircumscribed margins (85.50%), mass enhancement (52.31%), heterogeneous internal enhancement (71.72%), and type II or III time intensity curve (TIC) patterns (91.17%), showing significant differences compared with benign breast lesions (P < 0.05). For differentiating malignant from benign breast lesions, the area under the curve values of irregular shape, noncircumscribed margin, mass enhancement, heterogeneous internal enhancement, and type II or III TIC patterns were 0.79 (0.76-0.83), 0.87 (0.84-0.90), 0.63 (0.58-0.67), 0.82 (0.78-0.85), and 0.89 (0.86-0.92), respectively. CONCLUSIONS: Imaging features derived from DCE-MRI, especially TIC patterns, are important for diagnosing and differentiating malignant from benign breast lesions.

Hemolymphangioma with multiple hemangiomas in liver of elderly woman with history of gynecological malignancy: A case report.

BACKGROUND: Hepatic hemolymphangioma is an extremely rare benign congenital malformation composed of cystically dilated lymphatic and blood vessels, and they have nonspecific clinical symptoms and laboratory results. In this study, hepatic hemolymphangioma with multiple hemangiomas in an elderly woman was initially reported and analyzed. CASE SUMMARY: A 61-year-old female patient, with a history of hysterectomy and bilateral adnexectomy, was referred to the hepatobiliary surgery department with the complaint of multiple hepatic hemangiomas that had been diagnosed 2 years prior in a preoperative contrast-enhanced computed tomography (CECT) examination. Upon entering our hospital, no abnormal physical examination and laboratory data were found. The latest CECT revealed a new 7.0 cm × 6.2 cm cystic-solid lesion with multiple internal divisions in segment II of the liver, with delayed CECT enhancement characteristics that presented as solid parts with internal division. On the positron emission tomography (PET)/CT, no significant uptake of 18F-fluorodeoxyglucse was observed. Finally, hepatic hemolymphangioma was confirmed based on the pathological and immunohistochemical results after surgery. At 1-year follow-up, her posthepatectomy evaluation was uneventful, and she had recovered full activity. In addition, no postoperative recurrent or residual lesion was found on CECT imaging. CONCLUSION: Hepatic hemolymphangioma with multiple hemangiomas was reported and observed by CECT and PET/CT imaging.