Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations. Additionally, effectively utilizing network structure to fit the interaction properties of regulatory networks and combining specific case biomarker validations remains an unresolved issue in cancer biomarker prediction methods. To overcome these limitations, we propose a multi-view learning framework, CeRVE, based on directed graph neural networks (DGNN) for predicting unknown type cancer biomarkers. CeRVE effectively extracts and integrates subgraph information through multi-view feature learning. Subsequently, CeRVE utilizes DGNN to simulate the entire regulatory network, propagating node attribute features and extracting various interaction relationships between molecules. Furthermore, CeRVE constructed a comparative analysis matrix of three cancers and adjacent normal tissues through The Cancer Genome Atlas and identified multiple types of potential cancer biomarkers through differential expression analysis of mRNA, microRNA, and long noncoding RNA. Computational testing of multiple types of biomarkers for 72 cancers demonstrates that CeRVE exhibits superior performance in cancer biomarker prediction, providing a powerful tool and insightful approach for AI-assisted disease biomarker discovery.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.

Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

A Novel Liver Metastasis Score for Patients Undergoing Surgical Resection of Gastroenteropancreatic Neuroendocrine Tumors: A Multi-institutional Study.

BACKGROUND: Liver metastasis impacts survival in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs); however, current guidelines lack consensus on post-resection surveillance and adjuvant therapy. A comprehensive risk stratification tool is needed to guide personalized management. OBJECTIVE: We aimed to develop and validate a predictive model for liver metastasis risk after surgical resection of GEP-NETs that incorporates pathological factors and adjuvant therapy. METHODS: Patients with GEP-NETs who underwent surgical resection with curative intent at three major Chinese hospitals (2010-2022) were identified. Univariable and multivariable Cox regression analysis identified independent risk factors of liver metastasis. The liver metastasis score (LMS) was developed using weighted risk factors and validated by tenfold cross-validation. RESULTS: Among the 724 patients included in the analytic cohort, liver metastasis occurred in 66 patients (9.1%) at a median of 36 months; patients with liver metastasis had a worse 5-year overall survival (no liver metastasis 63.6% vs. liver metastasis 95.8%; p < 0.001). Independent predictors were Ki-67 index (hazard ratio [HR] 10.36 for Ki-67 3-20%, HR 18.30 for Ki-67 >20%, vs. <3%), vascular invasion (HR 5.03), lymph node metastases (HR 2.24), and lack of adjuvant therapy (HR 3.03). The LMS demonstrated excellent discrimination (C-index 0.888) and stratified patients into low, intermediate, and high-risk relative to 5-year risk of liver metastasis: 2.9%, 20.8%, and 49.7%, respectively (p < 0.001). CONCLUSIONS: The novel LMS effectively predicted the risk of liver metastasis after surgical resection of GEP-NETs. This validated model can help guide personalized surveillance and adjuvant treatment strategies, potentially improving outcomes for high-risk patients.

Real-world data on neoadjuvant chemotherapy with dual-anti HER2 therapy in HER2 positive breast cancer.

BACKGROUND: Neoadjuvant chemotherapy with dual-targeted therapy is the standard treatment for human epidermal growth factor 2 (HER2)-positive breast cancer. Although the dual-targeted therapy has significantly improved the pathological complete response (pCR) rate, further investigation is needed to identify biomarkers that predict the response to neoadjuvant therapy. METHODS: This retrospective study analyzed 353 patients with HER2-positive breast invasive ductal carcinoma. The correlation between clinicopathological factors and pCR rate was evaluated. A nomogram was constructed based on the results of the multivariate logistic regression analysis to predict the probability of pCR. RESULTS: The breast pCR (b-pCR) rate was 56.1% (198/353) and the total pCR (t-pCR) rate was 52.7% (186/353). Multivariate analysis identified ER status, PR status, HER2 status, Ki-67 index, and neoadjuvant chemotherapy regimens as independent indicators for both b-pCR and t-pCR. The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.73 (95% CI: 0.68-0.78). According to the nomogram, the t- pCR rate was highest in the ER-PR- HER2-positive patients (131/208) and lowest in the ER + PR + HER2-positive patients (19/73). The subgroup analyses showed that there was no significant difference in pCR rate among the neoadjuvant chemotherapy regimens in ER positive, PR positive, HER2 IHC 2 + , Ki67 index < 30% population. However, for ER-PR-HER2-positive patients, the neoadjuvant chemotherapy regimen has a great influence on the pCR rates. CONCLUSIONS: Patients with ER-negative, PR-negative, HER2 3 + and high KI-67 index were more likely to achieve pCR. THP may be used as an alternative to AC-THP or TCbHP in selected HER2-positive patients.

RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.

Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.

Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach.

Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity, accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto have received less attention. Conclusions of previous studies in this domain are moreover limited by suboptimal replicability of resting-state functional magnetic resonance imaging (fMRI) and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both the LEMON (Western European) and SALD (Chinese) cohorts, we consistently identify two distinct states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). Moreover, in both these cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Older participants also tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally specific networks.

Complete Genome Sequence of Candida mucifera from an Otitis Media Patient.

We describe for the first time, a high-quality genome for a rare human yeast pathogen Candida mucifera, from a patient with chronic suppurative otitis media. This pathogen exhibited reduced azole susceptibility, similar to its close relatives within the Trichomonascus ciferrii species complex.

Genome-wide analysis of in vivo-evolved Candida auris reveals multidrug-resistance mechanisms.

Candida auris, an emerging and multidrug-resistant fungal pathogen, has led to numerous outbreaks in China. While the resistance mechanisms against azole and amphotericin B have been studied, the development of drug resistance in this pathogen remains poorly understood, particularly in in vivo-generated drug-resistant strains. This study employed pathogen whole-genome sequencing to investigate the epidemiology and drug-resistance mutations of C. auris using 16 strains isolated from two patients. Identification was conducted through Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and antimicrobial susceptibilities were assessed using broth microdilution and Sensititre YeastOne YO10. Whole-genome sequencing revealed that all isolates belonged to the South Asian lineage, displaying genetic heterogeneity. Despite low genetic variability among patient isolates, notable mutations were identified, including Y132F in ERG11 and A585S in TAC1b, likely linked to increased fluconazole resistance. Strains from patient B also carried F214L in TAC1b, resulting in a consistent voriconazole minimum inhibitory concentration of 4 µg/mL across all isolates. Furthermore, a novel frameshift mutation in the SNG1 gene was observed in amphotericin B-resistant isolates compared to susceptible ones. Our findings suggest the potential transmission of C. auris and emphasize the need to explore variations related to antifungal resistance. This involves analyzing genomic mutations and karyotypes, especially in vivo, to compare sensitive and resistant strains. Further monitoring and validation efforts are crucial for a comprehensive understanding of the mechanisms of drug resistance in C. auris.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia.

BACKGROUND: Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. METHODS: We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). RESULTS: We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). CONCLUSIONS: The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Transdural circumferential decompression for thoracic spinal stenosis caused by beak-type ossification of the posterior longitudinal ligament: a technical note.

PURPOSE: Thoracic myelopathy caused by ossification of the posterior longitudinal ligament (OPLL) in the thoracic spine is usually progressive and responds poorly to conservative therapy, making surgery the only effective treatment option. A variety of surgical procedures have been developed to treat thoracic OPLL. However, the optimal surgical approach for removal of thoracic OPLL remains unclear. In the present study, we described a newly modified posterior approach for the removal of OPLL: circular decompression via dural approach, and complete removal of OPLL can be achieved under direct vision and without neurological deficit. MATERIALS AND METHODS: Three patients with beak-type thoracic OPLL presented with progressive thoracic myelopathy and leg weakness. Magnetic resonance imaging showed the spinal cord severely compressed. The surgical management of the three patients involved the 'cave-in' circular decompression and transdural resection of OPLL. RESULTS: Transdural circumferential decompression was successfully performed in all three patients. Clinical outcome measures, including pre- and postoperative radiographic parameters, were assessed. All of the patients were followed up for an average of 12 months (ranging from 10 to 15 months), and no surgery-related complications occurred. Weakness relief and neural function recovery were satisfactorily achieved in all patients by the final follow-up. CONCLUSIONS: Transdural circumferential decompression was an effective method for thoracic spinal stenosis caused by concurrent beak-type OPLL, by which OPLL could be safely removed. It is especially useful when there is a severe adhesion between the dura OPLL.

Associations between 24-h movement behaviours and BMI in Chinese primary- and middle- school students.

Purposes: This cross-sectional study aimed to examine the associations between the 24-h movement behaviours and body mass index (BMI) of students from China by using compositional data analysis. Methods: A total of 389 students aged 6-16 years participated in this study. Accelerometers were used to measure moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LPA), sedentary behaviour (SED), and sleep. Weight and height were objectively measured. The association between 24-h movement and BMI was analyzed by using compositional data analysis. Results: Time reallocation using minutes and proportions created major differences to the results. Reallocating 10 min from other movement behaviours to MVPA was associated with decreased BMI z-score of 1.372 to 0.158 among primary-school students. Reallocating 10 min from sleep and SED to MVPA, and from sleep and SED to LPA were associated with decreased BMI z-score of 0.505 to 0.017 among middle-school students. Reallocating 10% of time from all other components to SED and sleep were associated with a higher BMI z-score by 0.148 (95%CI: 0.020; 0.276) and 0.125 (95%CI: 0.046; 0.204), while reallocating time to MVPA was associated with a decrease in BMI z-scores of 0.132 (95%CI: -0.193; -0.070) among primary-school students. Reallocating 10% of time from all other components to SED was associated with a higher BMI z-score of 0.254 (95%CI: 0.165; 0.345), whereas reallocating time to MVPA and LPA was associated with a decrease in BMI z-scores of 0.039 (95%CI: -0.073; -0.005) and 0.093 (95%CI: -0.153; -0.033) among middle-school students. Conclusion: Research results of 10-min one-to-one reallocation may be treated cautiously due to uneven distribution of time in 24-h movement behaviours. Based on the results of 10% one-to-remaining reallocation, replacing SED with MVPA may be an appropriate target for adiposity intervention in primary-school students, while increasing MVPA or LPA at the expense of SED may be effective in controlling adiposity of middle-school students in China.

Association of Preoperative Body Mass Index with Surgical Textbook Outcomes Following Hepatectomy for Hepatocellular Carcinoma: A Multicenter Study of 1206 Patients.

BACKGROUND: Assessment of quality in the perioperative period is critical to ensure good patient care. Textbook outcomes (TO) have been proposed to combine several parameters into a single defined quality metric. The association of preoperative body mass index (BMI) with incidences of achieving or not achieving TO (non-TO) among patients undergoing hepatectomy for hepatocellular carcinoma (HCC) was characterized. METHODS: Patients who underwent curative-intent hepatectomy for HCC between 2015 and 2018 were identified from a multicenter database. These patients were divided into three groups based on preoperative BMI: low-BMI (≤ 18.4 kg/m2), normal-BMI (18.5-24.9 kg/m2), and high-BMI (≥ 25.0 kg/m2). The incidences of non-TO among these three groups were compared. Multivariate analyses were performed to identify whether there was any independent association between preoperative BMI and non-TO. RESULTS: Among 1206 patients, 100 (8.3%), 660 (54.7%), and 446 (37.0%) were in the low-BMI, normal-BMI, and high-BMI groups, respectively. The incidence of non-TO was 65.6% in the whole cohort. The incidence of non-TO was significantly higher among patients in the low- and high-BMI cohorts versus the normal-BMI cohort (75.0% and 74.7% versus 58.0%, both P < 0.01). After adjustment of other confounding factors on multivariate analysis, low-BMI and high-BMI were independently associated with higher incidences of non-TO compared with normal-BMI (OR: 1.98 and 2.27, both P < 0.05). CONCLUSIONS: Two out of three patients did not achieve TO after hepatectomy for HCC. Both preoperative low-BMI and high-BMI were independently associated with lower odds to achieve optimal TO following HCC resection.

Clinical Features of Recurrence After Hepatic Resection for Early-Stage Hepatocellular Carcinoma and Long-Term Survival Outcomes of Patients with Recurrence: A Multi-institutional Analysis.

BACKGROUND: A potentially curative hepatic resection is the optimal treatment for hepatocellular carcinoma (HCC), but most HCCs, even at an early stage, eventually recur after resection. This study investigates clinical features of initial recurrence and long-term prognosis of patients with recurrence after curative resection for early-stage HCC. PATIENTS AND METHODS: From a multicenter database, patients who underwent curative hepatic resection for early-stage HCC [Barcelona Clinic Liver Cancer (BCLC) stage 0/A] were extracted. Time to initial recurrence, patterns of initial recurrence, and treatment modalities for recurrent tumors were investigated. Univariate and multivariate analysis were used to identify independent risks associated with postoperative recurrence, as well as post-recurrence survival (PRS) for patients with recurrence. RESULTS: Among 1424 patients, 679 (47.7%) developed recurrence at a median follow-up of 54.8 months, including 408 (60.1%) early recurrence (≤ 2 years after surgery) and 271 (39.9%) late recurrence (> 2 years). Independent risks of postoperative recurrence included cirrhosis, preoperative alpha-fetoprotein level > 400 ug/L, tumor size > 5 cm, multiple tumors, satellites, microvascular invasion, and intraoperative blood transfusion. Multivariate analysis revealed that receiving irregular recurrence surveillance, initial tumor beyond Milan criteria, early recurrence, BCLC stage B/C of the recurrent tumor, and noncurative treatments were independently associated with poorer PRS. CONCLUSIONS: Nearly half of patients with early-stage HCC experienced recurrence after resection. Understanding recurrence risks may help identify patients at high risk of recurrence who may benefit from future adjuvant therapies. Meaningful survival even after recurrence can still be achieved by postoperative regular surveillance and curative treatment.

Oxytocin Modulates the Intrinsic Dynamics Between Attention-Related Large-Scale Networks.

Attention and salience processing have been linked to the intrinsic between- and within-network dynamics of large-scale networks engaged in internal (default network [DN]) and external attention allocation (dorsal attention network [DAN] and salience network [SN]). The central oxytocin (OXT) system appears ideally organized to modulate widely distributed neural systems and to regulate the switch between internal attention and salient stimuli in the environment. The current randomized placebo (PLC)-controlled between-subject pharmacological resting-state fMRI study in N = 187 (OXT, n = 94; PLC, n = 93; single-dose intranasal administration) healthy male and female participants employed an independent component analysis approach to determine the modulatory effects of OXT on the within- and between-network dynamics of the DAN-SN-DN triple network system. OXT increased the functional integration between subsystems within SN and DN and increased functional segregation of the DN with both attentional control networks (SN and DAN). Whereas no sex differences were observed, OXT effects on the DN-SN interaction were modulated by autistic traits. Together, the findings suggest that OXT may facilitate efficient attention allocation by modulating the intrinsic functional dynamics between DN components and large-scale networks involved in external attentional demands (SN and DAN).

Associations between sleep duration, sleep quality, and weight status in Chinese children and adolescents.

BACKGROUND AND OBJECTIVE: The high prevalence of obesity is a serious problem, and sleep is considered to be a factor for obesity. This study aimed to examine the relationship between sleep duration, sleep quality, and weight status among children and adolescents in China and to explore whether the relationship between sleep duration and weight status is independent of sleep quality. Sex and age differences were also explored. METHODS: A cross-sectional research was conducted among 2019 children and adolescents (1029 boys and 990 girls) aged 8-16 years in Shanghai. An open-question was used to obtain data on sleep duration, which was categorized into the following four groups based on the age-specific National Sleep Foundation Sleep Duration Recommendations: 1) very short, 2) short, 3) recommended, and 4) long. The Pittsburgh Sleep Quality Index was used to assess sleep quality. Weight and height were measured for all participants. The variable weight status was estimated with the Chinese children and adolescent age- and sex-specific body mass index (BMI) and was categorized into overweight/obesity and normal weight. RESULTS: Short sleep duration (7-8 and 6-7 h for 6-13 and 14-16 years old, respectively) significantly increased odds of overweight/obesity (OR = 1.32, 95% CI: 1.06-1.64) compared with that of the recommended sleep duration (9-11 and 8-10 h for 6-13 and 14-16 years old, respectively). The relationship between the two variables existed independent of sleep quality. No significant relationship was found between sleep quality and overweight/obesity of children and adolescents. Sleep quality (OR = 1.07, 95% CI: 1.01-1.14) and short sleep duration (OR = 1.51, 95% CI: 1.06-2.13) increased the risk for overweight/obesity among girls, whereas no significant relationships between sleep duration, sleep quality, and overweight/obesity were found among boys. Short sleep duration increased the risk of overweight/obesity in children aged 8-13 years (OR = 1.34, 95% CI: 1.05-1.71), independent of sleep quality, but no significant relationships between these two variables existed for adolescents aged 14-16 years. CONCLUSIONS: Overall, short sleep duration increased the risks of overweight/obesity in children and adolescents in China, independent of sleep quality. This relationship is significant for girls and children aged 8-13 years instead of boys and adolescents aged 14-16 years. Interventions to extend the sleep duration of children and adolescents, especially girls and children aged 8-13 years in China, are necessary to improve their weight status.

[Correlations between resting-state brain functional characteristics and TCM syndrome in premature ejaculation patients with heart-kidney disharmony].

OBJECTIVE: To investigate the correlation between the characteristics of resting-state brain function and the types of traditional Chinese medicine (TCM) syndrome in premature ejaculation (PE) patients with heart-kidney disharmony, and the pathogenesis of abnormal ejaculation of the patients. METHODS: We enrolled 33 PE patients with heart-kidney disharmony and 32 healthy controls matched in general demographic data, evaluated the severity of the main and concurrent symptoms of PE using the PE Diagnostic Tool (PEDT) and TCM Syndrome Scale (TCMSS), and obtained the brain structural and functional MRI data. We processed the collected data and calculated the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF) and regional homogeneity (Reho) of the brain with the DPABI software. Using the REST software package, we compared the significantly different brain areas between the PE and control groups by two-sample t-test and corrected the results for multiple comparisons by AlphaSim, followed by Pearson correlation analysis of ALFF, fALFF and Reho in abnormal brain areas and the PEDT and CMSS scores of the patients. RESULTS: The PE patients showed decreased ALFF values in the bilateral inferior temporal gyrus, left middle frontal gyrus and left orbital part of the inferior frontal gyrus, and increased ALFF values in the bilateral hippocampus, thalamus and precuneus, right inferior occipital gyrus, right calcarine and left inferior parietal, with positive correlations of the ALFF values of the left thalamus with the scores on PEDT (r = 0.35, P < 0.05) and TCMSS (r = 0.44, P < 0.05). The fALFF values of the patients were also decreased in the left temporal pole of the middle temporal gyrus and left anterior cingulate gyrus, but increased in the left inferior temporal gyrus. The Reho values of the patients were decreased as well in the right inferior temporal gyrus, left middle frontal gyrus, right dorsolateral superior frontal gyrus, right middle occipital gyrus, right middle occipital gyrus and right precuneus, but increased in the left temporal pole of the middle temporal gyrus, left middle frontal gyrus and left superior frontal gyrus, with negative correlations between the Reho value of the right superior parietal gyrus and TCMSS scores (r = －0.35, P < 0.05). CONCLUSION: Abnormal brain regions were found in PE patients with heart-kidney disharmony, with might be the pathologically associated with PE symptoms and heart-kidney disharmony of the patients.

[Effects of Extracellular Vesicles on the Drug Resistance of Lung Adenocarcinoma Cells by Modulating the ATP Binding Cassette Transporter G2].

Objective: To investigate the regulatory role of extracellular vesicles (EVs) carrying ATP binding cassette transporter G2 (ABCG2) on the drug resistance of lung adenocarcinoma cells and the relevant molecular mechanisms. Methods: A549 cells, human lung adenocarcinoma cells, were used to form cisplatin (or cis-Diaminedichloroplatinum, CDDP)-resistant lung adenocarcinoma cells, i.e., A549/CDDP cells. EVs from A549 and A549/CDDP cells were extracted by gradient centrifugation method and were hence named EVs 1 and EVs 2, respectively. The A549 cells were treated with EVs 1 and EVs 2 for 48 hours, and the cells were named A549-EVs 1 and A549-EVs 2 cells, respectively. A549/ ABCG2 cells were established by transfecting A549 cells with pCDNA3.1- ABCG2 recombinant plasmids. On the other hand, A549 cells transfected with empty vectors were named A549/pCDNA3.1 cells. MTT assay was conducted to calculate the 24-hour cell drug resistance index for CDDP. The ABCG2 gene expression in cells and EVs were assessed with real-time PCR. A549 and A549-EVs 2 cells were transplanted subcutaneously into nude mice, which were labeled the control group and the experimental group accordingly. After tumor formation, 3 mg/kg CDDP was intraperitoneally injected once a week for two times. The ABCG2 gene expression of subcutaneous transplanted tumor cells was examined by real-time PCR. The cell apoptosis rate of subcutaneous transplanted tumor cells was examined by flow cytometry. Results: Using the parental A549 cells as reference, the 24-h CDDP-resistance indexes of 549/CDDP, A549/ ABCG 2, A549/pCDNA3.1, A549-EVs 1, A549-EVs 2 cells were 7.17, 10.06, 1.02, 1.19 and 5.40, respectively. When comparing the ABCG2 gene expression levels in all cells and EVs, the findings were higher in A549/CDDP cells than those inA549 cells, higher in A549/ ABCG2 cells than those in A549/pCDNA3.1 or A549 cells, higher in EVs 2 than those in EVs 1, and higher in A549-EVs 2 than those in A549-EVs 1 cells ( P<0.01) . The volume of transplanted tumor and the ABCG2 gene expression level in the experimental group were higher than those in the control group, while the apoptosis rate was lower than that in the control group ( P<0.01). Conclusion: EVs carrying ABCG2 gene can regulate the drug resistance of lung adenocarcinoma cells.