Inhibition of Cyclin D1 Expression in Human Glioblastoma Cells is Associated with Increased Temozolomide Chemosensitivity

Background/Aims: Cyclin D1 (CCND1) is frequently overexpressed in malignant gliomas. We have previously shown ectopic overexpression of CCND1 in human malignant gliomas cell lines. Methods: Quantitative reverse transcriptase PCR (qRT-PCR) and Western Blot (WB) was performed to investigate the expression of CCND1 in glioma tissues and cell lines. The biological function of CCND1 was also investigated through knockdown and overexpression of BCYRN1 in vitro. Results: Here we reported that CCND1 expression was positively associated with the pathological grade and proliferative activity of astrocytomas, as the lowest expression was found in normal brain tissue (N = 3) whereas the highest expression was in high-grade glioma tissue (N = 25). Additionally, we found that the expression level of CCND1 was associated with IC50 values in malignant glioma cell lines. Forced inhibition of CCND1 increased temozolomide efficacy in U251 and SHG-44 cells. After CCND1 overexpression, the temozolomide efficacy decreased in U251 and SHG-44 cells. Colony survival assay and apoptosis analysis confirmed that CCND1 inhibition renders cells more sensitive to temozolomide treatment and temozolomide-induced apoptosis in U251 and SHG-44 cells. Inhibition of P-gp (MDR1) by Tariquidar overcomes the effects of CCND1 overexpression on inhibiting temozolomide-induced apoptosis. Inhibition of CCND1 inhibited cell growth in vitro and in vivo significantly more effectively after temozolomide treatments than single temozolomide treatments. Finally, inhibition of CCND1 in glioma cells reduced tumor volume in a murine model. Conclusion: Taken together, these data indicate that CCND1 overexpression upregulate P-gp and induces chemoresistance in human malignant gliomas cells and that inhibition of CCND1 may be an effective means of overcoming CCND1 associated chemoresistance in human malignant glioma cells

Intervening Effects of Total Alkaloids of Corydalis saxicola Bunting on Rats With Antibiotic-Induced Gut Microbiota Dysbiosis Based on 16S rRNA Gene Sequencing and Untargeted Metabolomics Analyses

Gut microbiota dysbiosis induced by antibiotics is strongly connected with health concerns. Studying the mechanisms underlying antibiotic-induced gut microbiota dysbiosis could help to identify effective drugs and prevent many serious diseases. In this study, in rats with antibiotic-induced gut microbiota dysbiosis treated with total alkaloids of Corydalis saxicola Bunting (TACS), urinary and fecal biochemical changes and cecum microbial diversity were investigated using 16S rRNA gene sequencing analysis and untargeted metabolomics. The microbial diversity results showed that 10 genera were disturbed by the antibiotic treatment, and two of them were obviously restored by TACS. The untargeted metabolomics analysis identified 34 potential biomarkers in urine and feces that may be the metabolites that are most related to the mechanisms underlying antibiotic-induced gut microbiota dysbiosis and the therapeutic effects of TACS treatment. The biomarkers were involved in six metabolic pathways, comprising pathways related to branched-chain amino acid (BCAA), bile acid, arginine and proline, purine, aromatic amino acid, and amino sugar and nucleotide sugar metabolism. Notably, there was a strong correlation between these metabolic pathways and two gut microbiota genera (g__Blautia and g__Intestinibacter). The correlation analysis suggested that TACS might synergistically affect four of these metabolic pathways (BCAA, bile acid, arginine and proline, and purine metabolism), thereby modulating gut microbiota dysbiosis. Furthermore, we performed a molecular docking analysis involving simulating high-precision docking and using molecular pathway maps to illuminate the way that ligands (the five main alkaloid components of TACS) act on a complex molecular network, using CYP27A1 (a key enzyme in the bile acid synthesis pathway) as the target protein. This study provides a comprehensive overview of the intervening effects of TACS on the host metabolic phenotype and gut microbiome in rats with gut microbiota dysbiosis, and it presents new insights for the discovery of effective drugs and the best therapeutic approaches

Epidemiology and clinical course of COVID-19 in Shanghai, China.

Background: Novel coronavirus pneumonia (COVID-19) is prevalent around the world. We aimed to describe epidemiological features and clinical course in Shanghai. Methods: We retrospectively analysed 325 cases admitted at Shanghai Public Health Clinical Center, between January 20 and February 29, 2020. Results: 47.4% (154/325) had visited Wuhan within 2 weeks of illness onset. 57.2% occurred in 67 clusters; 40% were situated within 53 family clusters. 83.7% developed fever during the disease course. Median times from onset to first medical care, hospitalization and negative detection of nucleic acid by nasopharyngeal swab were 1, 4 and 8 days. Patients with mild disease using glucocorticoid tended to have longer viral shedding in blood and feces. At admission, 69.8% presented with lymphopenia and 38.8% had elevated D-dimers. Pneumonia was identified in 97.5% (314/322) of cases by chest CT scan. Severe-critical patients were 8% with a median time from onset to critical disease of 10.5 days. Half required oxygen therapy and 7.1% high-flow nasal oxygen. The case fatality rate was 0.92% with median time from onset to death of 16 days. Conclusion: COVID-19 cases in Shanghai were imported. Rapid identification, and effective control measures helped to contain the outbreak and prevent community transmission

A 5.4GHz wide tuning range CMOS PLL using an auto-calibration multiple-pass ring oscillator

A 5.4GHz multiple-pass ring voltage controlled oscillator (VCO) based phase-locked loop (PLL) is described. For the sake of active devices’ sensitivity to process and temperature regarding ring oscillators, an effective automatic frequency calibration scheme is proposed. A new process-independent differential to single (DTOS) is used to adjust control voltage range and loop gain. The chip is implemented in 0.18-um CMOS process and achieves phase noise of -100dBc/Hz@1MHz and a 40% tuning range

The cubital tunnel syndrome caused by intraneural ganglion cyst of the ulnar nerve at the elbow: a case report

Abstract Background Cubital tunnel syndrome is common nerve compression syndrome among peripheral nerve compression diseases. However, the syndrome caused by intraneural ganglion cysts has been rarely reported. Medical approaches, like ultrasound-guided aspiration and open surgical treatment remain to be discussed. Case presentation A 57-year-old woman presented with occasional pain, numbness and paralysis in her left hand and a palpable, painless mass in the ulnar side of her left elbow. Ultrasound-guided aspiration of the mass was performed to decompress the ulnar nerve. The patient experienced an evident release of pain in her hand, but symptoms of numbness and paralysis recurred 3 months later which greatly bothered the patient’s daily life. After evaluation, we had to perform an open surgery to excise the cyst. External neurolysis and anterior subcutaneous transposition were done. The patient was followed up for 2 years, and she made a complete recovery with no functional limitation. Conclusions The symptoms caused by intraneural ganglion cyst can be alleviated by accurate puncture. But puncture may be not complete and symptoms could recur. Complete external neurolysis can be counted as a complete and reliable treatment. Therefore, early diagnosis, careful preoperative imaging assessment and full decompression can be expected to receive a good rehabilitation

Low Cd content emitted by humans into the atmosphere

According to the data in May, September and October 1993, the variation range of Cd content in the water body of Jiaozhou Bay was 0.07-0.23μg/L, which conforms to the national water quality standard of Class I. It indicated that in May, September and October, the water in the entire water area of Jiaozhou Bay was not contaminated by Cd content. In May, the variation range of Cd content in the waters of Jiaozhou Bay was 0.09-0.18μg/L. In the coastal waters of the north of Jiaozhou Bay, the Cd content reached a relatively high value, which was 0.18μg/L. In September, the variation range of Cd content in the waters of Jiaozhou Bay was 0.07-0.23μg/L. In the coastal waters of the east of Jiaozhou Bay, the Cd content reached the highest value, 0.23μg/L. In October, the variation range of Cd content in the waters of Jiaozhou Bay was 0.08-0.18μg/L. In the coastal waters of the east of Jiaozhou Bay, the Cd content reached a relative high value, 0.18μg/L. In terms of Cd content, the water quality of Jiaozhou Bay had reached high quality. The water was clean, and it was not polluted by Cd content at all. The Cd content in the waters of Jiaozhou Bay mainly came from two sources, the transport of surface runoff and the transport of atmospheric deposition. The Cd content from surface runoff transportation was 0.18μg/L, and the Cd content from atmospheric deposition transportation was 0.18-0.23μg/L. The Cd content transported by atmospheric deposition was very close to the Cd content transported by surface runoff, and was very low, ranging from 0.18 to 0.23μg/L, far less than 1.00 μg/L. This revealed that the humans had realized the importance of environmental protection, and the emissions to the environment were very low. The atmosphere, land and sea were not polluted by Cd content. The Cd content transported by atmospheric deposition 0.18-0.23μg/L ≥ the Cd content transported by surface runoff 0.18μg/L, which indicated that the Cd content was mainly discharged into the atmosphere by humans and then deposited on the land

T.: A Surface Reconstruction Method for Highly Noisy Point Clouds

Abstract. In this paper we propose a surface reconstruction method for highly noisy and non-uniform data based on minimal surface model and tensor voting method. To deal with ill-posedness, noise and/or other uncertainties in the data we processes the raw data first using tensor voting before we do surface reconstruction. The tensor voting procedure allows more global and robust communications among the data to extract coherent geometric features and saliency independent of the surface reconstruction. These extracted information will be used to preprocess the data and to guide the final surface reconstruction. Numerically the level set method is used for surface reconstruction. Our method can handle complicated topology as well as highly noisy and/or non-uniform data set. Moreover, improvements of efficiency in implementing the tensor voting method are also proposed. We demonstrate the ability of our method using synthetic and real data.

PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity

Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products

An adversarial time-frequency reconstruction network for unsupervised anomaly detection

Detecting anomalies in massive volumes of multivariate time series data, particularly in the IoT domain, is critical for maintaining stable systems. Existing anomaly detection models based on reconstruction techniques face challenges in distinguishing normal and abnormal samples from unlabeled data, leading to performance degradation. Moreover, accurately reconstructing abnormal values and pinpointing anomalies remains a limitation. To address these issues, we introduce the Adversarial Time–Frequency Reconstruction Network for Unsupervised Anomaly Detection (ATF-UAD). ATF-UAD consists of a time reconstructor, a frequency reconstructor and a dual-view adversarial learning mechanism. The time reconstructor utilizes a parity sampling mechanism to weaken the dependency between neighboring points. Then attention mechanisms and graph convolutional networks (GCNs) are used to update the feature information for each point, which combines points with close feature relationships and dilutes the influence of abnormal points on normal points. The frequency reconstructor transforms the input sequence into the frequency domain using a Fourier transform and extracts the relationship between frequencies to reconstruct anomalous frequency bands. The dual-view adversarial learning mechanism aims to maximize the normal values in the reconstructed sequences and highlight anomalies and aid in their localization within the data. Through dual-view adversarial learning, ATF-UAD minimizes reconstructed value errors and maximizes the identification of residual outliers. We conducted extensive experiments on nine datasets from different domains, and ATF-UAD showed an average improvement of 6.94% in terms of F1 score compared to the state-of-the-art method