Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA. METHODS: We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS). RESULTS: The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups. CONCLUSIONS: In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Burden and trends of mental disorders in China from 1990 to 2019: findings from the Global Burden of Disease Study 2019.

PURPOSE: Mental disorders remain the leading causes of disability worldwide. We aimed to determine the burden and trends of mental disorders in China from 1990 to 2019. METHODS: The incidence, prevalence, and disability-adjusted life years (DALYs) of mental disorders at national level of China were examined by age, sex, and subcategories. Temporal trends in the age-standardized rates for incidence, prevalence, and DALYs were assessed by the average annual percentage change (AAPC). All estimates are presented as numbers and age-standardized rates, with 95% uncertainty intervals (UIs). RESULTS: The number of incident cases due to mental disorders increased from 42.90 million to 52.72 million, the number of prevalent cases increased from 132.63 million to 160.16 million, and the number of DALYs increased from 15.64 million to 20.29 million during 1990-2019. Decreasing trends were observed in the age-standardized rates for incidence, prevalence and DALYs. Anxiety and depressive disorders were more frequent in women, while ADHD, conduct disorder, and autism spectrum disorders were more common in men. Compared with 1990, the age-specific incidence rates were higher in individuals under 14 years and over 55 years, whereas rates were lower in those aged 15-49 years in 2019. CONCLUSION: The number of incident cases, prevalent cases, and DALYs due to mental disorders gradually increased in China from 1990 to 2019. Anxiety and depressive disorders were the leading causes of burden due to mental disorders, which affected women more than men. Mental disorders deserve greater attention in health policy decision making.

Research on Speech Synthesis Based on Mixture Alignment Mechanism.

In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment mechanism. Mixture-TTS aims to optimize the alignment information between text sequences and mel-spectrogram. Mixture-TTS uses a linguistic encoder based on soft phoneme-level alignment and hard word-level alignment approaches, which explicitly extract word-level semantic information, and introduce pitch and energy predictors to optimally predict the rhythmic information of the audio. Specifically, Mixture-TTS introduces a post-net based on a five-layer 1D convolution network to optimize the reconfiguration capability of the mel-spectrogram. We connect the output of the decoder to the post-net through the residual network. The mel-spectrogram is converted into the final audio by the HiFi-GAN vocoder. We evaluate the performance of the Mixture-TTS on the AISHELL3 and LJSpeech datasets. Experimental results show that Mixture-TTS is somewhat better in alignment information between the text sequences and mel-spectrogram, and is able to achieve high-quality audio. The ablation studies demonstrate that the structure of Mixture-TTS is effective.

The Method and Study of Detecting Phenanthrene in Seawater Based on a Carbon Nanotube-Chitosan Oligosaccharide Modified Electrode Immunosensor.

Phenanthrene (PHE), as a structurally simple, tricyclic, polycyclic aromatic hydrocarbon (PAHs), is widely present in marine environments and organisms, with serious ecological and health impacts. It is crucial to study fast and simple high-sensitivity detection methods for phenanthrene in seawater for the environment and the human body. In this paper, a immunosensor was prepared by using a multi-wall carbon nanotube (MWCNTs)-chitosan oligosaccharide (COS) nanocomposite membrane loaded with phenanthrene antibody. The principle was based on the antibody-antigen reaction in the immune reaction, using the strong electron transfer ability of multi-walled carbon nanotubes, coupled with chitosan oligosaccharides with an excellent film formation and biocompatibility, to amplify the detection signal. The content of the phenanthrene in seawater was studied via differential pulse voltammetry (DPV) using a potassium ferricyanide system as a redox probe. The antibody concentration, pH value, and probe concentration were optimized. Under the optimal experimental conditions, the response peak current of the phenanthrene was inversely proportional to the concentration of phenanthrene, in the range from 0.5 ng·mL-1 to 80 ng·mL-1, and the detection limit was 0.30 ng·mL-1. The immune sensor was successfully applied to the detection of phenanthrene in marine water, with a recovery rate of 96.1~101.5%, and provided a stable, sensitive, and accurate method for the real-time monitoring of marine environments.

Detection of Cd2+ based on Nano-Fe3O4/MoS2/Nafion/GCE sensor.

It is necessary to detect cadmium ions in seawater with high sensitivity because the pollution of cadmium ions seriously endangers the health and life of human beings. Nano-Fe3O4/MoS2/Nafion modified glassy carbon electrode was prepared by a drop coating method. The electrocatalytic properties of Nano-Fe3O4/MoS2/Nafion were measured by Cyclic Voltammetry (CV). Differential Pulse Voltammetry (DPV) was used to study the stripping Voltammetry response of the modified electrode to Cd2+. The optimal conditions were determined: In 0.1 mol/L HAc-NaAc solution, the solution pH was 4.2, the deposition potential was - 1.0 V, and the deposition time was 720 s, the membrane thickness was 8 µL. Under the optimum condition, the linear relation of Cd2+ concentration was found in the range of 5-300 µg/L, and the detection limit was 0.053 µg/L. The recovery of Cd2+ in seawater ranged from 99.2 to 102.9%. A composite material with simple operation, rapid response and high sensitivity was constructed for the determination of Cd2+ in seawater.

A Planar Disk Electrode Chip Based on MWCNT/CS/Pb2+ Ionophore IV Nanomaterial Membrane for Trace Level Pb2+ Detection.

Unlike conventional lead ion (Pb2+) detecting methods, electrochemical methods have the attractive advantages of rapid response, good portability and high sensitivity. In this paper, a planar disk electrode modified by multiwalled carbon nanotube (MWCNTs)/chitosan (CS)/lead (Pb2+) ionophore IV nanomaterial and its matched system are proposed. This system presented a good linear relationship between the concentration of Pb2+ ions and the peak current in differential pulse stripping voltammetry (DPSV), under optimized conditions of -0.8 V deposition potential, 5.5 pH value, 240 s deposition time, performed sensitive detection of Pb2+ within sensitivity of 1.811 µA · µg-1 and detection limit of 0.08 µg · L-1. Meanwhile, the results of the system in detecting lead ions in real seawater samples are highly similar to that of inductively coupled plasma emission spectrometer (ICP-MS), which proved a practicability for the system in detection of trace-level Pb2+.

Detection of BaP in seawater based on multi-walled carbon nanotubes composites immunosenor.

Benzo(a)pyrene, as the main polycyclic aromatic hydrocarbon pollutant in marine oil spill pollution, has negative effects on marine ecology and human health. A facile and sensitive method of rapid benzo(a)pyrene detection in seawater is essential for marine conservation. In this paper, a novel immunosensor is fabricated using a multi-walled carbon nanotubes-chitosan composite loaded with benzo(a)pyrene antibody. This immunosensor is based on a biosensing assay mechanism that uses multi-walled carbon nanotubes-chitosan composites as conductive mediators to enhance electron transfer kinetics. Then, potassium ferricyanide was used as an electrochemical probe to produce an electrochemical signal for the voltammetric behavior investigation of the immune response by differential pulse voltammetry. Under optimal experimental conditions, the peak current change was inversely proportional to the benzo(a)pyrene concentration in the range of 0.5 ng⋅ml-1 and 80 ng⋅ml-1 with a detection limit of 0.27 ng⋅ml-1. The immunosensor was successfully applied to assay BaP in seawater, and the recovery was between 96.6 and 100%, which exhibited a novel, sensitive and interference-resistant analytical method for real-time water environment monitoring. The results demonstrate that the proposed immunosensor has a great potential for application in the monitoring of seawater.

Doxycycline sensitizes renal cell carcinoma to chemotherapy by preferentially inhibiting mitochondrial translation.

OBJECTIVES: The anti-cancer activity of doxycycline has been reported in many cancers but not renal cell carcinoma (RCC). This study aimed to determine the efficacy of doxycycline alone and in combination with paclitaxel and analyze the underlying mechanism in RCC. METHODS: Proliferation, colony formation and apoptosis assays were performed in RCC cell lines after drug treatments. An RCC xenograft mouse model was generated, and tumor growth was monitored. Mechanistic studies focused on mitochondrial translation and functions. RESULTS: Doxycycline at clinically achievable concentrations inhibited proliferation and colony formation and induced apoptosis in RCC cell lines. In normal kidney cells, doxycycline at the same concentrations either had no effect or was less effective. The combination index value demonstrated that doxycycline and paclitaxel were synergistic in vitro. Consistently, this combination therapy was significantly more effective than the monotherapy in RCC xenograft mice without causing significant toxicity. Mechanistic studies revealed that doxycycline acts on RCC cells via preferentially inhibiting mitochondrial DNA translation, thereby disrupting multiple mitochondrial complexes and impairing mitochondrial respiration. CONCLUSIONS: Doxycycline is a useful addition to the treatment strategy for RCC. Our work also highlights the therapeutic value of mitochondrial translation inhibition in sensitizing RCC to chemotherapy.