The Role of Cell Cycle Regulators in Cell Survival—Dual Functions of Cyclin-Dependent Kinase 20 and p21Cip1/Waf1

The mammalian cell cycle is important in controlling normal cell proliferation and the development of various diseases. Cell cycle checkpoints are well regulated by both activators and inhibitors to avoid cell growth disorder and cancerogenesis. Cyclin dependent kinase 20 (CDK20) and p21^Cip1/Waf1 are widely recognized as key regulators of cell cycle checkpoints controlling cell proliferation/growth and involving in developing multiple cancers. Emerging evidence demonstrates that these two cell cycle regulators also play an essential role in promoting cell survival independent of the cell cycle, particularly in those cells with a limited capability of proliferation, such as cardiomyocytes. These findings bring new insights into understanding cytoprotection in these tissues. Here, we summarize the new progress of the studies on these two molecules in regulating cell cycle/growth, and their new roles in cell survival by inhibiting various cell death mechanisms. We also outline their potential implications in cancerogenesis and protection in heart diseases. This information renews the knowledge in molecular natures and cellular functions of these regulators, leading to a better understanding of the pathogenesis of the associated diseases and the discovery of new therapeutic strategies.     

Human responses to high levels of carbon dioxide and air temperature

In this study, 30 subjects were exposed to different combinations of air temperature (T_a: 24, 27, and 30°C) and CO₂ level (8000, 10 000, and 12 000 ppm) in a high-humidity (RH: 85%) underground climate chamber. Subjective assessments, physiological responses, and cognitive performance were investigated. The results showed that as compared with exposure to T_a = 24°C, exposure to 30°C at all CO₂ levels caused subjects to feel uncomfortably warm and experience stronger odor intensity, while increased mental effort and greater intensity of acute health symptoms were reported. However, no significant effects of T_a on task performance or physiological responses were found. This indicated that subjects had to exert more effort to maintain their performance in an uncomfortably warm environment. Increasing CO₂ from 8000 to 12 000 ppm at all T_a caused subjects to report higher rates of headache, fatigue, agitation, and feeling depressed, although the results were statistically significant only at 24 and 27°C. The text typing performance and systolic blood pressure (SBP) decreased significantly at this exposure, whereas diastolic blood pressure (DBP) and thermal discomfort increased significantly. These effects suggest higher arousal/stress. No significant interaction effect of T_a and CO₂ concentration on human responses was identified.     

Raman and X-ray photoelectron spectroscopy study on the influence of La2O3 on the melt structure of SiO2–CaO–Al2O3–MgO

In order to gain more insights into the influence of rare earth elements on the melt structure of SiO₂–CaO–Al₂O₃–MgO glass ceramics, Raman and X-ray photoelectron spectroscopy techniques were used to study the influence of La₂O₃ on the Si–O/Al–O tetrahedron structure within SiO₂–CaO–Al₂O₃–MgO–quenched glass samples in this study. Results showed that some Raman peak shapes at low frequencies (200–840 cm^?1) changed significantly after the addition of La₂O₃, compared to the high frequency (840–1200 cm^?1) region that corresponds to the [SiO₄] structure, suggesting that the depolymerization of the low-frequency T–O–T (T=Si or Al) structure was more prevalent with La³⁺ addition. Besides, the depolymerization extent of the Si–O/Al–O tetrahedral network varied when the melt composition altered. Most notably, depolymerization is the most significant at a low CaO/SiO₂ ratio (0.25) and a high Al₂O₃ content (8%). Meanwhile, La³⁺ can promote the transformation of Si–O–Si and Al–O–Al bonds to the Si–O–Al ones, thereby forming a complex ionic cluster network interwoven with Si–O and Al–O tetrahedrons.     

Effect of Li2O on the crystallization behavior of CaO-Al2O3-SiO2-Li2O-Ce2O3 mold slags

The effect of Li₂O on the crystallization properties of CaO-Al₂O₃-SiO₂-Li₂O-Ce₂O₃ slags was investigated. With increasing the Li₂O content, LiAlO₂ and CaCeAlO₄ were the main crystalline phases. LiAlO₂ formed for the charge compensating of Li⁺ ions to [AlO₄^5?]-tetrahedrons, and CaCeAlO₄ formed as a result of the charge balance of Ce³⁺ ions, Ca²⁺ ions, and [AlO₆^9?]-octahedrons. Increasing the content of Li₂O to 10%, the crystallization temperature was the highest, and the incubation time was the shortest. The crystallization ability was strong due to the three factors of strengthening the interaction between ions and ion groups, decreasing the polymerization degree, and increasing the melting temperature. Further increasing the content of Li₂O, the crystallization performance was obviously suppressed, because the melting temperature and the force between the cations and the anion groups decreased.     

Characterization,in vitro bioactivity and biological studies of sol-gel-derived TiO2 substituted 58S bioactive glass

Bioactive glasses (BGs) have been used for bone formation and bone repair processes in recent years. This study investigated the titanium substitution effect on 58S BGs (Ti-BGs) 60SiO₂-(36 − X)CaO-4P₂O₅-XTiO₂ (X = 0, 3, and 5 mol.%) prepared by the sol-gel technique, and the main goal was to find the optimum amount of titanium in Ti-BGs. Synthesized BGs, which were investigated after immersion in simulated body fluid (SBF), were tested by X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy. Moreover alkaline phosphate (ALP) activity, 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and antibacterial studies were employed to investigate the biological properties of Ti-BGs. According to the FTIR and XRD test results, hydroxyapatite (HA) formation on Ti-BGs surfaces was confirmed. Meanwhile, the presence of 5 mol.% compared to 3 mol.% increased the HA grain distribution and their size on the Ti-BGs surface. Additionally, MTT and ALP results confirmed that the optimal amount of titanium substitution in BG was 5 mol.%. Since 5 mol.% Ti incorporated BG (BG-5) had the highest biocompatibility level, antibacterial properties, maximum cell proliferation, and ALP activity among the synthesized Ti-BGs, it is presented as the best candidate for further in vivo investigations.     

Red-shift and improved afterglow of Sr3Al2-xBxO5Cl2:Eu2+, Dy3+ phosphors via a cation substitution strategy

Sr₃Al_2-xB_xO₅Cl₂:Eu²⁺, Dy³⁺ (x = 0, 0.2, 0.4) long persistent phosphors were prepared via solid-state process. The pristine Sr₃Al₂O₅Cl₂:Eu²⁺, Dy³⁺ phosphor exhibits orange/red broad band emission around 609 nm, which can be attributed to the electric radiation transitions 4f⁶5 d¹→4f⁷ of Eu²⁺. Upon the same excitation, the B³⁺-doped Sr₃Al_2-xB_xO₅Cl₂:Eu²⁺, Dy³⁺ phosphors display red-shift from 609 nm to 625 nm with increasing B³⁺ concentrations. The XRD patterns show that Al³⁺ can be replaced by B³⁺ in the host lattice at the tetrahedral site, which causes lattice contraction and crystal field enhancement, and thereafter achieves the red-shift on the emission spectrum. The XPS investigation provides direct evidence of the dominant 2-valent europium in the phosphor, which can be ascribed for the broad band emission of the prepared phosphors. The afterglow of all phosphors show standard double exponential decay behavior, and the afterglow of Sr₃Al₂O₅Cl₂:Eu²⁺, Dy³⁺is rather weak, while the sample co-doped with B³⁺shows longer and stronger afterglow, as confirmed after the curve simulation. The analysis of thermally stimulated luminescence showed that, when B³⁺ is introduced, a much deeper trap is created, and the density of the electron trap is also significantly increased. As a result, B³⁺ ions caused redshift and enhanced afterglow for the Sr₃Al_2-xB_xO₅Cl₂:Eu²⁺, Dy³⁺ phosphor.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Fabrication and characterizations of Cr3+-doped ZnGa2O4 transparent ceramics with persistent luminescence

0.5 at.% Cr:ZnGa₂O₄ precursor was synthesized by the co-precipitation method with nitrates as raw materials, using ammonium carbonate as the precipitant. Low-agglomerated Cr:ZnGa₂O₄ powders with an average particle size of 43 nm were obtained by calcining the precursor at 900℃ for 4 h. Using the powders as starting materials, 0.5 at.% Cr:ZnGa₂O₄ ceramics with an average grain size of about 515 nm were prepared by presintering at 1150℃ for 5 h in air and HIP post-treatment at 1100℃ for 3 h under 200 MPa Ar. The in-line transmittance of 0.5 at.% Cr:ZnGa₂O₄ ceramics with a thickness of 1.3 mm reaches 59.5% at the wavelength of 700 nm. The Cr:ZnGa₂O₄ ceramics can be effectively excited by visible light and produce persistent luminescence at 700 nm. For Cr:ZnGa₂O₄ transparent ceramics, the brightness of afterglow was larger than 0.32 mcd/m² after 30 min, which is far superior to that of Cr:ZnGa₂O₄ persistent luminescence powders.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.