Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder. Typically, it is characterized by hirsutism, hyperandrogenism, ovulatory dysfunction, menstrual disorders and ...infertility. To date, its pathogenesis remains unclear. However, insulin resistance (IR) is considered as the primary pathological basis for its reproductive dysfunction. On the other hand, a condition in which insulin is over-secreted is called hyperinsulinemia. IR/Hyperinsulinemia is associated with chronic inflammation, hormonal changes, follicular dysplasia, endometrial receptivity changes, and abortion or infertility. Additionally, it increases incidence of complications during pregnancy and has been associated with anxiety, depression, and other psychological disorders. Gut microbiota, the "second genome" acquired by the human body, can promote metabolism, immune response through interaction with the external environment. Gut microbiota dysbiosis can cause IR, which is closely linked to the occurrence of PCOS. This article reviewed recent findings on the roles of gut microbiota in the development of insulin resistance and the mechanism underlying polycystic ovary syndrome.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Current evidence suggests high serum uric acid may increase the risk of type 2 diabetes, but the association is still uncertain. The aim of the study was to evaluate the association between serum ...uric acid and future risk of type 2 diabetes by conducting a meta-analysis of prospective cohort studies.
We conducted a systematic literature search of the PubMed database through April 2012. Prospective cohort studies were included in meta-analysis that reported the multivariate adjusted relative risks (RRs) and the corresponding 95% confidence intervals (CIs) for the association between serum uric acid and risk of type 2 diabetes. We used both fix-effects and random-effects models to calculate the overall effect estimate. The heterogeneity across studies was tested by both Q statistic and I(2) statistic. Begg's funnel plot and Egger's regression test were used to assess the potential publication bias.
We retrieved 7 eligible articles derived from 8 prospective cohort studies, involving a total of 32016 participants and 2930 incident type 2 diabetes. The combined RR of developing type 2 diabetes for the highest category of serum uric acid level compared with the lowest was 1.56(95% CI, 1.39-1.76). Dose-response analysis showed the risk of type 2 diabetes was increased by 6% per 1 mg/dl increment in serum uric acid level (RR 1.06, 95% CI: 1.04-1.07). The result from each subgroup showed a significant association between serum uric acid and risk of type 2 diabetes. In sensitive analysis, the combined RR was consistent every time omitting any one study. Little evidence of heterogeneity and publication bias was observed.
Our meta-analysis of prospective cohort studies provided strong evidence that high level of serum uric acid is independent of other established risk factors, especially metabolic syndrome components, for developing type 2 diabetes in middle-aged and older people.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It has been accepted that kidney function is connected with brain activity. In clinical studies, chronic kidney disease (CKD) patients have been found to be prone to suffering cognitive decline and ...Alzheimer’s disease (AD). The cognitive function of CKD patients may improve after kidney transplantation. All these indicators show a possible link between kidney function and dementia. However, little is known about the mechanism behind the relation of CKD and AD. This review discusses the associations between CKD and AD from the perspective of the pathophysiology of the kidney and complications and/or concomitants of CKD that may lead to cognitive decline in the progression of CKD and AD. Potential preventive and therapeutic strategies for AD are also presented. Further studies are warranted in order to confirm whether the setting of CKD is a possible new determinant for cognitive impairment in AD.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted ...static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machine-learning as an intelligent process to learn and utilize the complex time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula>-dimensional feature space to a single-dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least mean square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.
Conspectus Persistent luminescence nanoparticles (PLNPs) are unique optical materials emitting long-lasting luminescence after ceasing excitation. Such a unique optical feature allows luminescence ...detection without constant external illumination to avoid the interferences of autofluorescence and scattering light from biological fluids and tissues. Besides, near-infrared (NIR) PLNPs have advantages of deep penetration and the reactivation of the persistent luminescence (PL) by red or NIR light. These features make the application of NIR-emitting PLNPs in long-term bioimaging no longer limited by the lifetime of PL. To take full advantage of PLNPs for biological applications, the versatile strategies for bridging PLNPs and biological system become increasingly significant for the design of PLNPs-based nanoprobes. In this Account, we summarize our systematic achievements in the biological applications of PLNPs from biosensing/bioimaging to theranostics with emphasizing the engineering strategies for fabricating specific PLNPs-based nanoprobes. We take surface engineering and manipulating energy transfer as the major principles to design various PLNPs-based nanoprobes based on the nature of interactions between nanoprobes and targets. We have developed target-induced formation or interruption of fluorescence resonance energy transfer systems for autofluorescence-free biosensing and imaging of cancer biomarkers. We have decorated single or dual targeting ligands on PLNPs for tumor-targeted imaging, and integrated other modal imaging agents into PLNPs for multimodal imaging. We have also employed specific functionalization for various biomedical applications including chemotherapy, photodynamic therapy, photothermal therapy, stem cells tracking and PL imaging-guided gene therapy. Besides, we have modified PLNPs with multiple functional units to achieve challenging metastatic tumor theranostics. The proposed design principle and comprehensive strategies show great potential in guiding the design of PLNPs nanoprobes and promoting further development of PLNPs in the fields of biological science and medicine. We conclude this Account by outlining the future directions to further promote the practical application of PLNPs. The novel protocols for the synthesis of small-size, monodisperse, and water-soluble PLNPs with high NIR PL intensity and superlong afterglow are the vibrant directions for the biomedical applications of PLNPs. In-depth theories and evidence on luminescence mechanism of PLNPs are highly desired for further improvement of their luminescence performance. Furthermore, other irradiations without tissue penetrating depth limit, such as X-ray, are encouraged for use in energy storage and re-excitation of PLNPs, enabling imaging in deep tissue in vivo and integrating other X-ray sensitized theranostic techniques such as computed tomography imaging and radiotherapy. Last but not least, PLNPs-based nanoprobes and the brand new hybrids of PLNPs with other nanomaterials show a bright prospect for accurate diagnosis and efficient treatment of diseases besides tumors.
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IJS, KILJ, NUK, PNG, UL, UM
The 5G and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing ...attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing continuous protection, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/ unsupervised/reinforcement learning algorithms. In a nutshell, the machine-learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation under unknown network conditions and unpredictable dynamics.
Diabetic kidney disease (DKD) is one of the most common causes of end-stage renal disease worldwide. The treatment of DKD is strongly associated with clinical outcomes in patients with diabetes ...mellitus. Traditional therapeutic strategies focus on the control of major risk factors, such as blood glucose, blood lipids, and blood pressure. Renin–angiotensin–aldosterone system inhibitors have been the main therapeutic measures in the past, but the emergence of sodium–glucose cotransporter 2 inhibitors, incretin mimetics, and endothelin-1 receptor antagonists has provided more options for the management of DKD. Simultaneously, with advances in research on the pathogenesis of DKD, some new therapies targeting renal inflammation, fibrosis, and oxidative stress have gradually entered clinical application. In addition, some recently discovered therapeutic targets and signaling pathways, mainly in preclinical and early clinical trial stages, are expected to provide benefits for patients with DKD in the future. This review summarizes the traditional treatments and emerging management options for DKD, demonstrating recent advances in the therapeutic strategies for DKD.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Six-generation (6G) networks will contain a higher density of users, base stations, and communication equipment, which poses a significant challenge to secure communications and collaborations due to ...the complex network and environment as well as the number of resource-constraint devices used. Trust evaluation is the basis for secure communications and collaborations, providing an access criterion for interconnecting different nodes. Without a trust evaluation mechanism, the risk of cyberattacks on 6G networks will be greatly increased, which will eventually lead to the failure of network collaboration. For the sake of performing a comprehensive evaluation of nodes, this paper proposes a novel multiple role fusion trust evaluation framework that integrates multiple role fusion trust calculation and blockchain-based trust management. In order to take advantage of fused trust values for trust prediction, a neural network fitting method is utilized in the paper. This work further optimizes the traditional trust management framework and utilizes the optimized model for node trust prediction to better increase the security of communication systems. The results show that multiple role fusion has better stability than a single role evaluation network and better performance in anomaly detection and evaluation accuracy.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Petroleum refining unavoidably generates large volumes of oily wastewater. The environmentally acceptable disposal of oily wastewater is a current challenge to the petroleum industry. Nowadays, more ...attention has been focused on the treatment techniques of oily wastewater. Therefore, oily wastewater treatment has become an urgent problem, and it must be explored and resolved by every oilfield and petroleum company. The development status of treatment methods was summarized from six aspects, which contains flotation, coagulation, biological treatment, membrane separation technology, combined technology and advanced oxidation process. Finally, the development and prospect of treating oily wastewater was predicted.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with ...unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.
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