ROS/mtROS promotes TNTs formation via the PI3K/AKT/mTOR pathway to protect against mitochondrial damages in glial cells induced by engineered nanomaterials.

BACKGROUND: As the demand and application of engineered nanomaterials have increased, their potential toxicity to the central nervous system has drawn increasing attention. Tunneling nanotubes (TNTs) are novel cell-cell communication that plays a crucial role in pathology and physiology. However, the relationship between TNTs and nanomaterials neurotoxicity remains unclear. Here, three types of commonly used engineered nanomaterials, namely cobalt nanoparticles (CoNPs), titanium dioxide nanoparticles (TiO2NPs), and multi-walled carbon nanotubes (MWCNTs), were selected to address this limitation. RESULTS: After the complete characterization of the nanomaterials, the induction of TNTs formation with all of the nanomaterials was observed using high-content screening system and confocal microscopy in both primary astrocytes and U251 cells. It was further revealed that TNT formation protected against nanomaterial-induced neurotoxicity due to cell apoptosis and disrupted ATP production. We then determined the mechanism underlying the protective role of TNTs. Since oxidative stress is a common mechanism in nanotoxicity, we first observed a significant increase in total and mitochondrial reactive oxygen species (namely ROS, mtROS), causing mitochondrial damage. Moreover, pretreatment of U251 cells with either the ROS scavenger N-acetylcysteine or the mtROS scavenger mitoquinone attenuated nanomaterial-induced neurotoxicity and TNTs generation, suggesting a central role of ROS in nanomaterials-induced TNTs formation. Furthermore, a vigorous downstream pathway of ROS, the PI3K/AKT/mTOR pathway, was found to be actively involved in nanomaterials-promoted TNTs development, which was abolished by LY294002, Perifosine and Rapamycin, inhibitors of PI3K, AKT, and mTOR, respectively. Finally, western blot analysis demonstrated that ROS and mtROS scavengers suppressed the PI3K/AKT/mTOR pathway, which abrogated TNTs formation. CONCLUSION: Despite their biophysical properties, various types of nanomaterials promote TNTs formation and mitochondrial transfer, preventing cell apoptosis and disrupting ATP production induced by nanomaterials. ROS/mtROS and the activation of the downstream PI3K/AKT/mTOR pathway are common mechanisms to regulate TNTs formation and mitochondrial transfer. Our study reveals that engineered nanomaterials share the same molecular mechanism of TNTs formation and intercellular mitochondrial transfer, and the proposed adverse outcome pathway contributes to a better understanding of the intercellular protection mechanism against nanomaterials-induced neurotoxicity.

Immunotherapy for Ovarian Cancer: Disappointing or Promising?

Ovarian cancer, one of the deadliest malignancies, lacks effective treatment, despite advancements in surgical techniques and chemotherapy. Thus, new therapeutic approaches are imperative to improving treatment outcomes. Immunotherapy, which has demonstrated considerable success in managing various cancers, has already found its place in clinical practice. This review aims to provide an overview of ovarian tumor immunotherapy, including its basics, key strategies, and clinical research data supporting its potential. In particular, this discussion highlights promising strategies such as checkpoint inhibitors, vaccines, and pericyte transfer, both individually and in combination. However, the advancement of new immunotherapies necessitates large controlled randomized trials, which will undoubtedly shape the future of ovarian cancer treatment.

Evaluation of neurotoxicity and the role of oxidative stress of cobalt nanoparticles, titanium dioxide nanoparticles, and multiwall carbon nanotubes in Caenorhabditis elegans.

The widespread use of nanomaterials in daily life has led to increased concern about their potential neurotoxicity. Therefore, it is particularly important to establish a simple and reproducible assessment system. Representative nanomaterials, including cobalt nanoparticles (CoNPs), titanium dioxide nanoparticles (TiO2-NPs), and multiwall carbon nanotubes (MWCNTs), were compared in terms of their neurotoxicity and underlying mechanisms. In 0, 25, 50, and 75 µg/ml of these nanomaterials, the survival, locomotion behaviors, acetylcholinesterase (AchE) activity, reactive oxygen species production, and glutathione-S transferase 4 (Gst-4) activation in wildtype and transgenic Caenorhabditis elegans (C. elegans) were evaluated. All nanomaterials induced an imbalance in oxidative stress, decreased the ratio of survival, impaired locomotion behaviors, as well as reduced the activity of AchE in C. elegans. Interestingly, CoNPs and MWCNTs activated Gst-4, but not TiO2-NPs. The reactive oxygen species scavenger, N-acetyl-l-cysteine, alleviated oxidative stress and Gst-4 upregulation upon exposure to CoNPs and MWCNTs, and rescued the locomotion behaviors. MWCNTs caused the most severe damage, followed by CoNPs and TiO2-NPs. Furthermore, oxidative stress and subsequent activation of Gst-4 were involved in nanomaterials-induced neurotoxicity. Our study provides a comprehensive comparison of the neurotoxicity and mechanisms of typical nanomaterials, which could serve as a model for hazard assessment of environmental pollutants using C. elegans as an experimental model system.

Ferroptosis: Mechanism and potential applications in cervical cancer.

Ferroptosis is a distinct form of cell death mechanism different from the traditional ones. Ferroptosis is characterized biochemically by lipid peroxidation, iron accumulation, and glutathione deficiency. It has already demonstrated significant promise in antitumor therapy. Cervical cancer (CC) progression is closely linked to iron regulation and oxidative stress. Existing research has investigated the role of ferroptosis in CC. Ferroptosis could open up a new avenue of research for treating CC. This review will describe the factors and pathways and the research basis of ferroptosis, which is closely related to CC. Furthermore, the review may provide potential future directions for CC research, and we believe that more studies concerning the therapeutic implications of ferroptosis in CC will come to notice.

Elevated dementia risk, cognitive decline, and hippocampal atrophy in multisite chronic pain.

Numerous studies have investigated the impacts of common types of chronic pain (CP) on patients' cognitive function and observed that CP was associated with later dementia. More recently, there is a growing recognition that CP conditions frequently coexist at multiple body sites and may bring more burdens on patients' overall health. However, whether and how multisite CP (MCP) contributes to an increased risk of dementia, compared to single-site CP (SCP) and pain-free (PF), is largely unclear. In the current study, utilizing the UK Biobank cohort, we first investigated dementia risk in individuals (n = 354,943) with different numbers of coexisting CP sites using Cox proportional hazards regression models. We then applied generalized additive models to investigate whether MCP leads to excessive deterioration of participants' (n = 19,116) cognition and brain structure. We found that individuals with MCP were associated with significantly higher dementia risk, broader and faster cognitive impairment, and greater hippocampal atrophy than both PF individuals and those with SCP. Moreover, the detrimental effects of MCP on dementia risk and hippocampal volume aggravated along with the number of coexisting CP sites. Mediation analyses further revealed that the decline of fluid intelligence in MCP individuals was partially mediated by hippocampal atrophy. Our results suggested that cognitive decline and hippocampal atrophy interact biologically and may underlie the increased risk of dementia associated with MCP.

Cobalt nanoparticles induce mitochondrial damage and ß-amyloid toxicity via the generation of reactive oxygen species.

Exposure to cobalt nanoparticles (CoNPs) has been associated with neurodegenerative disorders, while the mitochondrial-associated mechanisms that mediate their neurotoxicity have yet to be fully characterized. In this study, we reported that CoNPs exposure reduced the survival and lifespan in the nematodes, Caenorhabditis elegans (C. elegans). Moreover, exposure to CoNPs aggravated the induction of paralysis and the aggregation of ß-amyloid (Aß). These effects were accompanied by reactive oxygen species (ROS) overproduction, ATP reduction as well as mitochondrial fragmentation. Dynamin-related protein 1 (drp-1) activation and ensuing mitochondrial fragmentation have been shown to be associated with CoNPs-reduced survival. In order to address the role of mitochondrial damage and ROS production in CoNPs-induced Aß toxicity, the mitochondrial reactive oxygen species scavenger mitoquinone (Mito Q) was used. Our results showed that Mito Q pretreatment alleviated CoNPs-induced ROS generation, rescuing mitochondrial dysfunction, thereby lessening the CoNPs-induced Aß toxicity. Taken together, we show for the first time, that increasing of ROS and the upregulation of drp-1 lead to CoNPs-induced Aß toxicity. Our novel findings provide in vivo evidence for the mechanisms of environmental toxicant-induced Aß toxicity, and can afford new modalities for the prevention and treatment of CoNPs-induced neurodegeneration.

Asymmetric cryptosystem based on optical scanning cryptography and elliptic curve algorithm.

We propose an asymmetric cryptosystem based on optical scanning cryptography (OSC) and elliptic curve cryptography (ECC) algorithm. In the encryption stage of OSC, an object is encrypted to cosine and sine holograms by two pupil functions calculated via ECC algorithm from sender's biometric image, which is sender's private key. With the ECC algorithm, these holograms are encrypted to ciphertext, which is sent to the receiver. In the stage of decryption, the encrypted holograms can be decrypted by receiver's biometric private key which is different from the sender's private key. The approach is an asymmetric cryptosystem which solves the problem of the management and dispatch of keys in OSC and has more security strength than the conventional OSC. The feasibility of the proposed method has been convincingly verified by numerical and experiment results.

Accelerated Distributed Approximate Newton Method.

Distributed second-order optimization, as an effective strategy for training large-scale machine learning systems, has been widely investigated due to its low communication complexity. However, the existing distributed second-order optimization algorithms, including distributed approximate Newton (DANE), accelerated inexact DANE (AIDE), and statistically preconditioned accelerated gradient (SPAG), are all required to precisely solve an expensive subproblem up to the target precision. Therefore, this causes these algorithms to suffer from high computation costs and this hinders their development. In this article, we design a novel distributed second-order algorithm called the accelerated distributed approximate Newton (ADAN) method to overcome the high computation costs of the existing ones. Compared with DANE, AIDE, and SPAG, which are constructed based on the relative smooth theory, ADAN's theoretical foundation is built upon the inexact Newton theory. The different theoretical foundations lead to handle the expensive subproblem efficiently, and steps required to solve the subproblem are independent of the target precision. At the same time, ADAN resorts to the acceleration and can effectively exploit the objective function's curvature information, making ADAN to achieve a low communication complexity. Thus, ADAN can achieve both the communication and computation efficiencies, while DANE, AIDE, and SPAG can achieve only the communication efficiency. Our empirical study also validates the advantages of ADAN over extant distributed second-order algorithms.

Learning With Selected Features.

The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innovation, due to its computational and storage bottlenecks. This article presents a scalable algorithm based on subsampling, called learning with selected features (LSF), to reduce the computational burden of RLS. Almost the optimal learning rate together with a sufficient condition on selecting kernels and centers to guarantee the optimality is derived. Our theoretical assertions are verified by numerical experiments, including toy simulations, UCI standard data experiments, and a real-world massive data application. The studies in this article show that LSF can reduce the computational burden of RLS without sacrificing its generalization ability very much.

Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study.

BACKGROUND: Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, has become one of the major causes of death in Intensive Care Units (ICUs). The heterogeneity and complexity of this syndrome lead to the absence of golden standards for its diagnosis, treatment, and prognosis. The early prediction of in-hospital mortality for sepsis patients is not only meaningful to medical decision making, but more importantly, relates to the well-being of patients. METHODS: In this paper, a rule discovery and analysis (rule-based) method is used to predict the in-hospital death events of 2021 ICU patients diagnosed with sepsis using the MIMIC-III database. The method mainly includes two phases: rule discovery phase and rule analysis phase. In the rule discovery phase, the RuleFit method is employed to mine multiple hidden rules which are capable to predict individual in-hospital death events. In the rule analysis phase, survival analysis and decomposition analysis are carried out to test and justify the risk prediction ability of these rules. Then by leveraging a subset of these rules, we establish a prediction model that is both more accurate at the in-hospital death prediction task and more interpretable than most comparable methods. RESULTS: In our experiment, RuleFit generates 77 risk prediction rules, and the average area under the curve (AUC) of the prediction model based on 62 of these rules reaches 0.781 ([Formula: see text]) which is comparable to or even better than the AUC of existing methods (i.e., commonly used medical scoring system and benchmark machine learning models). External validation of the prediction power of these 62 rules on another 1468 sepsis patients not included in MIMIC-III in ICU provides further supporting evidence for the superiority of the rule-based method. In addition, we discuss and explain in detail the rules with better risk prediction ability. Glasgow Coma Scale (GCS), serum potassium, and serum bilirubin are found to be the most important risk factors for predicting patient death. CONCLUSION: Our study demonstrates that, with the rule-based method, we could not only make accurate prediction on in-hospital death events of sepsis patients, but also reveal the complex relationship between sepsis-related risk factors through the rules themselves, so as to improve our understanding of the complexity of sepsis as well as its population.

SURVFIT: Doubly sparse rule learning for survival data.

Survival data analysis has been leveraged in medical research to study disease morbidity and mortality, and to discover significant bio-markers affecting them. A crucial objective in studying high dimensional medical data is the development of inherently interpretable models that can efficiently capture sparse underlying signals while retaining a high predictive accuracy. Recently developed rule ensemble models have been shown to effectively accomplish this objective; however, they are computationally expensive when applied to survival data and do not account for sparsity in the number of variables included in the generated rules. To address these gaps, we present SURVFIT, a "doubly sparse" rule extraction formulation for survival data. This doubly sparse method can induce sparsity both in the number of rules and in the number of variables involved in the rules. Our method has the computational efficiency needed to realistically solve the problem of rule-extraction from survival data if we consider both rule sparsity and variable sparsity, by adopting a quadratic loss function with an overlapping group regularization. Further, a systematic rule evaluation framework that includes statistical testing, decomposition analysis and sensitivity analysis is provided. We demonstrate the utility of SURVFIT via experiments carried out on a synthetic dataset and a sepsis survival dataset from MIMIC-III.

Privacy-Preserving Cost-Sensitive Learning.

Cost-sensitive learning methods guaranteeing privacy are becoming crucial nowadays in many applications where increasing use of sensitive personal information is observed. However, there has no optimal learning scheme developed in the literature to learn cost-sensitive classifiers under constraint of enforcing differential privacy. Our approach is to first develop a unified framework for existing cost-sensitive learning methods by incorporating the weight constant and weight functions into the classical regularized empirical risk minimization framework. Then, we propose two privacy-preserving algorithms with output perturbation and objective perturbation methods, respectively, to be integrated with the cost-sensitive learning framework. We showcase how this general framework can be used analytically by deriving the privacy-preserving cost-sensitive extensions of logistic regression and support vector machine. Experimental evidence on both synthetic and real data sets verifies that the proposed algorithms can reduce the misclassification cost effectively while satisfying the privacy requirement. A theoretical investigation is also conducted, revealing a very interesting analytic relation, i.e., that the choice of the weight constant and weight functions does not only influence the Fisher-consistent property (population minimizer of expected risk with a specific loss function leads to the Bayes optimal decision rule) but also interacts with privacy-preserving levels to affect the performance of classifiers significantly.

Down-regulation of Exosomal miR-214-3p Targeting CCN2 Contributes to Endometriosis Fibrosis and the Role of Exosomes in the Horizontal Transfer of miR-214-3p.

Endometriosis (EMs) is defined as the presence of tissue which somewhat resembles endometrial glands and stroma outside the uterus, and elicits fibrosis. Fibrosis is the main factor resulting in pain and infertility, while the aetiology of endometrial fibrosis is unknown. There is strong evidence from numerous experiments showing that connective tissue growth factor (CCN2) plays a central role in fibrogenesis. Exosomal miR-214-3p can regulate the expression of CCN2 through binding to complementary sites in the 3' untranslated region. This study aimed to explore the role of exosomal miR-214-3p in endometriosis fibrosis and the relationship between CCN2 and miR-214-3p in endometriosis fibrosis. Our results demonstrated that miR-214-3p was significantly down-regulated and CCN2 was up-regulated in EMs ectopic lesion and stromal cells compared with EMs eutopic and endometrium of patients without endometriosis. Exosomal miR-214-3p can inhibit fibrosis in EMs through targeting CCN2. The results were explored and verified in vitro and in vivo, respectively. Cell co-culture was used to explore the contributions of exosomes to intercellular information transmission of miR-214-3p. The results showed that exosomes play a pivotal role in the transportation of miR-214-3p between cells. Furthermore, level of exosomal miR-214-3p in endometriosis patients' serum was lower than that in patients without endometriosis. In conclusion, exosomal miR-214-3p can inhibit fibrosis in EMs by targeting CCN2. MiR-214-3p may be considered as a bio-marker and has a potential therapeutic effect in EMs.

Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network.

To meet the growing electricity demand, China's power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO2 and NOX emissions from power plants using systematic actual measurements monitored by China's continuous emission monitoring systems (CEMS) network over 96-98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions.

Portal Nodes Screening for Large Scale Social Networks.

Network autoregression model (NAM), as a powerful tool to study user social behaviors on large scale social networks, has drawn great attention in recent years. In this paper, we are interested in identifying the influential users (i.e., portal nodes) in a social network under the framework of NAM. Especially, we consider the autoregression model that allows to have a heterogenous and sparse network effect coefficients. Therefore, the portal nodes take influential powers which are corresponding to the nonzero network effect coefficients. A screening procedure is designed to screen out the portal nodes and the strong screening consistency is established theoretically. A quasi maximum likelihood method is applied to estimate the influential powers. The asymptotic normality of the resulting estimator is established. Further selection procedure is given by taking advantage of the local linear approximation algorithm. Extensive numerical studies are conducted by using a Sina Weibo dataset for illustration purpose.

Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making.

Background: There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data led to the emergence of artificial intelligence (AI). This article emphasizes the capabilities of AI to identify patterns of SSI data. Method: Artificial intelligence comprises various subfields that present potential solutions to identify patterns of SSI data. Discussions on opportunities, challenges, and limitations of applying these methods to derive accurate SSI prediction are provided. Results: Four main challenges in dealing with SSI data were defined: (1) complexities in using SSI data, (2) disease knowledge, (3) decision support, and (4) heterogeneity. The implications of some of the recent advances in AI methods to optimize clinical effectiveness were discussed. Conclusions: Artificial intelligence has the potential to provide insight in detecting and decision-support of SSI. As we turn SSI data into intelligence about the disease, we increase the possibility of improving surgical practice with the promise of a future optimized for the highest quality patient care.

A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images.

Background: Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. Methods: Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. Conclusions: Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.

Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation.

Low-rank matrix estimation arises in a number of statistical and machine learning tasks. In particular, the coefficient matrix is considered to have a low-rank structure in multivariate linear regression and multivariate quantile regression. In this paper, we propose a method called penalized matrix least squares approximation (PMLSA) toward a unified yet simple low-rank matrix estimate. Specifically, PMLSA can transform many different types of low-rank matrix estimation problems into their asymptotically equivalent least-squares forms, which can be efficiently solved by a popular matrix fast iterative shrinkage-thresholding algorithm. Furthermore, we derive analytic degrees of freedom for PMLSA, with which a Bayesian information criterion (BIC)-type criterion is developed to select the tuning parameters. The estimated rank based on the BIC-type criterion is verified to be asymptotically consistent with the true rank under mild conditions. Extensive experimental studies are performed to confirm our assertion.

Statistical patterns of human mobility in emerging Bicycle Sharing Systems.

The emerging Bicycle Sharing System (BSS) provides a new social microscope that allows us to "photograph" the main aspects of the society and to create a comprehensive picture of human mobility behavior in this new medium. BSS has been deployed in many major cities around the world as a short-distance trip supplement for public transportations and private vehicles. A unique value of the bike flow data generated by these BSSs is to understand the human mobility in a short-distance trip. This understanding of the population on short-distance trip is lacking, limiting our capacity in management and operation of BSSs. Many existing operations research and management methods for BSS impose assumptions that emphasize statistical simplicity and homogeneity. Therefore, a deep understanding of the statistical patterns embedded in the bike flow data is an urgent and overriding issue to inform decision-makings for a variety of problems including traffic prediction, station placement, bike reallocation, and anomaly detection. In this paper, we aim to conduct a comprehensive analysis of the bike flow data using two large datasets collected in Chicago and Hangzhou over months. Our analysis reveals intrinsic structures of the bike flow data and regularities in both spatial and temporal scales such as a community structure and a taxonomy of the eigen-bike-flows.

Learning Rates for Classification with Gaussian Kernels.

This letter aims at refined error analysis for binary classification using support vector machine (SVM) with gaussian kernel and convex loss. Our first result shows that for some loss functions, such as the truncated quadratic loss and quadratic loss, SVM with gaussian kernel can reach the almost optimal learning rate provided the regression function is smooth. Our second result shows that for a large number of loss functions, under some Tsybakov noise assumption, if the regression function is infinitely smooth, then SVM with gaussian kernel can achieve the learning rate of order [Formula: see text], where [Formula: see text] is the number of samples.