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Mood disorders are associated with elevated inflammation, and the reduction of symptoms after multiple treatments is often accompanied by pro-inflammation restoration. A variety of neuromodulation techniques that regulate regional brain activities have been used to treat refractory mood disorders. However, their efficacy varies from person to person and lack reliable indicator. This review summarizes clinical and animal studies on inflammation in neural circuits related to anxiety and depression and the evidence that neuromodulation therapies regulate neuroinflammation in the treatment of neurological diseases. Neuromodulation therapies, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), electroconvulsive therapy (ECT), photobiomodulation (PBM), transcranial ultrasound stimulation (TUS), deep brain stimulation (DBS), and vagus nerve stimulation (VNS), all have been reported to attenuate neuroinflammation and reduce the release of pro-inflammatory factors, which may be one of the reasons for mood improvement. This review provides a better understanding of the effective mechanism of neuromodulation therapies and indicates that inflammatory biomarkers may serve as a reference for the assessment of pathological conditions and treatment options in anxiety and depression.

Non-invasive neuromodulation technology is important for the treatment of brain diseases. The effects of focused ultrasound on neuronal activity have been investigated since the 1920s. Low intensity transcranial focused ultrasound (tFUS) can exert non-destructive mechanical pressure effects on cellular membranes and ion channels and has been shown to modulate the activity of peripheral nerves, spinal reflexes, the cortex, and even deep brain nuclei, such as the thalamus. It has obvious advantages in terms of security and spatial selectivity. This technology is considered to have broad application prospects in the treatment of neurodegenerative disorders and neuropsychiatric disorders. This review synthesizes animal and human research outcomes and offers an integrated description of the excitatory and inhibitory effects of tFUS in varying experimental and disease conditions.

Colorectal cancer (CRC) is the fourth leading cause of tumor-related deaths worldwide. In this study, we explored the in vivo effects of quercetin, a plant flavonol from the flavonoid group of polyphenols with antioxidant effects, on colon carcinogenesis induced by azoxymethane/dextran sodium sulfate (AOM/DSS). Thirty mice were randomly assigned into three groups: the control group, the AOM/DSS group, and the quercetin+AOM/DSS group. CRC was induced by AOM injection and a solution of 2% DSS in the drinking water. In the AOM/DSS-induced colon cancer mice model, quercetin treatment dramatically reduced the number and size of colon tumors. In addition, quercetin significantly restored the leukocyte counts by decreasing the inflammation caused by AOM/DSS. We also observed that the expression of oxidative stress markers, such as lipid peroxide (LPO), nitric oxide (NO), superoxide dismutase (SOD), glucose-6-phosphate (G6PD), and glutathione (GSH), could be reduced by quercetin, suggesting that the anti-inflammatory function of quercetin comes from its antioxidant effect. Moreover, potential biomarkers were identified with serum metabolite profiling. Increased levels of 2-hydroxybutyrate, 2-aminobutyrate, and 2-oxobutyrate and decreased levels of gentian violet, indole-3-methyl acetate, N-acetyl-5-hydroxytryptamine, indoxyl sulfate, and indoxyl were also found in the AOM/DSS-treated mice. However, quercetin treatment successfully decreased the levels of 2-hydroxybutyrate, 2-aminobutyrate, 2-oxobutyrate, endocannabinoids, and sphinganine and increased the levels of gentian violet, N-acetyl-5-hydroxytryptamine, indoxyl sulfate, and indoxyl. Together, our data demonstrated that quercetin could maintain relatively potent antitumor activities against colorectal cancer in vivo through its anti-inflammation effect.

This paper proposes two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure. As to the PDR approach, a more effective multi-threshold step detection algorithm is used to improve the positioning accuracy. According to pedestrians’ different walking patterns such as walking or running, this paper makes a comparative analysis of multiple step length calculation models to determine a linear computation model and the relevant parameters. In consideration of the deviation between the real heading and the value of the orientation sensor, a heading estimation method with real-time compensation is proposed, which is based on a Kalman filter with map geometry information. The corrected heading can inhibit the positioning error accumulation and improve the positioning accuracy of PDR. Moreover, this paper has implemented two positioning approaches integrated with Bluetooth and PDR. One is the PDR-based positioning method based on map matching and position correction through Bluetooth. There will not be too much calculation work or too high maintenance costs using this method. The other method is a fusion calculation method based on the pedestrians’ moving status (direct movement or making a turn) to determine adaptively the noise parameters in an Extended Kalman Filter (EKF) system. This method has worked very well in the elimination of various phenomena, including the “go and back” phenomenon caused by the instability of the Bluetooth-based positioning system and the “cross-wall” phenomenon due to the accumulative errors caused by the PDR algorithm. Experiments performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building in the China University of Mining and Technology (CUMT) campus showed that the proposed scheme can reliably achieve a 2-meter precision.

Varicocele (VC) is present in 35 - 40% of men with infertility. However, current surgical and antioxidant treatments are not completely effective. In addition to oxidative stress, it is likely that other factors such as testicular immune microenvironment disorder contribute to irreversible testicular. Evidence suggests that VC is associated with anti-sperm antibodies (ASAs), spermatogenesis and testosterone secretion abnormalities, and testicular cytokine production. Moreover, inhibition of inflammation can alleviate VC-mediated pathogenesis. The normal function of the testis depends on its immune tolerance mechanism. Testicular immune regulation is complex, and many infectious or non-infectious diseases may damage this precision system. The testicular immune microenvironment is composed of common immune cells and other cells involved in testicular immunity. The former includes testicular macrophages, T cells, dendritic cells (DCs), and mast cells, whereas the latter include Leydig cells and Sertoli cells (SCs). In animal models and in patients with VC, most studies have revealed an abnormal increase in the levels of ASAs and pro-inflammatory cytokines such as interleukin (IL)-1 and tumor necrosis factor (TNF)-alpha in the seminal plasma, testicular tissue, and even peripheral blood. It is also involved in the activation of potential inflammatory pathways, such as the nucleotide-binding oligomerization domain-like receptor family pyrin domain containing (NLRP)-3 pathway. Finally, the development of VC-mediated infertility (VMI) may be facilitated by abnormal permeability of proteins, such as claudin-11, that constitute the blood-testis barrier (BTB). The testicular immune response, including the production of ASAs and inflammatory factors, activation of inflammatory pathways, and destruction of the BTB may be involved in the pathogenesis of VMI it is necessary to further explore how patient outcomes can be improved through immunotherapy.

Background This study aimed to construct predictive models for the risk of sepsis in patients with Acute pancreatitis (AP) using machine learning methods and compared optimal one with the logistic regression (LR) model and scoring systems. Methods In this retrospective cohort study, data were collected from the Medical Information Mart for Intensive Care III (MIMIC III) database between 2001 and 2012 and the MIMIC IV database between 2008 and 2019. Patients were randomly divided into training and test sets (8:2). The least absolute shrinkage and selection operator (LASSO) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors. Based on the selected predictive factors, 6 machine learning models were constructed, including support vector machine (SVM), K-nearest neighbour (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT) and adaptive enhancement algorithm (AdaBoost). The models and scoring systems were evaluated and compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC). Results A total of 1, 672 patients were eligible for participation. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The predictive factors for the risk of sepsis in AP patients included age, insurance, vasopressors, mechanical ventilation, Glasgow Coma Scale (GCS), heart rate, respiratory rate, temperature, SpO2, platelet, red blood cell distribution width (RDW), International Normalized Ratio (INR), and blood urea nitrogen (BUN). The AUC of the GBDT model for sepsis prediction in the AP patients in the testing set was 0.985. The GBDT model showed better performance in sepsis prediction than the LR, systemic inflammatory response syndrome (SIRS) score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), and simplified acute physiology score II (SAPS II). Conclusion The present findings suggest that compared to the classical LR model and SOFA, qSOFA, SAPS II, SIRS, and BISAP scores, the machine learning model-GBDT model had a better performance in predicting sepsis in the AP patients, which is a useful tool for early identification of high-risk patients and timely clinical interventions.

The purpose of the study is to investigate the relationships and potential impacts of environmental pollutants, human resources, GDP, sustainable power sources, financial assets, and SAARC countries from 1995 to 2022. Board cointegration tests, D-H causality, cross-sectional reliance (CSD), Saville and Holdsworth Restricted (SHL), and the DSK Appraisal Strategy were among the logical techniques employed to discover long-term connections between these components. Results demonstrate that GDP growth, renewable energy sources (REC), and environmental pollution (ENP) all contribute to SAARC countries’ progress. However, future opportunities and HR are negatively impacted by increased ecological pollution. The results of the two-way causality test demonstrate a strong correlation between HR and future possibilities. Opportunities for the SAARC countries are closely related to the growth of total national output, the use of green electricity, and public support sources. Ideas for tackling future projects are presented in the paper’s conclusion. These include facilitating financial development, reducing ecological pollution, financing the progress of human resources, and promoting the use of sustainable power sources.

Interfacial tension (IFT) between water and crude oil is a crucial variable that enhanced oil recovery (EOR) techniques can adjust to increase oil extraction from depleted fields. Most of the developed intelligent models in the literature are based on synthetic oil samples rather than real crude oil samples or brine total salinity rather than salinity of each salt type. Hence, this study applies various machine learning approaches, such as Convolutional Neural Networks (CNN), Adaptive Boosting (AdaBoost), Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Ensemble Learning, Support Vector Machines (SVM), and Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) to develop advanced models for predicting the IFT between brine and crude oil considering real crude oil samples and taking the account of each salt type prevalent within the brine phase, which represent the realistic circumstances encountered in the oil reservoirs. These predictions are based on factors like the type and concentration of salt, the API of the crude oil, and the properties of the system (pressure and temperature) using previously published experimental data. A sensitivity analysis, incorporating a relevancy factor, is performed to highlight the influence of various input parameters on the IFT. Among these models, the Decision Tree is highlighted for its high accuracy and low training cost compared to ANN-based models, as evidenced by its emerged evaluation metrics (R-squared of 0.9796 and mean square error of 5e-4). It is noted that the AdaBoost model is the least accurate with an R 2 of 0.6696. Furthermore, the sensitivity analysis indicates that the molecular weight of the salt has the smallest impact on the IFT, whereas temperature has the most significant effect. The developed smart model may be used to accurately estimate crude oil/brine IFT without needing tedious, time-consuming and expensive experimental workflows.

Pyruvate kinase (PK) is a key enzyme that catalyzes the dephosphorylation of phosphoenolpyruvate (PEP) into pyruvate, and is responsible for the production of ATP during glycolysis. As another important isozyme of PK, pyruvate kinase M2 (PKM2) exists in cells with high levels of nucleic acid synthesis, such as normal proliferating cells (e.g., lymphocytes and intestinal epithelial cells), embryonic cells, adult stem cells, and tumor cells. With further research, PKM2, as an important regulator of cellular pathophysiological activity, has attracted increasing attention in the process of autoimmune response and inflammatory. In this re]view, we examine the contribution of PKM2 to the human immune response. Further studies on the immune mechanisms of PKM2 are expected to provide more new ideas and drug targets for immunotherapy of inflammatory and autoimmune diseases, guiding drug development and disease treatment.

[This corrects the article DOI: 10.1371/journal.pone.0296516.].