Millimeter-scale magnetic implants paired with a fully integrated wearable device for wireless biophysical and biochemical sensing.

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors mainly requires transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces biocompatibility, or limits portability. Here, we develop a set of millimeter-scale, chip-less, and battery-less magnetic implants paired with a fully integrated wearable device for measuring biophysical and biochemical signals. The wearable device can induce a large amplitude damped vibration of the magnetic implants and capture their subsequent motions wirelessly. These motions reflect the biophysical conditions surrounding the implants and the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities of measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure, and CSF glucose levels. This miniaturized system opens the possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism.

Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model.

The water body's suspended concentration reflects many coastal environmental indicators, which is important for predicting ecological hazards. The modeling of any concentration in water requires solving the settling-diffusion equation (SDE), and the values of several key input parameters therein (settling velocity ws, eddy diffusivity Ds, and erosion rates p(t)) directly determine the prediction performance. The time-consuming large-scale simulations would benefit if the parameter values could be estimated through available observations in the target sea area. The present work proposes a new optimization method for synchronously estimating the three parameters from limited concentration observations. First, an analytical solution to the one-dimensional vertical (1DV) SDE for suspended concentrations in an unsteady scenario is derived. Second, the near bottom suspended sediment concentration (SSC) profiles are measured with high-resolution observation. Third, the key parameters are optimized through the best fit of the measured SSC profiles and those modeled with the unsteady solution. Nonlinear least square fitting (NLSF) is introduced to judge the best fits automatically. The high-resolution concentration measurements in a specially-designed cylindrical tank experiment using the Yellow River Delta sediments test the proposed method. The method performs well in the initial period of turbulence generation when sediment resuspension is significant. It optimizes p(t), ws, and Ds with reasonable values and uniqueness of their combination. The proposed theory is a practical tool for quickly estimating key substance transport parameters from limited observations; it also has the potential to construct local parametric models to benefit the 3D modeling of coastal substance transport. Although the present work takes SSC as an example, it can be extended to any suspended particulate concentration in the water.

Continuous genetic monitoring of transient mesenchymal gene activities in distal tubule and collecting duct epithelial cells during renal fibrosis.

Epithelial cells (ECs) have been proposed to contribute to myofibroblasts or fibroblasts through epithelial-mesenchymal transition (EMT) during renal fibrosis. However, since EMT may occur dynamically, transiently, and reversibly during kidney fibrosis, conventional lineage tracing based on Cre-loxP recombination in renal ECs could hardly capture the transient EMT activity, yielding inconsistent results. Moreover, previous EMT research has primarily focused on renal proximal tubule ECs, with few reports of distal tubules and collecting ducts. Here, we generated dual recombinases-mediated genetic lineage tracing systems for continuous monitoring of transient mesenchymal gene expression in E-cadherin+ and EpCAM+ ECs of distal tubules and collecting ducts during renal fibrosis. Activation of key EMT-inducing transcription factor (EMT-TF) Zeb1 and mesenchymal markers αSMA, vimentin, and N-cadherin, were investigated following unilateral ureteral obstruction (UUO). Our data revealed that E-cadherin+ and EpCAM+ ECs did not transdifferentiate into myofibroblasts, nor transiently expressed these mesenchymal genes during renal fibrosis. In contrast, in vitro a large amount of cultured renal ECs upregulated mesenchymal genes in response to TGF-ß, a major inducer of EMT.

Evaluating the Immune Response of a Nanoemulsion Adjuvant Vaccine Against Methicillin-Resistant Staphylococcus aureus (MRSA) Infection.

Nanoemulsion adjuvant vaccines have attracted extensive attention because of their small particle size, high thermal stability, and ability to induce validly immune responses. However, establishing a series of comprehensive protocols to evaluate the immune response of a novel nanoemulsion adjuvant vaccine is vital. Therefore, this article features a rigorous procedure to determine the physicochemical characteristics of a vaccine (by transmission electron microscopy [TEM], atomic force microscopy [AFM], and dynamic light scattering [DLS]), the stability of the vaccine antigen and system (by a high-speed centrifuge test, a thermodynamic stability test, SDS-PAGE, and western blot), and the specific immune response (IgG1, IgG2a, and IgG2b). Using this approach, researchers can evaluate accurately the protective effect of a novel nanoemulsion adjuvant vaccine in a lethal MRSA252 mouse model. With these protocols, the most promising nanoemulsion vaccine adjuvant in terms of effective adjuvant potential can be identified. In addition, the methods can help optimize novel vaccines for future development.

The advantages of penehyclidine hydrochloride over atropine in acute organophosphorus pesticide poisoning: A meta-analysis.

Background: Penehyclidine hydrochloride (PHC) has been used for many years as an anticholinergic drug for the treatment of acute organophosphorus pesticide poisoning (AOPP). The purpose of this meta-analysis was to explore whether PHC has advantages over atropine in the use of anticholinergic drugs in AOPP. Methods: We searched Scopus, Embase, Cochrane, PubMed, ProQuest, Ovid, Web of Science, China Science and Technology Journal Database (VIP), Duxiu, Chinese Biomedical literature (CBM), WanFang, and Chinese National Knowledge Infrastructure (CNKI), from inception to March 2022. After all qualified randomized controlled trials (RCTs) were included, we conducted quality evaluation, data extraction, and statistical analysis. Statistics using risk ratios (RR), weighted mean difference (WMD), and standard mean difference (SMD). Results: Our meta-analysis included 20,797 subjects from 240 studies across 242 different hospitals in China. Compared with the atropine group, the PHC group showed decreased mortality rate (RR=0.20, 95% confidence intervals [CI]: 0.16-0.25, P <0.001), hospitalization time (WMD=-3.89, 95% CI: -4.37 to -3.41, P <0.001), overall incidence rate of complications (RR=0.35, 95% CI: 0.28-0.43, P <0.001), overall incidence of adverse reactions (RR=0.19, 95% CI: 0.17-0.22, P <0.001), total symptom disappearance time (SMD=-2.13, 95% CI: -2.35 to -1.90, P <0.001), time for cholinesterase activity to return to normal value 50-60% (SMD=-1.87, 95% CI: -2.03 to -1.70, P <0.001), coma time (WMD=-5.57, 95% CI: -7.20 to -3.95, P <0.001), and mechanical ventilation time (WMD=-2.16, 95% CI: -2.79 to -1.53, P <0.001). Conclusion: PHC has several advantages over atropine as an anticholinergic drug in AOPP.

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as - 25.88 in Wuhan and - 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

The Numerical Investigation of the Performance of a Newly Designed Sediment Trap for Horizontal Transport Flux.

Marine sediment transport is closely related to seafloor topography, material transport, marine engineering safety, etc. With a developed time-series vector observation device, the sediment capture and transport process can be observed. The structure of the capture tube and the internal filter screen can significantly affect the flow field during the actual observation, further influencing the sediment transport observation and particle capture process. This paper presents a numerical model for investigating the effect of device structure on seawater flow to study the processes of marine sediment transport observation and sediment particle capture. The model is based on the solution of both porous media and the Realizable k-ε turbulence in Fluent software. The flow velocity distribution inside and outside the capture tube with different screen pore sizes (0.300, 0.150, and 0.075 mm) is analyzed. To enhance the reliability of the numerical simulation, the simulation calculation results are compared with the test results and have good coincidence. Finally, by analyzing the motion law of sediment in the capture tube, the accurate capture of sediment particles is achieved, and the optimal capture efficiency of the sediment trap is obtained.

Genomic Profiling Reveals Novel Predictive Biomarkers for Chemo-Radiotherapy Efficacy and Thoracic Toxicity in Non-Small-Cell Lung Cancer.

Chemo-radiotherapy (CRT) remains the main treatment modality for non-small-cell lung cancer (NSCLC). However, its clinical efficacy is largely limited by individual variations in radio-sensitivity and radiotherapy-associated toxicity. There is an urgent need to identify genetic determinants that can explain patients' likelihood to develop recurrence and radiotherapy-associated toxicity following CRT. In this study, we performed comprehensive genomic profiling, using a 474-cancer- and radiotherapy-related gene panel, on pretreatment biopsy samples from patients with unresectable stage III NSCLCs who underwent definitive CRT. Patients' baseline clinical characteristics and genomic features, including tumor genetic, genomic and molecular pathway alterations, as well as single nucleotide polymorphisms (SNPs), were correlated with progression-free survival (PFS), overall survival (OS), and radiotherapy-associated pneumonitis and/or esophagitis development after CRT. A total of 122 patients were enrolled between 2014 and 2019, with 84 (69%) squamous cell carcinomas and 38 (31%) adenocarcinomas. Genetic analysis confirmed the association between the KEAP1-NRF2 pathway gene alterations and unfavorable survival outcome, and revealed alterations in FGFR family genes, MET, PTEN, and NOTCH2 as potential novel and independent risk factors of poor post-CRT survival. Combined analysis of such alterations led to improved stratification of the risk populations. In addition, patients with EGFR activating mutations or any oncogenic driver mutations exhibited improved OS. On the other hand, we also identified genetic markers in relation to radiotherapy-associated thoracic toxicity. SNPs in the DNA repair-associated XRCC5 (rs3835) and XRCC1 (rs25487) were associated with an increased risk of high-grade esophagitis and pneumonitis respectively. MTHFR (rs1801133) and NQO1 (rs1800566) were additional risk alleles related to higher susceptibility to pneumonitis and esophagitis overall. Moreover, through their roles in genome integrity and replicative fidelity, somatic alterations in ZNF217 and POLD1 might also serve as risk predictors of high-grade pneumonitis and esophagitis. Taken together, leveraging targeted next-generating sequencing, we identified a set of novel clinically applicable biomarkers that might enable prediction of survival outcomes and risk of radiotherapy-associated thoracic toxicities. Our findings highlight the value of pre-treatment genetic testing to better inform CRT outcomes and clinical actions in stage III unresectable NSCLCs.

Newly Designed and Experimental Test of the Sediment Trap for Horizontal Transport Flux.

The transport processes of marine suspended sediments are important to the material cycle and the shaping of seafloor topography. Existing sediment monitoring methods are limited in their use under high concentration conditions, and are not effective in monitoring and capturing sediment in 3D directions, and there is an inability to accurately explain sediment transport processes. To infer the transport process of suspended sediments, this study proposed a time-series vector in situ observation device. An accompanying time-series analytic method was developed for sediment transport fluxes. The correlation between the internal and external flow velocities of the capture tube was established through indoor tests, and then the applicability of the device was verified by the correlation between the theoretical capture quality and the actual capture quality, and the analytic formula of the flux was refined. The proposed observation technique can be used for in situ long-term observation and sampling of marine suspended sediments under conventional and even extreme sea conditions, achieving accurate time-series suspended sediment capture and high-resolution transport flux analysis. The technique thus provides a more effective means for scientific research into the dynamics of seafloor sedimentation, the mechanisms of ocean carbon sinks, and the processes of the carbon cycle.

A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment.

An in-situ monitoring of water quality (suspended sediment concentration, SSC) and concurrent hydrodynamics was conducted in the subaqueous Yellow River Delta in China. Empirical mode decomposition and spectral analysis on the SSC time series reveal the different periodicities of each physical mechanism that contribute to the SSC variations. Based on this physical understanding, the decomposed SSC time series were trained separately with a newly-proposed augmented lncosh ridge regression, in which (1) a lncosh function was incorporated in traditional ridge regression for handling outliers in original data, and (2) the temporal auto-correlation in the decomposed SSC series was used for augmented regression. Finally, the trained sub-series were added up as the final prediction. The advantages of this decomposition-ensemble framework is that it depends on SSC only, superior to the normal process-based models which need the concurrent hydrodynamics for estimating bed shear stress. This will not only reduce the measurement uncertainties of the input when training the data-driven model, but also save the prediction cost as no other parameters than SSC need to be measured and input for running the model. The framework realized 6-hour-ahead high-accuracy forecasting with mean relative errors of 5.80-9.44% in the present case study. The proposed framework can be extended to forecast any signal that is superposed by components with various timescales (periodicities) which is common in nature.

Comprehensive Next-Generation Sequencing Reveals Novel Predictive Biomarkers of Recurrence and Thoracic Toxicity Risks After Chemoradiation Therapy in Limited Stage Small Cell Lung Cancer.

PURPOSE: Although definitive chemoradiation therapy (dCRT) remains the most effective treatment modality in limited stage small cell lung cancer (SCLC), some patients progress quickly or develop serious radiation-induced thoracic toxicity (RITT). Molecular correlates of response to dCRT remain to be explored. METHODS AND MATERIALS: Genomic profiling was performed retrospectively on 231 patients with limited-stage SCLC treated with dCRT between 2015 and 2019 using a customized panel covering cancer and radiation therapy response-related genes. Exploratory associations of progression-free survival, overall survival, and RITT with clinical features, tumor genetics, genomic and molecular pathway alterations, and single nucleotide polymorphisms were conducted. RESULTS: In addition to the common SCLC genes, such as TP53, RB1, and NOTCH1/2, potentially actionable mutations in EGFR, KRAS, and BRCA1/2 were among the top alterations in the cohort. At the single-gene level, CDK4 and GATA6 alterations were independent predictors of poor survival by multivariate analysis. At the genomic level, high tumor mutational burden was strongly associated with favorable survival outcome. Pathway-level analysis showed that activating mutations in the MAPK/ERK pathway genes, particularly those in EGFR/ERBB2, correlated with poor survival. Combined analysis enabled optimized risk stratification of post-dCRT survival. On the other hand, our study also confirmed that single nucleotide polymorphisms in MTHFR, CYP2B6, NQO1, and LIG4 were risk alleles of high-grade RITT. Remarkably, somatic loss-of-function mutations in the DNA damage repair pathway genes were associated with increased risk of high-grade RITT, particularly pneumonitis, which likely reflect a complex interplay between the tumor and its immune microenvironment. CONCLUSIONS: Taken together, by examining the mutational landscape of a large cohort of limited-stage SCLC, we identified novel molecular predictors of survival and RITT. Our findings also implicate several key molecular pathways, including the MAPK/ERK and DNA damage repair pathways, in the regulation of dCRT response.

Transcriptomics Changes in the Peritoneum of Mice with Lipopolysaccharide-Induced Peritonitis.

Peritonitis caused by LPS is a severe clinical challenge, which causes organ damage and death. However, the mechanism of LPS-induced peritonitis has not been fully revealed yet. Here, we investigated the transcriptome profile of the peritoneal tissue of LPS-induced peritonitis in mice. A model of LPS-induced peritonitis in mice was established (LPS 10 mg/kg, i.p.), and the influence of TAK 242 (TLR4 inhibitor) on the level of inflammatory cytokines in mouse peritoneal lavage fluid was investigated by using an ELISA test. Next, the peritoneal tissues of the three groups of mice (Control, LPS, and LPS+TAK 242) (n = 6) were isolated and subjected to RNA-seq, followed by a series of bioinformatics analyses, including differentially expressed genes (DEGs), enrichment pathway, protein-protein interaction, and transcription factor pathway. Then, qPCR verified-hub genes that may interact with TAK 242 were obtained. Subsequently, the three-dimensional structure of hub proteins was obtained by using homology modeling and molecular dynamics optimization (300 ns). Finally, the virtual docking between TAK 242 and hub proteins was analyzed. Our results showed that TAK 242 significantly inhibited the production of inflammatory cytokines in the peritoneal lavage fluid of mice with peritonitis, including IL-6, IFN-γ, IL-1ß, NO, and TNF-α. Compared with the Control group, LPS treatment induced 4201 DEGs (2442 down-regulated DEGs and 1759 up-regulated DEGs). Compared with the LPS group, 30 DEGs were affected by TAK 242 (8 down-regulated DEGs and 22 up-regulated DEGs). A total of 10 TAK 242-triggered hub genes were obtained, and the possible docking modes between TAK 242 and hub proteins were acquired. Overall, our data demonstrated that a large number of DEGs were affected in LPS-triggered peritonitis mice. Moreover, the TLR4 inhibitor TAK 242 is capable of suppressing the inflammatory response of LPS-induced peritonitis. Our work provides clues for understanding the pathogenesis of LPS-induced peritonitis in mice.

Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio and Development of a Nomogram in Breast Cancer Patients.

PURPOSE: The objective of this study was to explore the prognostic significance of pretreatment hematologic parameters in predicting disease-free survival (DFS) of breast cancer patients. MATERIALS AND METHODS: The medical records of 440 breast cancer patients in Shandong Cancer Hospital and Institute from 2003 to 2013 were analyzed retrospectively. Through the results of blood routine before treatment, the absolute lymphocyte count (ALC), absolute neutrophil count (ANC), absolute monocyte count (AMC), and absolute platelet count (APC) in peripheral blood were collected. The lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-monocyte ratio (NMR) were calculated. Cox proportional hazard model was used for univariate and multivariate analysis. The DFS was compared using Kaplan-Meier method. The prognostic nomogram of patients with breast cancer was developed. RESULTS: The median DFS for all patients was 64.10 months. Univariate analysis showed that the DFS was associated with surgical approach, TNM stage, molecular subtype, neoadjuvant chemotherapy, radiotherapy, and LMR (p < 0.05). TNM stage, molecular subtype, and LMR were independent prognostic factors of breast cancer in multivariate analysis (p < 0.05). According to the Kaplan-Meier survival curve analysis, patients with higher LMR (≥4.85) were associated with longer median DFS (median DFS, 85.83 vs. 60.90, p < 0.001). The proposed nomogram that incorporated LMR, TNM stage, and molecular subtype got a concordance index (c-index) of 0.69 in predicting 5-year DFS. CONCLUSION: In breast cancer patients, higher LMR was associated with longer median DFS and the nomogram including LMR, TNM stage, and molecular subtype could accurately predict the prolonged 5-year DFS of breast cancer patients.

Developing more sensitive genomic approaches to detect radioresponse in precision radiation oncology: From tissue DNA analysis to circulating tumor DNA.

Despite the common application and considerable efforts to achieve precision radiotherapy (RT) in several types of cancer, RT has not yet entered the era of precision medicine; the ability to predict radiosensitivity and treatment responses in tumors and normal tissues is lacking. Therefore, development of genome-based methods for individual prognosis in radiation oncology is urgently required. Traditional DNA sequencing requires tissue samples collected during invasive operations; therefore, repeated tests are nearly impossible. Intra- and inter-tumoral heterogeneity may undermine the predictive power of a single assay from tumor samples. In contrast, analysis of circulating tumor DNA (ctDNA) allows for non-invasive and near real-time sampling of tumors. By investigating the genetic composition of tumors and monitoring dynamic changes during treatment, ctDNA analysis may potentially be clinically valuable in prediction of treatment responses prior to RT, surveillance of responses during RT, and evaluation of residual disease following RT. As a biomarker for RT response, ctDNA profiling may guide personalized treatments. In this review, we will discuss approaches of tissue DNA sequencing and ctDNA detection and summarize their clinical applications in both traditional RT and in combination with immunotherapy.