A Flexible Wearable Sensor Based on Laser-Induced Graphene for High-Precision Fine Motion Capture for Pilots.

There has been a significant shift in research focus in recent years toward laser-induced graphene (LIG), which is a high-performance material with immense potential for use in energy storage, ultrahydrophobic water applications, and electronic devices. In particular, LIG has demonstrated considerable potential in the field of high-precision human motion posture capture using flexible sensing materials. In this study, we investigated the surface morphology evolution and performance of LIG formed by varying the laser energy accumulation times. Further, to capture human motion posture, we evaluated the performance of highly accurate flexible wearable sensors based on LIG. The experimental results showed that the sensors prepared using LIG exhibited exceptional flexibility and mechanical performance when the laser energy accumulation was optimized three times. They exhibited remarkable attributes, such as high sensitivity (~41.4), a low detection limit (0.05%), a rapid time response (response time of ~150 ms; relaxation time of ~100 ms), and excellent response stability even after 2000 s at a strain of 1.0% or 8.0%. These findings unequivocally show that flexible wearable sensors based on LIG have significant potential for capturing human motion posture, wrist pulse rates, and eye blinking patterns. Moreover, the sensors can capture various physiological signals for pilots to provide real-time capturing.

Efficacy and influencing factor analysis of Voriconazole in the treatment of invasive fungal infections.

Current study aims to explore the correlation between the administered dose and efficacy of voriconazole in the treatment of invasive fungal infection (IFI). The correlation between different doses of Voriconazole and plasma concentrations as well as clinical efficacy was counted. Consequently, 40 strains of pathogenic micro-organisms were isoninelated and cultured from etiological samples. A total of 66 patients with steady-state trough serum concentrations ranging from 1.0 to 5.5 µg/mL were measured, with a compliance rate of 79.5%. Chi-square test showed that there was a significant correlation between Voriconazole steady-state serum trough concentration and treatment efficacy. In addition, the result of Pearson test showed that steady-state trough serum concentration of Voriconazole was significantly positively correlated with the administered dose (γ = 0.866, P < 0.001). On conclusion, Voriconazole is effective in treatment of IFI, and there is a significant dose-plasma concentration correlation with efficacy.

Flexible Wearable Strain Sensors Based on Laser-Induced Graphene for Monitoring Human Physiological Signals.

Flexible wearable strain sensors based on laser-induced graphene (LIG) have attracted significant interest due to their simple preparation process, three-dimensional porous structure, excellent electromechanical characteristics, and remarkable mechanical robustness. In this study, we demonstrated that LIG with various defects could be prepared on the surface of polyimide (PI) film, patterned in a single step by adjusting the scanning speed while maintaining a constant laser power of 12.4 W, and subjected to two repeated scans under ambient air conditions. The results indicated that LIG produced at a scanning speed of 70 mm/s exhibited an obvious stacked honeycomb micropore structure, and the flexible strain sensor fabricated with this material demonstrated stable resistance. The sensor exhibited high sensitivity within a low strain range of 0.4-8.0%, with the gauge factor (GF) reaching 107.8. The sensor demonstrated excellent stability and repeatable response at a strain of 2% after approximately 1000 repetitions. The flexible wearable LIG-based sensor with a serpentine bending structure could be used to detect various physiological signals, including pulse, finger bending, back of the hand relaxation and gripping, blinking eyes, smiling, drinking water, and speaking. The results of this study may serve as a reference for future applications in health monitoring, medical rehabilitation, and human-computer interactions.

Hippocampal Proteomics Reveals the Role of Glutamatergic Synapse Activation in the Depression Induced by Perfluorooctane Sulfonate.

Perfluorooctane sulfonate (PFOS), a new type of persistent organic pollutant in the environment of water, has drawn significant attention in recent years due to its widespread prevalence and high toxicity. Neurotoxicity is regarded as one of the major toxic effects of PFOS, while research studies on PFOS-induced depression and the underlying mechanisms remain scarce. In this study, behavioral tests revealed the depressive-like behaviors in PFOS-exposed male mice. Neuron damages including pyknosis and staining deepening were identified through hematoxylin and eosin staining. Then, we noticed the elevation of glutamate and proline levels as well as the decline of glutamine and tryptophan levels. Proteomics analysis identified 105 differentially expressed proteins that change in a dose-dependent manner and revealed that PFOS exposure activated the glutamatergic synapse signaling pathway, which were further confirmed by Western blot, and the data were consistent with the findings of the proteomics analysis. Additionally, the downstream signaling cyclic AMP-responsive element-binding protein (CREB)/brain-derived neurotrophic factor (BDNF) and synaptic plasticity-related postsynaptic density protein 95, synaptophysin, were downregulated. Our results highlight that PFOS exposure may inhibit the synaptic plasticity of the hippocampus via glutamatergic synapse and the CREB/BDNF signaling pathway to cause depressive-like behaviors in male mice.

Therapeutic potential of tumor treating fields for malignant brain tumors.

BACKGROUND: Malignant brain tumors are among the most threatening diseases of the central nervous system, and despite increasingly updated treatments, the prognosis has not been improved. Tumor treating fields (TTFields) are an emerging approach in cancer treatment using intermediate-frequency and low-intensity electric field and can lead to the development of novel therapeutic options. RECENT FINDINGS: A series of biological processes induced by TTFields to exert anti-cancer effects have been identified. Recent studies have shown that TTFields can alter the bioelectrical state of macromolecules and organelles involved in cancer biology. Massive alterations in cancer cell proteomics and transcriptomics caused by TTFields were related to cell biological processes as well as multiple organelle structures and activities. This review addresses the mechanisms of TTFields and recent advances in the application of TTFields therapy in malignant brain tumors, especially in glioblastoma (GBM). CONCLUSIONS: As a novel therapeutic strategy, TTFields have shown promising results in many clinical trials, especially in GBM, and continue to evolve. A growing number of patients with malignant brain tumors are being enrolled in ongoing clinical studies demonstrating that TTFields-based combination therapies can improve treatment outcomes.

Positron emission tomography molecular imaging to monitor anti-tumor systemic response for immune checkpoint inhibitor therapy.

Immune checkpoint inhibitors (ICIs) achieve a milestone in cancer treatment. Despite the great success of ICI, ICI therapy still faces a big challenge due to heterogeneity of tumor, and therapeutic response is complicated by possible immune-related adverse events (irAEs). Therefore, it is critical to assess the systemic immune response elicited by ICI therapy to guide subsequent treatment regimens. Positron emission tomography (PET) molecular imaging is an optimal approach in cancer diagnosis, treatment effect evaluation, follow-up, and prognosis prediction. PET imaging can monitor metabolic changes of immunocytes and specifically identify immuno-biomarkers to reflect systemic immune responses. Here, we briefly review the application of PET molecular imaging to date of systemic immune responses following ICI therapy and the associated rationale.

Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention.

In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as "attention" to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.

Determination of Osimertinib, Aumolertinib, and Furmonertinib in Human Plasma for Therapeutic Drug Monitoring by UPLC-MS/MS.

The third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), osimertinib, aumolertinib, and furmonertinib represent a new treatment option for patients with EGFR p.Thr790 Met (T790 M)-mutated non-small cell lung cancer (NSCLC). Currently, there are no studies reporting the simultaneous quantification of these three drugs. A simple ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for the simultaneous quantitative determination of osimertinib, aumolertinib, and furmonertinib concentrations in human plasma, and it was applied for therapeutic drug monitoring (TDM). Plasma samples were processed using the protein precipitation method (acetonitrile). A positive ion monitoring mode was used for detecting analytes. D3-Sorafenib was utilized as the internal standard (IS), and the mobile phases were acetonitrile (containing 0.1% formic acid) and water with gradient elution on an XSelect HSS XP column (2.1 mm × 100.0 mm, 2.5 µm, Waters, Milford, MA, USA) at a flow rate of 0.5 mL·min-1. The method's selectivity, precision (coefficient of variation of intra-day and inter-day ≤ 6.1%), accuracy (95.8-105.2%), matrix effect (92.3-106.0%), extraction recovery, and stability results were acceptable according to the guidelines. The linear ranges were 5-500 ng·mL-1, 2-500 ng·mL-1, and 0.5-200 ng·mL-1 for osimertinib, aumolertinib, and furmonertinib, respectively. The results show that the method was sensitive, reliable, and simple and that it could be successfully applied to simultaneously determine the osimertinib, aumolertinib, and furmonertinib blood concentrations in patients. These findings support using the method for TDM, potentially reducing the incidence of dosing blindness and adverse effects due to empirical dosing and inter-patient differences.

Positron emission tomography molecular imaging-based cancer phenotyping.

During the past several decades, numerous studies have provided insights into biological characteristics of cancer cells and identified various hallmarks of cancer acquired in the tumorigenic processes. However, it is still challenging to image these distinctive traits of cancer to facilitate the management of patients in clinical settings. The rapidly evolving field of positron emission tomography (PET) imaging has provided opportunities to investigate cancer's biological characteristics in vivo. This article reviews the current status of PET imaging on characterizing hallmarks of cancer and discusses the future directions of PET imaging strategies facilitating in vivo cancer phenotyping.

Integrating analysis of circular RNA and mRNA expression profiles in doxorubicin induced cardiotoxicity mice.

Doxorubicin (DOX)-induced cardiotoxicity impedes its clinical application, but the mechanisms have not been thoroughly elucidated. Based on circRNA and mRNA expression profiles, we illustrated RNA expression signature changes during DOX-induced cardiotoxicity; mechanism exploration and biomarkers screening were also conducted. Twelve mice were randomly divided into two groups, induction group was treated with doxorubicin, and the control group was given an equal quantity of saline. After the confirmation of myocardial injury in induction group, the heart tissues from both groups were isolated for RNA high-throughput sequencing. The expression profiles were compared between the two groups; a total of 295 mRNAs and 11 circRNAs were shown as biased expression in DOX-induced cardiotoxicity mouse hearts. The dysregulation of three circRNAs were validated by quantitative real-time PCR: mmu_circ_0015773, mmu_circ_0002106, and mmu_circ_001606. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differentially expressed RNAs were performed; the results implied that DOX might cause cardiotoxicity by interfering hemoglobin-based oxygen delivery and DNA-associated signal pathways. We integrated the differential expressed mRNA and validated circRNAs by constructing a competing endogenous RNA (ceRNA) network, which indicated that the alteration of the three circRNAs could activate apoptosis process of myocardial cells. This study provided novel insight into the mechanisms of DOX induced cardiotoxicity, and potential biomarkers or therapeutic targets were also proposed.

Integration analysis of metabolites and single nucleotide polymorphisms improves the prediction of drug response of celecoxib.

INTRODUCTION: Pharmacogenetics and pharmacometabolomics are the common methods for personalized medicine, either genetic or metabolic biomarkers have limited predictive power for drug response. OBJECTIVES: In order to better predict drug response, the study attempted to integrate genetic and metabolic biomarkers for drug pharmacokinetics prediction. METHODS: The study chose celecoxib as study object, the pharmacokinetic behavior of celecoxib was assessed in 48 healthy volunteers based on UPLC-MS/MS platform, and celecoxib related single nucleotide polymorphisms (SNPs) were also detected. Three mathematic models were constructed for celecoxib pharmacokinetics prediction, the first one was mainly based on celecoxib-related SNPs; the second was based on the metabolites selected from a pharmacometabolomic analysis by using GC-MS/MS method, the last model was based on the combination of the celecoxib-related SNPs and metabolites above. RESULTS: The result proved that the last model showed an improved prediction power, the integration model could explain 71.0% AUC variation and predict 62.3% AUC variation. To facilitate clinical application, ten potential celecoxib-related biomarkers were further screened, which could explain 68.3% and predict 54.6% AUC variation, the predicted AUC was well correlated with the measured values (r = 0.838). CONCLUSION: This study provides a new route for personalized medicine, the integration of genetic and metabolic biomarkers can predict drug response with a higher accuracy.

Predicting Pharmacokinetics Variation of Faropenem Using a Pharmacometabonomic Approach.

Individual variation in pharmacokinetics of faropenem is significant. We attempted to predict drug response of faropenem using a pharmacometabonomic approach. Metabolic profiling was performed on predose plasma samples from 36 healthy volunteers by gas chromatography-mass spectrometry (GC/MS), with 204 endogenous metabolites detected. Plasma concentration was measured after drug administration, using high pressure liquid chromatography-tandem mass spectroscopy (LC/MS/MS), and the pharmacokinetic parameters were calculated. Then a two-stage partial least squares strategy was employed to screen potential biomarkers and predict the pharmacokinetic parameters of faropenem. The results showed a good prediction of AUC and Cmax with the screened biomarkers, and metabolites such as valine, proline, aspartic acid, gluconic acid, glucuronic acid, and 2-ketoisocaproic acid were indicated as candidate biomarkers. Finally, we explored the mechanism of individual variation by pathway enrichment analysis, and it suggested that organic anion transporter 1 (OAT1) and 3 (OAT3) might be responsible for individual variation of faropenem, and this hypothesis was verified by an experiment in rats.