Adjusting the catalytic activity of nanozymes for enhanced oncotherapy has attracted significant interest. However, it remains challenging to engineer regulatory tactics with a minimal impact on normal tissues. By exploiting the advantages of energy storage, photostimulated, and long afterglow luminescence of persistent nanoparticles (PLNPs), a persistent luminescence-based nanoreservoir was produced to improve its catalytic activity for benign oncotherapy. In the study, PLNPs in a nanoreservoir with the ability to store photons served as a self-illuminant to promote its peroxidase-like activity and therapeutic efficacy by persistently motivating its photothermal effect before and after external irradiation ceased. The photostimulated and persistent luminescence of PLNPs and spatiotemporal controllability of exogenous light jointly alleviated adverse effects induced by prolonged irradiation and elevated the catalytic capability of the nanoreservoir. Ultimately, the system fulfilled benign photothermal-intensive nanozymatic therapy. This work provides new insights into boosting the catalytic activity of nanozymes for secure disease treatment.

Low back pain (LBP) is the leading cause of disability worldwide, yet its quantitative and noninvasive assessment remains challenging. Considering that near-infrared spectroscopy (NIRS) became a promising noninvasive tool for monitoring muscle and cupping therapy could regulate muscle blood flow to relieve LBP, we attempted to incorporate cupping and hemodynamics monitoring in muscle tissue by NIRS to assess LBP. We collected 3-min NIRS recordings on 12 LBP patients and 12 healthy subjects before and after 20-min cupping. Initially, no significant hemodynamic differences were observed between the groups. After cupping, the concentration changes of oxy-hemoglobin (Δ[HbO2]) in the emitter-detector channel parallel to spine unexpectedly exhibited that LBP was remarkably lower by approximately 67% compared with the controls. This study highlighted the potential of combining NIRS and cupping protocol as a quantitative assessment technique for LBP, also providing a new idea for clinical integration of novel optical assessment technologies.

暂无摘要。

Low-intensity focused ultrasound represents groundbreaking medical advancements, characterized by its noninvasive feature, safety, precision, and broad neuromodulatory capabilities. This technology operates through mechanisms, for example, acoustic radiation force, cavitation, and thermal effects. Notably, with the evolution of medical technology, ultrasound neuromodulation has been gradually applied in treating central nervous system diseases, especially stroke. Furthermore, burgeoning research areas such as sonogenetics and nanotechnology show promising potential. Despite the benefit of low-intensity focused ultrasound the precise biophysical mechanism of ultrasound neuromodulation still need further exploration. This review discusses the recent and ongoing developments of low-intensity focused ultrasound for neurological regulation, covering the underlying rationale to current utility and the challenges that impede its further development and broader adoption of this promising alternative to noninvasive therapy.

BACKGROUND: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the \'suspicious\' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients\' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet \'suspicious\' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling.
METHODS: An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model.
RESULTS: Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group.
CONCLUSIONS: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.

This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML - particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors - in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components - including the blue-green-red, multiple, and spatial attentions - in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.

Mid-infrared spectral analysis of glucose in subcutaneous interstitial fluid has been widely employed as a noninvasive alternative to the standard blood-glucose detection requiring blood-sampling via skin-puncturing, but improving the confidence level of such a replacement remains highly desirable. Here, we show that with an innovative metric of attributes in measurements and data-management, a high accuracy in correlating the test results of our improved spectral analysis to those of the standard detection is accomplished. First, our comparative laser speckle contrast imaging of subcutaneous interstitial fluid in fingertips, thenar and hypothenar reveal that spectral measurements from hypothenar, with an attenuated total reflection Fourier transform infrared spectrometer, give much stronger signals than the stereotype measurements from fingertips. Second, we demonstrate that discriminative selection of the spectral locations and ranges, to minimize spectral interference and maximize signal-to-noise, are critically important. The optimal band is pinned at that between 1000 ± 3 cm-1 and1040 ± 3 cm-1. Third, we propose an individual exclusive prediction model by adopting the support vector regression analysis of the spectral data from four subjects. The average predicted coefficient of determination, root mean square error and mean absolute error of four subjects are 0.97, 0.21 mmol/L, 0.17 mmol/L, respectively, and the average probability of being in Zone A of the Clark error grid is 100.00 %. Additionally, we demonstrate with the Bland and Altman plot that our proposed model has the highest consistency with portable blood glucose meter detection method.

Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model\'s efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.

[This corrects the article DOI: 10.3389/fmed.2024.1354363.].

BACKGROUND: Drug-induced liver injury (DILI) is one of the most common adverse events of medication use, and its incidence is increasing. However, early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests.
OBJECTIVE: To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.
METHODS: Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.
RESULTS: We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1).
CONCLUSIONS: Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.