Protein aggregation is challenging for biopharmaceutical drug, because it affects the stability of protein formulations in real-time. However, current techniques for protein aggregate indication meet a number of limitations including limited aggregate size range, complex pre-treatments and lack of chromatographic approaches. Herein, a rapid, automatic, non-invasive and wide-scale coverage technique for aggregates indication is developed to overcome these challenges. Firstly, the response of low-field nuclear magnetic resonance (LF-NMR) to the aggregates is explored by making a comparison with certain established techniques. LF-NMR achieves a high sensitivity of water proton transverse relaxation rate (R2 of H2O, hereinafter referred as R2(H2O)) to protein aggregates from nanometer to micrometer. Then, the quantitative relationship between R2(H2O) and aggregates is investigated furtherly. R2(H2O) could serve as an all-size coverage protein aggregates indicator during development. As a non-invasive method, LF-NMR does not need any sample handling. It takes only 44 s for one test, and saves a lot of manpower, materials and costs. Compared with other established analytical techniques, the technique developed here could be a powerful tool for a rapid, automatic, non-invasive and wide-scale coverage technique for aggregates indication in biomacromolecule development.

To enhance personalized diabetes management, there is a critical need for non-invasive wearable electrochemical sensors made from flexible materials to enable continuous monitoring of sweat glucose levels. The main challenge lies in developing glucose sensors with superior electrochemical characteristics and high adaptability. Herein, we present a wearable sensor for non-enzymatic electrochemical glucose analysis. The sensor was synthesized using hydrothermal and one-pot preparation methods, incorporating gold nanoparticles (AuNPs) functionalized onto aminated multi-walled carbon nanotubes (AMWCNTs) as an efficient catalyst, and crosslinked with carboxylated styrene butadiene rubber (XSBR) and PEDOT:PSS. The sensors were then integrated onto screen-printed electrodes (SPEs) to create flexible glucose sensors (XSBR-PEDOT:PSS-AMWCNTs/AuNPs/SPE). Operating under neutral conditions, the sensor exhibits a linear range of 50 μmol/L to 600 μmol/L, with a limit of detection limit of 3.2 μmol/L (S/N = 3), enabling the detection of minute glucose concentrations. The flexible glucose sensor maintains functionality after 500 repetitions of bending at a 180° angle, without significant degradation in performance. Furthermore, the sensor exhibits exceptional stability, repeatability, and resistance to interference. Importantly, we successfully monitored changes in sweat glucose levels by applying screen-printed electrodes to human skin, with results consistent with normal physiological blood glucose fluctuations. This study details the fabrication of a wearable sensor characterized by ease of manufacture, remarkable flexibility, high sensitivity, and adaptability for non-invasive blood glucose monitoring through non-enzymatic electrochemical analysis. Thus, this streamlined fabrication process presents a novel approach for non-invasive, real-time blood glucose level monitoring.

BACKGROUND: Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.
METHODS: First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.
RESULTS: The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.
CONCLUSIONS: These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

This study evaluates the non-invasive diagnosis of Invasive Aspergillosis Pneumonia (IPA) in mechanically ventilated patients by measuring galactomannan (GM) in exhaled breath condensate (EBC). Utilizing a rat model and a novel EBC collection device, we compared GM levels in bronchoalveolar lavage fluid (BALF) and EBC, supplemented by cytokine profiling. Analysis of 75 patients confirmed the device\'s efficacy, with EBC-GM and BALF-GM showing high diagnostic accuracy (AUC = 0.88). The threshold of 0.235 ng/ml for EBC-GM achieved 92.8 % sensitivity and 66.7 % specificity, with a strong correlation (r = 0.707, P < 0.001) with BALF-GM. This approach offers a safe, effective alternative to invasive diagnostics, enhancing precision with IL-6 and TNF-α measurements. The number registered on clinicaltrails.gov is NCT06333379.

暂无摘要。

Alternating electric fields (AEFs) at intermediate frequencies (100-300 kHz) and low intensities (1-3 V/cm) have shown promise as an effective approach for inhibiting cancer cell proliferation. However, a noticeable research gap exists in comparing the biophysical properties of invasive and non-invasive AEFs methods, and AEFs delivery strategies require further improvement. In this study, we constructed a realistic head model to simulate the effects of non-invasive and invasive AEFs on malignant gliomas. Additionally, a novel method was proposed involving the placement of a return electrode under the scalp. We simulated the electric field and temperature distributions in the brain tissue for each method. Our results underscore the advantages of invasive AEFs, showcasing their superior tumor-targeting abilities and reduced energy requirements. The analysis of brain tissue temperature changes reveals that non-invasive AEFs primarily generate heat at the scalp level, whereas invasive methods localize heat production within the tumor itself, thereby preserving surrounding healthy brain tissue. Our proposed invasive AEFs method also shows potential for selective electric field intervention. In conclusion, invasive AEFs demonstrate potential for precise and effective tumor treatment. Its enhanced targeting capabilities and limited impact on healthy tissue make it a promising avenue for further research in the realm of cancer treatment.

This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC).
This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs).
A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status.
Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

OBJECTIVE: To investigate the relative effectiveness of various Non-Invasive Treatment Techniques (NITs) in chronic tinnitus management.
METHODS: We searched PubMed, Embase and Cochrane Library databases from the time of data construction to December 31, 2022. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, NITs were evaluated, including Aacceptance and commitment therapy (A), Cognitive behavioral therapy (C), Sound therapy (S), Transcranial magnetic stimulation (T), Electrical stimulation therapy (E), Virtual reality therapy (V), tinnitus Retraining therapy (R), general psychotherapy (D), and Placebo (P). The outcome indicators included the Tinnitus Handicap Inventory (THI), Tinnitus Questionnaire (TQ), Hospital Anxiety and Depression Scale-anxiety-Depression (HADS-D), Insomnia Severity Index (ISI), Visual Analogue Scales-Loudness (VAS-L), and Visual Analogue Scales-Distress (VAS-D). Statistical analysis was performed using Stata 14.0 for NMA.
RESULTS: This systematic review and meta-analysis included 22 randomized controlled trials comprising 2,354 patients. The treatment effects varied on each scale. For THI, S (86.9%) was the most effective, whereas P (6.5%) was the worst. For TQ, C (89.5%) was the most effective, whereas D (25.4%) was the worst. For HADS-D, A (82.4%) was the most effective, whereas D (9.47%) was the worst. For ISI, A (83.2%) was the most effective, whereas R (20.6%) was the worst. For VAS-L, S (73.5%) was the most effective, whereas E (18.9%) was the worst. For VAS-D, C (84.7%) was the most effective, whereas P (18.1%) was the worst.
CONCLUSIONS: The combination of acoustics and cognitive behavioral therapy may be an effectively treat patients with chronic tinnitus.
METHODS: How common is the problem? Level 2. Is this diagnostic or monitoring test accurate? (Diagnosis) Level 1. What will happen if we do not add a therapy? (Prognosis) Level 1. Does this intervention help? (Treatment Benefits) Level 1. What are the COMMON harms? (Treatment Harms) Level 1. What are the RARE harms? (Treatment Harms) Level 1. Is this (early detection) test worthwhile? (Screening) Level 1I.

Sweat is an accessible biofluid that provides useful physiological information about the body\'s biomolecular state and systemic health. Wearable sensors possess various advantageous features, such as lightweight design, wireless connectivity, and compatibility with human skin, that make them suitable for continuous monitoring. Wearable electrochemical sweat sensors can diagnose diseases and monitor health conditions by detecting biomedical signal changes in sweat. This paper discusses the state-of-the-art research in the field of wearable sweat sensors and the materials used in their construction. It covers biomarkers present in sweat, sensing modalities, techniques for sweat collection, and ways to power these sensors. Innovative materials are categorized into three subcategories: sweat collection, sweat detection, and self-powering. These include substrates for sensor fabrication, analyte detection electrodes, absorbent patches, microfluidic devices, and self-powered devices. This paper concludes by forecasting future research trends and prospects in material-based wearable non-invasive sweat sensors.

Kidney transplantation is the preferred treatment for pediatric end-stage renal disease. However, pediatric recipients face unique challenges due to their prolonged need for kidney function to accommodate growth and development. The continual changes in the immune microenvironment during childhood development and the heightened risk of complications from long-term use of immunosuppressive drugs. The overwhelming majority of children may require more than one kidney transplant in their lifetime. Acute rejection (AR) stands as the primary cause of kidney transplant failure in children. While pathologic biopsy remains the \"gold standard\" for diagnosing renal rejection, its invasive nature raises concerns regarding potential functional impairment and the psychological impact on children due to repeated procedures. In this review, we outline the current research status of novel biomarkers associated with AR in urine and blood after pediatric kidney transplantation. These biomarkers exhibit superior diagnostic and prognostic performance compared to conventional ones, with the added advantages of being less invasive and highly reproducible for long-term graft monitoring. We also integrate the limitations of these novel biomarkers and propose a refined monitoring model to optimize the management of AR in pediatric kidney transplantation.