Frontiers in medical technology最新文献

Introduction: Cervical cancer (CA cervix) ranks as the second most common cancer among women aged 15-44 and remains a leading cause of cancer-related mortality. Regular screening for cervical cancer significantly reduces mortality rates. Due to the strong causal relationship between high-risk human papillomavirus (hrHPV) and cervical cancer, HPV DNA testing has been developed as a screening method. HPV self-sampling kits have the potential to increase screening uptake, facilitate early detection, and reduce the global burden of cervical cancer. This study evaluates the efficacy of an in-house developed HPV CerviSens self-sampling kit for women in detecting hrHPV types.

Methodology: The study, approved by the Gupte Hospital Ethics Committee, included women aged 35-65 visiting Gupte Hospital in Pune, India. Participants self-collected vaginal samples using the in-house developed HPV CerviSens kit, and trained healthcare practitioners collected conventional samples. HPV DNA analysis was performed using the Cobas 4800 assay. Concordance between self-sampling and clinician sampling was assessed using Cohen's κ statistic. The sensitivity and specificity of HPV detection in self-samples were calculated with clinician-collected samples as the reference standard.

Results: A total of 203 paired self-collected and clinician-collected samples were analyzed for HPV detection. The median age of participants was 44 years. Concordance for HPV detection between self-samples and clinician-collected samples was very good (Cohen's κ: 0.88, 95% CI: κ ≥ 0.81). For HPV detection in self-samples, the in-house HPV CerviSens self-sampling kit demonstrated a sensitivity of 98.0% (95% CI: 89.4%-99.9%) and a specificity of 99.4% (95% CI: 96.3%-99.9%) when clinician-collected samples were used as the reference standard. These results demonstrate that the self-sampling method provides high accuracy in identifying high-risk HPV infections.

Conclusion: HPV self-sampling using the in-house developed HPV CerviSens kit is a reliable and effective method for cervical cancer screening, with high concordance and accuracy in detecting HPV infections. Integrating self-sampling into screening programs can enhance early detection, improve patient outcomes, and significantly reduce the global burden of cervical cancer.

Introduction: Oral mucositis (OM) is a prevalent complication of cancer treatment that causes painful erythematous and ulcerated lesions in oral mucosa. Current treatments lack efficacy, being natural compounds explored as alternatives. Chestnut shells (CS) are rich in (poly)phenols with antioxidant, anti-inflammatory, and antitumor properties. This study aims to develop orodispersible films (OFs) with CS extract as active ingredient to manage OM.

Methods: OFs, prepared by solvent casting and incorporating the CS extract, were characterized regarding physicochemical, antioxidant/antiradical, and anticancer properties, as well as bioactive compounds permeation through in vitro and ex vivo buccal models.

Results: OFs revealed excellent features: thickness (125 µm), tensile strength (43.05 MPa), elongation (75.28%), weight (19 mg/cm²), moisture content (4.25%), and disintegration time (20.43 min). Significant antioxidant/antiradical activities were observed (TPC = 37.05 mg GAE/g film; DPPH = 143.42 mg TE/g film; FRAP = 0.142 µmol FSE/g film). LC-ESI-LTQ-Orbitrap-HRMS analysis confirmed the high permeation of sebacic acid, epicatechin, isorhamnetin, protocatechuic acid, and L-tyrosine across both models, while cytotoxicity assays in oral epithelial cell lines (HSC3 and TR146) demonstrated the cytocompatibility.

Conclusion: CS-OFs offers a promising approach for preventing and treating OM, enhancing efficacy and patient comfort by delivering bioactive compounds directly to the oral mucosa.

This paper is a bibliometric analysis of the utilization of exosomes in hydrogels for various applications, conducted by assessing the relevant documents in this evolving field of research. Since targeted drug delivery, cell communication, and tissue regeneration are of high importance in the field of biomedicine and medical sciences, incorporating exosomes can offer a valuable addition in such applications due to their high bioactivity and biocompatibility. Applications include ocular drug delivery, boosting optic nerve damage, and disease detection, such as cancer and diabetes. Exosome-based technologies have been of interest since the mid-2000s, with an increased momentum in recent years. In this study, data were exported from the Web of Science and Scopus databases and plotted in order to identify the research trends and publication impact in such an evolving area. The analysis reveals that among several countries, China has the greatest number of publications within the period of 2015 to 9th-11th of May 2025, with a contribution of approximately 76.21% and 61.92% based on the WOS and Scopus databases, respectively. Both publications and citation trends show a significant increase with time, reflecting the increased interest in this field. This study aims to provide an overview of the current impact of research on utilizing exosomes in hydrogel systems.

Introduction: The primary objective of this study was to evaluate the efficiency and ergonomic benefits of a novel surgical lighting system developed within the SmartOT project. The developed system aims to automatically prevent shadows on the surgical field, eliminating the need for frequent manual adjustments, which is common with conventional surgical lights. Additionally, the study seeks to explore the feasibility of using EEG recordings as an objective method for assessing workplace strain in clinical settings, thereby laying the groundwork for future studies focused on reducing the workload of medical personnel.

Methods: To achieve these objectives, we conducted a passive Oddball experiment involving EEG measurements to assess the impact of the new lighting system on workplace strain. Participants performed a task requiring them to identify specific LEGO® pieces. The study involved 30 participants (13 females, 17 males), with errors being tracked as an additional measure of cognitive load. The experimental setup was informed by previous research, which established a method for objectively determining workload generated by AR and VR technologies in clinical settings. In that research, EEG signals were recorded during surgical planning under different conditions, revealing trends in cognitive load and validating the utility of EEG for workload assessment.

Results: The NASA Task Load Index (NASA-TLX) analysis revealed significantly lower mental demand, temporal demand, effort, and frustration scores for the smart surgical lamp compared to the manual lamp conditions, with mandatory and optional adjustments. However, there were no significant differences between the smart and conventional lamp in the dimensions of physical demand and performance. Similarly, EEG recordings indicated a higher P300 amplitude at electrode Fz following the smart lamp condition (p = 0.037), reflecting less cognitive load; latencies did not differ between conditions. Error analysis confirmed fewer errors and shorter processing times for the smart lamp.

Conclusions: The measurements of NASA-TLX and EEG after running simulated surgical tasks showed that the SmartOT prototype significantly reduced errors and workload compared to the conventional surgical lamp. These findings reflect the capability of smart surgical lighting in improving patient safety and efficiency within operating theaters.

Excessive pressure and shear forces on bedridden patients can lead to pressure injuries, particularly on those with existing ulcers. Monitoring pressure distribution is crucial for preventing such injuries by identifying high-risk areas. To address this challenge, we propose Attention Feature Network (AttnFnet), a self-attention-based deep neural network that generates pressure distribution maps from single-depth images using Conditional Generative Adversarial Network (cGAN) training. We introduce a mixed-domain SSIML2 loss function, combining structural similarity and pixel-level accuracy, along with adversarial loss, to enhance the prediction of pressure distributions for subjects lying in a bed. Evaluation results from the benchmark dataset demonstrate that the AttnFnet outperforms existing methods in terms of Structural Similarity Index Measure (SSIM) and quality analysis, providing accurate pressure distribution estimation from a single depth image.

Objective: This study investigates deep convolutional neural networks (CNNs) for automated detection of early to mid-stage Parkinson's disease (PD) from static facial images, aiming to explore non-invasive, cost-effective approaches for early diagnosis and remote monitoring.

Methods: 2,000 facial images were collected from PD patients and healthy controls, followed by data augmentation to expand the dataset to 6,000 images. After randomly dividing the dataset into training and test sets according to 8:2, five CNN architectures were fine-tuned and assessed. Model performance was assessed by accuracy, precision, recall, specificity, F1 score, and area under the ROC and PR curve (AUC). Grad-CAM visualization techniques were applied to identify the discriminative facial regions associated with PD.

Results: ResNet18 achieved the best overall performance, yielding an F1 score of 99.67% across metrics. MobileNetV3 also performed robustly, particularly excelling in recall (99.00%), suggesting its suitability for high-sensitivity screening applications. EfficientNetV2 demonstrated stable convergence and competitive classification performance (F1 score: 96.30%), while VGG16 exhibited balanced performance with rapid convergence. Inception-v4 showed relatively lower accuracy and greater variability, indicating a potential risk of overfitting. Grad-CAM heatmaps revealed that the most predictive facial regions across models were concentrated around the eyes, lips, and nose, consistent with PD-related hypomimia.

Conclusion: CNNs, particularly ResNet18 and MobileNetV3, exhibit significant potential for the automated identification of PD from facial imagery. These models offer promising avenues for developing scalable, non-invasive screening tools suitable for early detection and remote healthcare delivery, providing significant clinical and social value in the context of aging populations.

Bacteriocins are ribosomally produced, proteinaceous antimicrobial biomolecules with versatile functions and are considered potential next-generation therapeutics. They are secreted by a few groups of lactic acid bacteria (LAB) that possess the ability to combat spoilage and foodborne pathogens. Given these properties, bacteriocins have generated significant interest for their potential use as natural food preservatives. This study aimed to isolate and characterize bacteriocin-producing LAB with potent antimicrobial properties and evaluate their potential use as natural food preservatives and alternative therapeutics. A total of 47 morphologically distinct LAB isolates were screened for antibacterial activity against foodborne pathogens. The isolate exhibiting the strongest antimicrobial activity, designated CW40, was selected for further study. The bacteriocin was purified from the culture supernatant using gel filtration chromatography. The isolate was identified by 16S rDNA sequencing, and optimal conditions for bacteriocin production were determined. The molecular weight of the bacteriocin was estimated, and its antimicrobial spectrum, enzyme sensitivity, bile salt tolerance, and antibiotic resistance profile were assessed. Isolate CW40 produced 8 kDa (MW) of bacteriocin from the purified supernatant of its grown culture and was identified as Lactobacillus rhamnosus through 16S rDNA sequencing. The bacteriocin production of L. rhamnosus was optimized, with maximum yield observed at 37℃ with pH 7. The bacteriocin exhibited strong antimicrobial activity against Bacillus subtilis, Bacillus cereus, and Escherichia coli. Protease treatment eliminated antimicrobial activity, confirming the proteinaceous nature of the bacteriocin. Maximum bacteriocin activity at 4,098 AU/mL was observed against E. coli. The strain tolerated up to 0.3% (w/v) ox gall and demonstrated a broad antibiotic resistance. The results highlight Lactobacillus rhamnosus CW40 as a promising source of bacteriocins with potent antimicrobial properties. These findings support the potential application of CW40-derived bacteriocins as natural biopreservatives and adjunct therapeutic agents in combating foodborne pathogens and antibiotic-resistant infections.

The efficacy of the gypsum-licorice (SG-GC) pair was evaluated through its in vivo and in vitro anti-respiratory syncytial virus (RSV) activity. The results showed that SG-GC had significant efficacy against RSV infection in mice, which was close to that of the whole formula, and could significantly reduce the viral load in the lungs, improve the symptoms of lung injury, and reduce the inflammatory cell infiltration in the pathological sites. The decoction of single-flavored gypsum showed some anti-RSV efficacy, but not significant; when combined with sub-inhibitory concentrations of GA, it showed a significantly enhanced anti-RSV effect. The experimental results of this study suggest that gypsum may be a potential key antiviral substance, but its efficacy needs to be complemented by licorice; the significant enhancement of antiviral efficacy of SG-GC may be related to the formation of metal-organic supramolecules after the interaction of trace metal ions in gypsum and GA in licorice.

As equipment improves and technology advances, the application of Computed Tomography (CT) in clinical disease diagnosis has become increasingly widespread, particularly demonstrating significant advantages in diagnosing solid lesions. However, CT scans still face challenges, including insufficient sensitivity and an inability to assess renal function when diagnosing bilateral renal tuberculosis (BRTB). By reviewing relevant high-quality literature, we compared the sensitivity, specificity, advantages, and limitations of USG, KUB, IVU, MRI, PET-CT, and CT in the diagnosis of BRTB. CT offers higher clinical detection rates and reduces the economic burden on patients compared to other imaging methods, making it the preferred modality for imaging in patients with BRTB. AI-assisted diagnosis and the integration of CT with PET may represent promising future directions for CT imaging.

According to the National Library of Medicine (NLM), chronic kidney disease (CKD) affects more than 10% of the world's population, and peritoneal dialysis (PD) is one of the promising treatments. Despite the advantages of the current PD machine over alternative treatments, it has certain limitations, such as high consumable costs, lengthy daily sessions, and a lack of portability. This work aims to implement a finite state machine design to modify the process of an automated, economical PD system. The proposed optimized process includes a flush system stage, where tubes are rinsed before and after each session to avoid contamination. This design is intended for use by only one patient to prevent contamination. Furthermore, a turbidity sensor is added to measure the efficiency of the dialysis process and reduce the current dialysis time, which can reach eight hours. The finite state machine design is developed using LabVIEW software, featuring a user-friendly interface that allows users to track the process's progress.