-
Vaccines May 2024This study aimed to investigate the infection status of Omicron in the population and the association between COVID-19 vaccination and infection with Omicron.
OBJECTIVE
This study aimed to investigate the infection status of Omicron in the population and the association between COVID-19 vaccination and infection with Omicron.
METHODS
We conducted a cross-sectional study to openly recruit participants for a survey of SARS-CoV-2 infection by convenience sampling from 1 January to 15 January 2023 after a COVID-19 pandemic swept across China. Additionally, the binary logistic regression model was adopted to evaluate the association between COVID-19 vaccination and the infection outcomes or symptom severity, respectively. Meanwhile, the relations between the vaccination and duration of the symptoms were estimated via ordinal logistic analysis.
RESULTS
Of the 2007 participants, the prevalence of infection with Omicron was 82.9%. Compared with unvaccinated individuals, inactivated COVID-19 vaccination could increase the risk of Omicron infection (OR = 1.942, 95% CI: 1.093-3.448), and the receipt of at least one dose of non-inactivated COVID-19 vaccines was a protective factor against infection (OR = 0.428, 95% CI: 0.226-0.812). By contrast, no relations were observed in COVID-19 vaccination with the symptoms of infection and duration of symptoms ( > 0.05).
CONCLUSIONS
This cross-sectional study concluded that inactivated COVID-19 vaccination might increase the risk of Omicron infection, which should be a concern during COVID-19 vaccination and the treatment of variant infections in the future, and the receipt of at least one dose of non-inactivated COVID-19 vaccine was a protective factor against infection.
PubMed: 38932296
DOI: 10.3390/vaccines12060567 -
Viruses May 2024We have been encouraging practicing gynecologists to adopt molecular diagnostics tests, PCR, and cancer biomarkers, as alternatives enabled by these platforms, to...
We have been encouraging practicing gynecologists to adopt molecular diagnostics tests, PCR, and cancer biomarkers, as alternatives enabled by these platforms, to traditional Papanicolaou and colposcopy tests, respectively. An aliquot of liquid-based cytology was used for the molecular test [high-risk HPV types, (HR HPV)], another for the PAP test, and one more for p16/Ki67 dual-stain cytology. A total of 4499 laboratory samples were evaluated, and we found that 25.1% of low-grade samples and 47.9% of high-grade samples after PAP testing had a negative HR HPV-PCR result. In those cases, reported as Pap-negative, 22.1% had a positive HR HPV-PCR result. Dual staining with p16/Ki67 biomarkers in samples was positive for HR HPV, and 31.7% were also positive for these markers. Out of the PCR results that were positive for any of these HR HPV subtypes, n 68.3%, we did not find evidence for the presence of cancerous cells, highlighting the importance of performing dual staining with p16/Ki67 after PCR to avoid unnecessary colposcopies. The encountered challenges are a deep-rooted social reluctance in Mexico to abandon traditional Pap smears and the opinion of many specialists. Therefore, we still believe that colposcopy continues to be a preferred procedure over the dual-staining protocol.
Topics: Humans; Female; Mexico; Uterine Cervical Neoplasms; Papillomavirus Infections; Molecular Diagnostic Techniques; Papanicolaou Test; Biomarkers, Tumor; Papillomaviridae; Cyclin-Dependent Kinase Inhibitor p16; Vaginal Smears; Colposcopy; Gynecology; Adult; Middle Aged; Ki-67 Antigen; Polymerase Chain Reaction; Early Detection of Cancer; Private Practice
PubMed: 38932179
DOI: 10.3390/v16060887 -
Polymers Jun 2024Hydrogel-based devices commonly have a high demand for material durability when subjected to prolonged or cyclic loads. To extend their service life, it is crucial to...
Hydrogel-based devices commonly have a high demand for material durability when subjected to prolonged or cyclic loads. To extend their service life, it is crucial to have a deep understanding of the fatigue mechanisms of hydrogels. It is well-known that double-network (DN) hydrogels are characterized by high strength and toughness and are thus recognized as a promising candidate under load-bearing conditions. However, the existing studies in the literature mainly focus on their resistant capability to fatigue crack growth, while the underlying mechanisms of fatigue crack nucleation are still inconclusive. This work aims to bridge this knowledge gap by formulating a fatigue life predictor for DN hydrogels within the framework of configurational mechanics to elucidate the underlying mechanisms governing fatigue crack nucleation. The fatigue life predictor for DN hydrogels is derived from the configurational stress by incorporating the corresponding constitutive models and the thermodynamic evolution laws for microdamage mechanisms and material viscoelasticity. With the developed fatigue predictor, the effect of the microdamage mechanism on fatigue is elucidated, i.e., the internal damage of the sacrificial network can improve the fatigue life of DN hydrogels. The fatigue life predictor is also adopted to evaluate the effects of some other factors on the fatigue crack nucleation, such as the loading rate, pre-stretching treatment, and water diffusion, identifying feasible loading profiles that could improve material durability. Overall, the theoretical framework and the modeling results in this work are expected to shed light on unveiling the fatigue mechanisms of DN hydrogels and advance the design of hydrogel-based devices.
PubMed: 38932049
DOI: 10.3390/polym16121700 -
Pharmaceutics Jun 2024Novel antifungal drugs are urgently needed to treat candidiasis caused by the emerging fungal multidrug-resistant pathogen . In this study, the most cost-effective drug...
Novel antifungal drugs are urgently needed to treat candidiasis caused by the emerging fungal multidrug-resistant pathogen . In this study, the most cost-effective drug repurposing technology was adopted to identify an appropriate option among the 1615 clinically approved drugs with anti- activity. High-throughput virtual screening of 1,3-beta-glucanosyltransferase inhibitors was conducted, followed by an analysis of the stability of 1,3-beta-glucanosyltransferase drug complexes and 1,3-beta-glucanosyltransferase-dutasteride metabolite interactions and the confirmation of their activity in biofilm formation and planktonic growth. The analysis identified dutasteride, a drug with no prior antifungal indications, as a potential medication for anti- activity in seven clinical isolates from Saudi Arabian patients. Dutasteride was effective at inhibiting biofilm formation by while also causing a significant reduction in planktonic growth. Dutasteride treatment resulted in disruption of the cell membrane, the lysis of cells, and crushed surfaces on , and significant (-value = 0.0057) shrinkage in the length of was noted at 100,000×. In conclusion, the use of repurposed dutasteride with anti-. potential can enable rapid recovery in patients with difficult-to-treat candidiasis caused by and reduce the transmission of nosocomial infection.
PubMed: 38931930
DOI: 10.3390/pharmaceutics16060810 -
Sensors (Basel, Switzerland) Jun 2024Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments...
Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.
PubMed: 38931767
DOI: 10.3390/s24123984 -
Sensors (Basel, Switzerland) Jun 2024Telehealth and remote patient monitoring (RPM), in particular, have been through a massive surge of adoption since 2020. This initiative has proven potential for the... (Review)
Review
Telehealth and remote patient monitoring (RPM), in particular, have been through a massive surge of adoption since 2020. This initiative has proven potential for the patient and the healthcare provider in areas such as reductions in the cost of care. While home-use medical devices or wearables have been shown to be beneficial, a literature review illustrates challenges with the data generated, driven by limited device usability. This could lead to inaccurate data when an exam is completed without clinical supervision, with the consequence that incorrect data lead to improper treatment. Upon further analysis of the existing literature, the RPM Usability Impact model is introduced. The goal is to guide researchers and device manufacturers to increase the usability of wearable and home-use medical devices in the future. The importance of this model is highlighted when the user-centered design process is integrated, which is needed to develop these types of devices to provide the proper user experience.
Topics: Humans; Telemedicine; Monitoring, Physiologic; Wearable Electronic Devices
PubMed: 38931760
DOI: 10.3390/s24123977 -
Sensors (Basel, Switzerland) Jun 2024Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait... (Meta-Analysis)
Meta-Analysis Review
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
Topics: Humans; Parkinson Disease; Machine Learning; Gait Disorders, Neurologic; Gait; Wearable Electronic Devices; Algorithms; Quality of Life
PubMed: 38931743
DOI: 10.3390/s24123959 -
Sensors (Basel, Switzerland) Jun 2024In this paper, we propose a Transformer-based encoder architecture integrated with an unsupervised denoising method to learn meaningful and sparse representations of...
In this paper, we propose a Transformer-based encoder architecture integrated with an unsupervised denoising method to learn meaningful and sparse representations of vibration signals without the need for data transformation or pre-trained data. Existing Transformer models often require transformed data or extensive computational resources, limiting their practical adoption. We propose a simple yet competitive modification of the Transformer model, integrating a trainable noise reduction method specifically tailored for failure mode classification using vibration data directly in the time domain without converting them into other domains or images. Furthermore, we present the key architectural components and algorithms underlying our model, emphasizing interpretability and trustworthiness. Our model is trained and validated using two benchmark datasets: the IMS dataset (four failure modes) and the CWRU dataset (four and ten failure modes). Notably, our model performs competitively, especially when using an unbalanced test set and a lightweight architecture.
PubMed: 38931737
DOI: 10.3390/s24123953 -
Sensors (Basel, Switzerland) Jun 2024Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the...
Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed.
Topics: Humans; Biomechanical Phenomena; Electromyography; Movement; Muscle, Skeletal
PubMed: 38931719
DOI: 10.3390/s24123934 -
Sensors (Basel, Switzerland) Jun 2024Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high...
Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments.
PubMed: 38931711
DOI: 10.3390/s24123927