Quadrivalent HPV vaccine effectiveness against cervical intraepithelial lesion grade 2 or worse in Norway: A registry-based study of 0,9 million Norwegian women.

In Norway, single cohort vaccination with quadrivalent HPV (qHPV) vaccine targeting 12-year-old girls took place from 2009-2016. In 2020, the oldest vaccinated cohort was 23 years old and had approached the age where risk of being diagnosed with cervical intraepithelial lesion grade 2 or worse (CIN2+) increases rapidly. The aim of this cohort study was to assess direct qHPV vaccine effectiveness (VE) against CIN2+ among Norwegian women aged 16-30 in 2007-2020. By using population-based health registries and individual-level data on vaccination status and potential subsequent CIN2+ incidence, we found 82% qHPV VE among women vaccinated before the age of 17.

Pros and cons of artificial intelligence implementation in diagnostic pathology.

The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.

Graph neural networks for clinical risk prediction based on electronic health records: A survey.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis.

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

Bibliometric insights into microalgae cultivation in wastewater: Trends and future prospects for biolipid production and environmental sustainability.

Cultivation of microalgae in wastewater stream has been extensively reported, especially for simultaneous production of biolipid and wastewater treatment process. This study aimed to derive the research trend and focus on biolipid production from microalgae cultivated in wastewater by using bibliometric approach. The search strategy used in Scopus database resulted in 1339 research articles from 1990 to November 2023. Majority of publications (46%) were affiliated to China and India, showing their predominance in this field. Keywords related to the center of attention included biodiesel, biofuel, biomass and nutrient removal. Meanwhile, keyword with recent publication year, indicating the emerging research trends, revolved around the cultivation techniques and application of the system. Co-culture involving more than one microalgae species, bacteria and yeast showed promising results, while addition of nanoparticles was also found to be beneficial. Increasing exploration on the application of microalgae for treatment of saline wastewater was also reported and the carbon fixation mechanism by microalgae has been widely investigated to promote less environmental impact. Future research on these topics were suggested based on the findings of the bibliometric analyses.

A 70% Ethanol Neorhodomela munita Extract Attenuates RANKL-Induced Osteoclast Activation and H2O2-Induced Osteoblast Apoptosis In Vitro.

The rapid aging of the population worldwide presents a significant social and economic challenge, particularly due to osteoporotic fractures, primarily resulting from an imbalance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation. While conventional therapies offer benefits, they also present limitations and a range of adverse effects. This study explores the protective impact of Neorhodomela munita ethanol extract (EN) on osteoporosis by modulating critical pathways in osteoclastogenesis and apoptosis. Raw264.7 cells and Saos-2 cells were used for in vitro osteoclast and osteoblast models, respectively. By utilizing various in vitro methods to detect osteoclast differentiation/activation and osteoblast death, it was demonstrated that the EN's potential to inhibit RANKL induced osteoclast formation and activation by targeting the MAPKs-NFATc1/c-Fos pathway and reducing H2O2-induced cell death through the downregulation of apoptotic signals. This study highlights the potential benefits of EN for osteoporosis and suggests that EN is a promising natural alternative to traditional treatments.

Overcoming challenges in nursing disaster preparedness and response: an umbrella review.

Disaster nursing plays a vital role in addressing the health needs of vulnerable populations affected by large scale emergencies. However, disaster nursing faces numerous challenges, including preparedness, logistics, education, ethics, recovery and legalities. To enhance healthcare system effectiveness during crises, it is essential to overcome these issues. This umbrella review, conducted using the Joanna Briggs Institute (JBI) methodology, synthesizes data from 24 studies to identify key strategies for improving disaster nursing. The review highlights nine key themes: Education and Training, Research and Development, Policy and Organizational Support, Technological Advancements, Psychological Preparedness and Support, Assessment and Evaluation, Role-Specific Preparedness, Interprofessional Collaboration and Cultural Competence, and Ethics and Decision-Making. The review emphasizes the importance of education, technological advancements, psychological support, and interprofessional collaboration in bolstering disaster nursing preparedness and response efforts. These elements are crucial for enhancing patient outcomes during emergencies and contributing to a more resilient healthcare system. This comprehensive analysis provides valuable insights into the various aspects essential for enhancing disaster nursing. By implementing evidence-based strategies within these nine themes, the nursing profession can enhance its capacity to effectively manage and respond to the complex needs of disaster-affected populations, ultimately improving patient care and outcomes during emergencies.

Barriers and Facilitators to Youth Decision-Making Involvement in Families Referred for Stem Cell Transplant.

Youth prefer to be involved in treatment decisions, yet youth participation is minimally present in decisions like stem cell transplant (SCT) that require frequent medications and social isolation to be successful in curing cancer and chronic illness. The purpose of our study is to identify the barriers and facilitators to youth decision-making involvement in the youth-parent interaction when referred for treatment with SCT. We report qualitative findings from our theory-driven mixed-methods study. We thematically analyzed our field notes of youth and parent observations and audio-recordings during SCT consultations and semi-structured interviews. Data were collected from 10 youth, 8 to 16 (median 12) years of age, and their parents (n = 20). Three themes emerged: (a) Reluctant unless motivated, (b) Uncertain but capable, and (c) Limited unless supported. Our findings emphasize the critical role parents may take in facilitating youth involvement in decisions.

Extracellular Vesicles from Mycoplasma gallisepticum: Modulators of Macrophage Activation and Virulence.

Extracellular vesicles (EVs) mediate intercellular communication by transporting proteins. To investigate the pathogenesis of Mycoplasma gallisepticum (MG), a major threat to the poultry industry, we isolated and characterized MG-produced EVs. Our study highlights the significant impact of MG-derived EVs on immune function and macrophage apoptosis, setting them apart from other MG metabolites. These EVs dose-dependently enhance MG adhesion and proliferation, simultaneously modulating TLR2 and IFN-γ pathways, thereby inhibiting macrophage activation. A comprehensive protein analysis revealed 117 proteins in MG-derived EVs, including established virulence factors such as GapA, CrmA, VlhA, and CrmB. Crucially, these EV-associated proteins significantly contribute to MG infection. Our findings advance our comprehension of MG pathogenesis, offering insights for preventive strategies, and emphasize the pivotal role of MG-derived EVs and their associated proteins. This research sheds light on the composition and crucial role of MG-derived EVs in MG pathogenesis, aiding our fight against MG infections.

Analysis of human ABO Blood Group as a risk factor with knee osteoarthritis at tertiary care hospital in Pakistan.

Objective: To investigate the association and risk estimation of ABO blood group distribution and clinical attributes in patients with Knee Osteoarthritis. Method: This was a hospital-based study conducted at, Liaquat University Hospital Hyderabad from December, 2019 to December, 2022 to investigate this least researched area of highly prevalent musculoskeletal disease in Pakistan. Non-Probability Convenience Sampling was used for selecting 190 cases of confirm Knee Osteoarthritis patients diagnosed by Orthopedic surgeon based on standard clinical and radiographic criteria. Data were analyzed using IBM-SPSS version 23.0. Percentages and frequencies were counted for categorical data. Pearson Chi Square test and fisher's exact test were used to check the association and Multinomial Logistic Regression was used to estimate the risk for moderate and severe kellgren grading Knee Osteoarthritis (KOA) cases with ABO blood grouping in comparison of mild Kellgren grading. Results: A total of 190 cases of Knee Osteoarthritis (KOA) were included in the study. Females (61.6%) and patients with age 50 and above were 40.5 % were found in greater proportion. Majority (41.6%) were classified radiologically as mild cases with O group (39.5%) and positive Rh antigen (95.8%). Strong association (p = <0.01) was found between gender, age group and ABO blood group with KOA radiological Kellgren and Lawrence score. Conclusion: There is strong relation in between radiological grading of knee osteoarthritis severity and A blood group, gender and age.

Advances in Ordered Architecture Design of Composite Solid Electrolytes for Solid-State Lithium Batteries.

Solid electrolyte lithium batteries are the next generation of advanced energy devices. The incorporation of solid electrolytes can significantly improve the safety issue of lithium-ion batteries. Organic-inorganic composite solid electrolytes (CSE) are promising candidates for solid-state batteries, but their application is mainly limited by low ionic conductivity. Many studies have shown that the architecture of ordered inorganic fillers in CSE can act as fast lithium-ion transfer channels by auxiliary means, thus significantly improving the ionic conductivities. This review summarises the recent advances in CSE with different dimensional inorganic fillers. Various effective strategies for the construction of ordered structures in CSE are then presented. The review concludes with an outlook on the future development of CSE. This review aims to provide researchers with an in-depth understanding of how to achieve ordered architectures in CSE for advanced solid state lithium batteries.

Research process, recap, and prediction of Chiari malformation based on bicentennial history of nomenclature and terms misuse.

There is an absent systematic analysis or review that has been conducted to clarify the topic of nomenclature history and terms misuse about Chiari malformations (CMs). We reviewed all reports on terms coined for CMs for rational use and provided their etymology and future development. All literature on the nomenclature of CMs was retrieved and extracted into core terms. Subsequently, keyword analysis, preceding and predicting (2023-2025) compound annual growth rate (CAGR) of each core term, was calculated using a mathematical formula and autoregressive integrated moving average model in Python. Totally 64,527 CM term usage was identified. Of these, 57 original terms were collected and then extracted into 24 core-terms. Seventeen terms have their own featured author keywords, while seven terms are homologous. The preceding CAGR of 24 terms showed significant growth in use for 18 terms, while 13, three, three, and five terms may show sustained growth, remain stable, decline, and rare in usage, respectively, in the future. Previously, owing to intricate nomenclature, Chiari terms were frequently misused, and numerous seemingly novel but worthless even improper terms have emerged. For a very basic neuropathological phenomenon tonsillar herniation by multiple etiology, a mechanism-based nosology seems to be more conducive to future communication than an umbrella eponym. However, a good nomenclature also should encapsulate all characteristics of this condition, but this is lacking in current CM research, as the pathophysiological mechanisms are not elucidated for the majority of CMs.

FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks.

Keyword spotting (KWS) systems are used for human-machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device. The design achieves significant area efficiency by redundantly utilizing bitwise operators in the computation of the binarized neural network (BNN) and ternary neural network (TNN). In a complementary metal-oxide semiconductor (CMOS) 40 nm process environment, the DS-BTNN accelerator demonstrated significant efficiency. Compared with a design approach where BNN and TNN were independently developed and subsequently integrated as two separate modules into the system, our method achieved a 49.3% area reduction while yielding an area of 0.558 mm2. The designed KWS system, which was implemented on a Xilinx UltraScale+ ZCU104 field-programmable gate array (FPGA) board, receives real-time data from the microphone, preprocesses them into a mel spectrogram, and uses this as input to the classifier. Depending on the order, the network operates as a BNN or a TNN for WUW recognition and command classification, respectively. Operating at 170 MHz, our system achieved 97.1% accuracy in BNN-based WUW recognition and 90.5% in TNN-based command classification.

Natural-Language-Driven Multimodal Representation Learning for Audio-Visual Scene-Aware Dialog System.

With the development of multimedia systems in wireless environments, the rising need for artificial intelligence is to design a system that can properly communicate with humans with a comprehensive understanding of various types of information in a human-like manner. Therefore, this paper addresses an audio-visual scene-aware dialog system that can communicate with users about audio-visual scenes. It is essential to understand not only visual and textual information but also audio information in a comprehensive way. Despite the substantial progress in multimodal representation learning with language and visual modalities, there are still two caveats: ineffective use of auditory information and the lack of interpretability of the deep learning systems' reasoning. To address these issues, we propose a novel audio-visual scene-aware dialog system that utilizes a set of explicit information from each modality as a form of natural language, which can be fused into a language model in a natural way. It leverages a transformer-based decoder to generate a coherent and correct response based on multimodal knowledge in a multitask learning setting. In addition, we also address the way of interpreting the model with a response-driven temporal moment localization method to verify how the system generates the response. The system itself provides the user with the evidence referred to in the system response process as a form of the timestamp of the scene. We show the superiority of the proposed model in all quantitative and qualitative measurements compared to the baseline. In particular, the proposed model achieved robust performance even in environments using all three modalities, including audio. We also conducted extensive experiments to investigate the proposed model. In addition, we obtained state-of-the-art performance in the system response reasoning task.

Novel Speech Recognition Systems Applied to Forensics within Child Exploitation: Wav2vec2.0 vs. Whisper.

The growth in online child exploitation material is a significant challenge for European Law Enforcement Agencies (LEAs). One of the most important sources of such online information corresponds to audio material that needs to be analyzed to find evidence in a timely and practical manner. That is why LEAs require a next-generation AI-powered platform to process audio data from online sources. We propose the use of speech recognition and keyword spotting to transcribe audiovisual data and to detect the presence of keywords related to child abuse. The considered models are based on two of the most accurate neural-based architectures to date: Wav2vec2.0 and Whisper. The systems were tested under an extensive set of scenarios in different languages. Additionally, keeping in mind that obtaining data from LEAs are very sensitive, we explore the use of federated learning to provide more robust systems for the addressed application, while maintaining the privacy of the data from LEAs. The considered models achieved a word error rate between 11% and 25%, depending on the language. In addition, the systems are able to recognize a set of spotted words with true-positive rates between 82% and 98%, depending on the language. Finally, federated learning strategies show that they can maintain and even improve the performance of the systems when compared to centralized trained models. The proposed systems set the basis for an AI-powered platform for automatic analysis of audio in the context of forensic applications of child abuse. The use of federated learning is also promising for the addressed scenario, where data privacy is an important issue to be managed.

Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control.

Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces such as joysticks that tend to be unintuitive and cumbersome to use. In contrast, vocal commands may represent a viable and intuitive alternative. This work represents an important step toward providing a viable vocal interface for people living with upper limb disabilities by proposing a novel lightweight vocal command recognition system. The proposed model leverages the MobileNet2 architecture, augmenting it with a novel approach to the self-attention mechanism, achieving a new state-of-the-art performance for Keyword Spotting (KWS) on the Google Speech Commands Dataset (GSCD). Moreover, this work presents a new dataset, referred to as the French Speech Commands Dataset (FSCD), comprising 4963 vocal command utterances. Using the GSCD as the source, we used Transfer Learning (TL) to adapt the model to this cross-language task. TL has been shown to significantly improve the model performance on the FSCD. The viability of the proposed approach is further demonstrated through real-life control of a robotic arm by four healthy participants using both the proposed vocal interface and a joystick.

A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5.

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement.

Themes in Health Sciences Librarianship Literature, 2016-2020: A Keyword and Subject Analysis.

Previous investigations into trends in Library and Information Science literature have revealed changes in the topics librarians publish on over time, with older studies highlighting classification and indexing, and information retrieval and more recent studies highlighting keywords such as Internet, information technology, digital libraries, and again, information retrieval. No similar investigation has been conducted on current publication trends by health sciences librarians. This study analyzes the top themes on which health sciences librarians published from 2016 to 2020 by examining the frequency of keywords. Keywords and subject headings were analyzed from The Journal of the Medical Library Association, Medical References Services Quarterly, The Journal of Hospital Librarianship, and The Journal of Electronic Resources in Medical Libraries. A total of 8,806 keywords were downloaded for analysis and organized into 292 categories during taxonomy creation. The ten most frequent themes were: libraries, information, education, humans, demography, librarian, geographical locations, research, electronic resources, and technology. The study also found that data, psychiatry and psychology, informatics, and publishing were other key themes, indicating that health sciences librarians are publishing on a wide range of topics. Some keywords that appeared only once, such as telecommuting and flexible staffing, suggest emerging areas of research for librarians.

Bibliometric Analysis and Visualization of the Extrahepatic Portal Venous Obstruction Publication Landscape.

Introduction: A scientometric analysis was conducted to characterize the global research publications in extrahepatic portal venous obstruction (EHPVO), and state-of-the-art visualization graphics were generated to provide insight into specific bibliometric variables. Materials and Methods: The Web of Science database was accessed for research productivity and bibliometric variables of countries, institutions, authors, journals, and content analysis of top-20 cited documents were performed. Collaborative networks and co-occurrence of keywords map were generated using VOSviewer software. Results: Two hundred and sixteen records were retrieved with an annual growth rate of 2.53%. India is the leading country in productivity (n = 4339), followed by the USA and China. Post Graduate Institute of Medical Education and Research, Chandigarh, was the top productive institute. Sarin SK was the most prolific author, having the highest citations received and h-index. The hotspot topics were "portal hypertension," "cirrhosis," "children," "biliopathy/cholangiopathy," "liver fibrosis," and "liver transplantation" as per keyword co-occurrence networking. J Gastroenterol Hepatol had the most publications of EHPVO research as well the h-index. Regarding collaborative network mapping, the USA and Primignani M were the significant nodes among country and author, respectively. Conclusion: EHPVO research publication volume is low but is gradually progressing with dominant contributions from Indian institutes and authors. Most highly cited articles are of low level of evidence, and multi-institutional collaborative research can be the way forward.

Enhanced short-wavelength sensitivity in the blue-tongued skink Tiliqua rugosa.

Despite lizards using a wide range of colour signals, the limited variation in photoreceptor spectral sensitivities across lizards suggests only weak selection for species-specific, spectral tuning of photoreceptors. Some species, however, have enhanced short-wavelength sensitivity, which probably helps with the detection of signals rich in ultraviolet and short wavelengths. In this study, we examined the visual system of Tiliqua rugosa, which has an ultraviolet/blue tongue, to gain insight into this species' visual ecology. We used electroretinograms, opsin sequencing and immunohistochemical labelling to characterize whole-eye spectral sensitivity and the elements that shape it. Our findings reveal that T. rugosa expresses all five opsins typically found in lizards (SWS1, SWS2, RH1, RH2 and LWS) but possesses greatly enhanced short-wavelength sensitivity compared with other diurnal lizards. This enhanced short-wavelength sensitivity is characterized by a broadening of the spectral sensitivity curve of the eye towards shorter wavelengths while the peak sensitivity of the eye at longer wavelengths (560 nm) remains similar to that of other diurnal lizards. While an increased abundance of SWS1 photoreceptors is thought to mediate elevated ultraviolet sensitivity in a couple of other lizard species, SWS1 photoreceptor abundance remains low in this species. Instead, our findings suggest that short-wavelength sensitivity is driven by multiple factors which include a potentially red-shifted SWS1 photoreceptor and the absence of short-wavelength-absorbing oil droplets. Examining the coincidence of enhanced short-wavelength sensitivity with blue tongues among lizards of this genus will provide further insight into the co-evolution of conspecific signals and whole-eye spectral sensitivity.