Diagnostic Technologies Research Articles

Informatization of the professional training of future teachers of physical education in the conditions of distance education

The article shows the importance of the use of new information technologies in the process of professional training of future teachers of physical education at the current stage of the development of society in the conditions of distance learning. The main directions and tasks regarding the use of info-communication and multimedia learning tools in the professional training of students from the specialty 014 Secondary education are defined. (physical culture). The educational component "sports-pedagogical informatics" was introduced into the educational process, the curriculum and diagnostic technology for assessing success according to the modular rating system for this course were developed. It has been established that the implementation of such innovative tasks in full will make it possible to significantly optimize the professional training of future specialists in the conditions of distance learning and to quickly integrate into the European educational space. In addition, the main stages of the introduction of computer and multimedia tools into the educational process of future teachers of physical culture are determined. The justified need to introduce the educational component "Sports and pedagogical informatics" into the educational process of students from the specialty 014 Secondary education (physical culture). Carrying out such innovative tasks in full will make it possible to significantly optimize the professional training of students in the specialty 014 Secondary education (physical education) in the conditions of distance learning.

Frontiers of myopia research in the 21st century: A bibliometric analysis of the top 100 most influential articles in the field.

Myopia is the most common refractive error worldwide, contributing not only to visual impairment but also serving as a potential risk factor for various severe ocular diseases. Its impact on patients' quality of life and health is significant and imposes substantial socioeconomic burdens. In this study, we analyzed the top 100 most cited articles related to myopia published in the Web of Science Core Collection database from January 2000 to February 2024. Using data visualization tools CiteSpace, VOSviewer, and Scimago Graphica, we identified the most influential research papers and academic journals in the field of myopia. The top 100 most influential articles were published in 25 renowned journals across 30 countries, with Investigative Ophthalmology & Visual Science being the most prolific. Ophthalmology had the highest total citation frequency, while The Lancet had the highest average citation frequency. The United States and the National University of Singapore were the top countries and institutions with the most published papers. The 3 major research directions are Ophthalmology, Medicine, General & Internal, and Genetics & Heredity. The top 5 co-occurring keywords were refractive error, risk factors, prevalence, eye growth, and form-deprivation myopia. Cluster analysis results highlighted focal points such as retinal detachment, high myopia, and contrast sensitivity, indicating potential future research trends. Prospective research directions include investigating the pathogenesis of myopia, updating diagnostic technologies, and identifying risk genes for myopia and its complications.

Blood MAIT cells phenotype in patients with Opisthorchis felineus invasion depending on the severity of liver fibrosis

Introduction. MAIT cells are a new subpopulation of T cells that protect mucous barriers against penetration of foreign substances. There are practically no studies devoted to the participation of these cells in the pathogenesis of parasitic diseases.Aim. To study the phenotype of blood MAIT cells in patients with Opisthorchis felineus (O. felineus) invasion depending on the severity of liver fibrosis.Materials and methods. A total of 78 patients with O. felineus invasion (42 men and 36 women) and 26 control group individuals (14 men and 12 women) were examined. Opisthorchiasis was diagnosed using coproovoscopy and duodenal contents microscopy. All patients underwent liver elastometry using Aixplorer (France) or Siemens Acuson S2000 (Germany) systems with determination of the liver fibrosis degree according to METAVIR. The phenotype composition of lymphocytes was investigated using a Navios flow cytometer (Beckman Coulter, USA). T cells, T helpers, and T cytotoxic lymphocytes were isolated and the presence of NCR Va7.2 and CD161 on the surface of these cells was assessed.Results. The content of MAIT T-helpers was decreased in patients with O. felineus invasion compared to healthy individuals (p < 0.001). In MAIT T-cytotoxic cells, a similar pattern was not detected (p = 0.5). In patients with liver fibrosis F2 according to METAVIR compared to individuals with F0 according to METAVIR, a decrease in the total number of T-cells, T-helpers and T-cytotoxic cells, as well as MAIT T-helpers and MAIT T-cytotoxic cells was observed. Thus, the content of CD161+ NCR Va7.2+ T-helpers was 0.020% [0.004–0.042%] in patients with opisthorchiasis with F0 according to METAVIR and 0.0% [0.0–0.003%] in individuals with liver fibrosis F2 according to METAVIR (p = 0.001). For CD161+ NCR Va7.2+ T-cytotoxic cells, these indicators were, respectively, 1.47% [0.41–2.49%] and 0.12% [0.07–0.31%] (p < 0.001).Conclusion. Further study of MAIT cells in patients with liver pathology has undoubted prospects for the creation of new therapeutic and diagnostic technologies.

Rapid Molecular Diagnostics in Vulvovaginal Candidosis.

Vulvovaginal candidosis (VVC) is a common condition among women, with current diagnostic methods relying on clinical evaluation and laboratory testing. These traditional methods are often limited by the need for specialized training, variable performance, and lengthy diagnostic processes, leading to delayed treatment and inappropriate antifungal use. This review evaluates the efficacy of molecular diagnostic tools for VVC and provides guidance on their application in clinical practice. A literature search was conducted using PubMed to identify studies evaluating rapid diagnostic tests specifically for vulvovaginal Candida isolates. Inclusion criteria focused on studies utilizing molecular diagnostics for the detection of Candida species in VVC. Articles discussing non-vaginal Candida infections, non-English studies, and animal or in vitro research were excluded. Twenty-three studies met the inclusion criteria, predominantly evaluating nucleid acid amplification tests/polymerase chain reaction (NAAT/PCR) assays and DNA probes. PCR/NAAT assays demonstrated high sensitivity and specificity (>86%) for VVC diagnosis, outperforming conventional diagnostic methods. Comparatively, DNA probes, while simpler, exhibited lower sensitivity. The included studies were mostly observational, with only one randomized controlled trial. Emerging diagnostic technologies, including artificial intelligence and integrated testing models, show promise for improving diagnostic precision and clinical outcomes. Molecular diagnostics offer a significant improvement in VVC management, though traditional methods remain valuable in resource-limited settings.

Toward Quantum Noses: Quantum Chemosensing Based on Molecular Qubits in Metal-Organic Frameworks.

ConspectusQuantum sensing leverages quantum properties to enhance the sensitivity and resolution of sensors beyond their classical sensing limits. Quantum sensors, such as diamond defect centers, have been developed to detect various physical properties, including magnetic fields and temperature. However, the spins of defects are buried within dense solids, making it difficult for them to strongly interact with molecular analytes. Therefore, nanoporous materials have been implemented in combination with electron spin center of molecules (molecular qubits) to produce quantum chemosensors that can distinguish various chemical substances. Molecular qubits have a uniform structure, and their properties can be precisely controlled by changing their chemical structure. Metal-organic frameworks (MOFs) are suitable for supporting molecular qubits because of their high porosity, structural regularity, and designability. Molecular qubits can be inserted in the MOF structures or adsorbed as guest molecules. The qubits in the MOF can interact with analytes upon exposure, providing an effective and tunable sensing platform.In this Account, we review the recent progress in qubit-MOF hybrids toward the realization of room-temperature quantum chemosensing. Molecular qubits can be introduced in controlled concentrations at targeted positions by exploiting metal ions, ligands, or guests that compose the MOF. Heavy metal-free organic chromophores have several outstanding features as molecular qubits; namely, they can be initialized by light irradiation and exhibit relatively long coherence times of submicroseconds to microseconds, even at room temperature. One detection method involves monitoring the hyperfine interaction between the electron spins of the molecular qubits and the nuclear spins of the analyte incorporated in the pore. There is also an indirect detection method that relies on the motional change in molecular qubits. If the motion of the molecular qubit changes with the adsorption of the analyte, it can be detected as a change in the spin relaxation process. This mechanism is unique to qubits exposed in nanopores, not observed in conventional qubits embedded in dense solids.By maximizing the guest recognition ability of MOFs and the environmental sensitivity of qubits, quantum chemosensing that recognizes specific chemical species in a highly selective and sensitive manner may be possible. It is difficult to distinguish between diverse chemical species by employing only one combination of MOF and qubit, but by creating arrays of different qubit-MOF hybrids, it would become possible to distinguish between various analytes based on pattern recognition. Inspired by the human olfactory mechanism, we propose the use of multiple qubit-MOF hybrids and pattern recognition to identify specific molecules. This system represents a quantum version of olfaction, and thus we propose the concept of a "quantum nose." Quantum noses may be used to recognize biometabolites and biomarkers and enable new medical diagnostic technologies and olfactory digitization.

Optical investigation of degradation of graphene oxide in alkaline environment: Evidence of two distinct photon-emitting phases in visible region.

In this work we show a procedure of treating of the graphene oxide in alkaline environment as a function of the treatment time in order to obtain novel structures with strong luminescence properties, water-stable, useful as potential replacement for critical raw materials employed as example in optical and optoelectronic devices or for diagnostic and therapeutic technology. These structures have distinct blue and green-luminescence properties which derived most likely from different structural conformations, one associable with that of carbon quantum dots (or as an alternative to that of the Oxidative Debris), the other, lighter and more similar to organic compounds, reported in literature as fulvic-like molecules, but whose nature has to be further investigated. We show that the lighter fraction has a dual mechanism of photoemission: the excitation-independent PL for excitation wavelength within 350 nm and the excitation-dependent component for excitation wavelength ranging in the visible spectrum. The PL dual behaviour could depend on fluorescent nanoclusters composed by specific organic fluorophores with a carbonaceous core. FTIR analysis shows reasonably the same functional groups unless of some difference discussed in the text, meanwhile UV–Vis and PL analysis clearly highlight two distinct emissions (450 nm and 530 nm) in the visible region of the electromagnetic spectrum. Excitation-dependent photoluminescence, water stability and organic fluorescent nanostructures are issues particularly required for application in the biological field but also in materials science.

Opportunities and challenges in the development of artificial intelligence research in spinal surgery

Artificial intelligence has emerged as a game-changer in the field of spine surgery, offering transformative diagnostic and therapeutic approaches for spinal conditions. The application of AI in spine research encompasses a diverse range of diseases, with usage scenarios becoming increasingly widespread and technological integration going deeper. AI technology shows immense promise and value in the diagnosis of spinal diseases, the formulation of treatment strategies, surgical navigation, prognostic evaluation, and postoperative rehabilitation. Through deep learning and machine learning, AI can aid doctors in enhancing diagnostic accuracy, creating personalized treatment plans, and executing precise maneuvers during surgery, thus improving operational safety. Moreover, AI can provide intelligent support for patients' postoperative recovery, facilitating the restoration of their functions. However, current research is still in its nascent stage and confronts several challenges, such as the lack of standardized databases, the simplicity of algorithmic learning models, the inadequate fusion of multi-modal clinical information, and the limited clinical applicability. By developing open-source, standardized spine databases, incorporating innovative intelligent core algorithms, and establishing normalized screening, diagnostic, and predictive models for spinal conditions, we trust that we can drive the innovation and refinement of diagnostic and treatment technologies in spine surgery. This will realize an effective conjunction between technological provision and clinical demands, continuously elevating the intelligence level of spine surgery and offering safer, more effective medical services to a vast array of patients.

Diagnostic reference levels for the city of Moscow during X-ray examinations

Diagnostic reference levels are one of the effective tools for optimizing patient radiation doses in diagnostic radiology. The staff of domestic radiology departments does not have an understanding of the development process and use of diagnostic reference levels during X-ray examinations, although this tool is a generally accepted in practice abroad. The article shows the experience of forming regional diagnostic reference levels based on measurements provided by the accredited testing laboratory of the Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department. The diagnostic reference levels for radiography in Moscow were developed for eight of the most common types of examinations based on the operation of 216 digital X-ray machines. The resulting levels, expressed in the dose-area product (cGy-cm2) and effective dose (mSv), were compared with the local diagnostic reference levels for the Ryazan region, Russia, and national diagnostic reference levels for other countries. The analysis showed continuity in the values. Some types of studies have been optimized. The developed diagnostic reference levels are the quality performance indicators foe diagnostic radiology departments and will be used to optimize a patient radiation dose during diagnostic studies in Moscow.

Diagnosis of systemic lupus erythematosus using cross-modal specific transfer fusion technology based on infrared spectra and metabolomics

BackgroundSystemic lupus erythematosus (SLE) is a chronic autoimmune disease. Currently, the medical diagnosis of SLE mainly relies on the clinical experience of physicians, and there is no universally accepted objective method for diagnosing SLE. Therefore, there is an urgent need to design an intelligent approach to accurately diagnose SLE to assist physicians in formulating appropriate treatment plans. With the rapid development of intelligent medical diagnostic technology, medical data is becoming increasingly multimodal. Multimodal data fusion can provide richer information than single-modal data, and the fusion of multiple modalities can effectively enhance the richness of data features to improve modeling performance. ResultsIn this paper, a cross-modal specific transfer fusion technique based on infrared spectra and metabolomics is proposed to effectively integrate infrared spectra and metabolomics by fully exploiting the intrinsic relationships between features across different modalities, thus achieving the diagnosis of SLE. In this research, a Decision Level Fusion module is also proposed to fuse the representations of two specific transfers further, obtaining the final prediction scores. Comprehensive experimental results demonstrate that the proposed method significantly improves the performance of SLE prediction, with accuracy and Area Under Curve (AUC) reaching 94.98 % and 97.13 %, respectively, outperforming existing methods. SignificanceOur framework effectively integrates infrared spectra and metabolomics to achieve a more accurate prediction of SLE. Our research indicates that prediction methods based on different modalities outperform those using single-modality data. The Cross-modal Specific Transfer Fusion module effectively captures the complex relationships within each single modality and models the complex relationships between different modalities.

Implementation and validation of MICaFVi: A highly efficient nanotechnology-based method for coronaviruses detection

Coronaviruses have emerged as a significant public health concern due to the global impact of Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which cause COVID-19. The development of sensitive and accurate detection methods is critical for early diagnosis, disease management, and outbreak control. In a previous study, we developed and optimized a nanobased detection methodology called Magnetic Immuno-Capture Followed by Flow Virometry (MICaFVi) using virus-mimicking silica nanoparticles and MERS-CoV/SARS-CoV-2 pseudoviral particles. In the present study, we have extended this methodology to evaluate its specificity and sensitivity for detecting wild-type MERS-CoV and SARS-CoV-2 in human and camel samples. Our results demonstrated that MICaFVi successfully detected MERS-CoV and SARS-CoV-2 viruses with high sensitivity and specificity, although it showed reduced performance for samples with Ct values of 30 or lower compared to qPCR. Despite some limitations in detection speed and sensitivity, MICaFVi represents a significant advancement in diagnostic methodologies by combining nanotechnology with flow cytometry. Additionally, we adapted the MICaFVi methodology to simultaneously detect MERS-CoV and SARS-CoV-2 in a single multiplex assay. The successful implementation of this advanced detection approach has important implications for improving early detection, surveillance, and control of both current and future viral or bacterial pandemics. Our results underscore the potential of MICaFVi as a valuable tool for monitoring the spread of these viruses and highlight its role in advancing diagnostic technologies. This extension of our earlier work offers new insights into the application of nanotechnology and flow cytometry for viral detection.

Recent Advances in Ocular Drug Delivery: Insights into Lyotropic Liquid Crystals.

Background/Objectives: This review examines the evolution of lyotropic liquid crystals (LLCs) in ocular drug delivery, focusing on their ability to address the challenges associated with traditional ophthalmic formulations. This study aims to underscore the enhanced bioavailability, prolonged retention, and controlled release properties of LLCs that significantly improve therapeutic outcomes. Methods: This review synthesizes data from various studies on both bulk-forming LLCs and liquid crystal nanoparticles (LCNPs). It also considers advanced analytical techniques, including the use of machine learning and AI-driven predictive modeling, to forecast the phase behavior and molecular structuring of LLC systems. Emerging technologies in biosensing and real-time diagnostics are discussed to illustrate the broader applicability of LLCs in ocular health. Results: LLCs are identified as pivotal in promoting targeted drug delivery across different regions of the eye, with specific emphasis on the tailored optimization of LCNPs. This review highlights principal categories of LLCs used in ocular applications, each facilitating unique interactions with physiological systems to enhance drug efficacy and safety. Additionally, novel applications in biosensing demonstrate LLCs' capacity to improve diagnostic processes. Conclusions: Lyotropic liquid crystals offer transformative potential in ocular drug delivery by overcoming significant limitations of conventional delivery methods. The integration of predictive technologies and biosensing applications further enriches the utility of LLCs, indicating a promising future for their use in clinical settings. This review points to continued advancements and encourages further research in LLC technology to maximize its therapeutic benefits.

Computational Analysis Suggests That AsnGTT 3′-tRNA-Derived Fragments Are Potential Biomarkers in Papillary Thyroid Carcinoma

Transfer-RNA-derived fragments (tRFs) are a novel class of small non-coding RNAs that have been implicated in oncogenesis. tRFs may act as post-transcriptional regulators by recruiting AGO proteins and binding to highly complementary regions of mRNA at seed regions, resulting in the knockdown of the transcript. Therefore, tRFs may be critical to tumorigenesis and warrant investigation as potential biomarkers. Meanwhile, the incidence of papillary thyroid carcinoma (PTC) has increased in recent decades and current diagnostic technology stands to benefit from new detection methods. Although small non-coding RNAs have been studied for their role in oncogenesis, there is currently no standard for their use as PTC biomarkers, and tRFs are especially underexplored. Accordingly, we aim to identify dysregulated tRFs in PTC that may serve as biomarker candidates. We identified dysregulated tRFs and driver genes between PTC primary tumor samples (n = 511) and adjacent normal tissue samples (n = 59). Expression data were obtained from MINTbase v2.0 and The Cancer Genome Atlas. Dysregulated tRFs and genes were analyzed in tandem to find pairs with anticorrelated expression. Significantly anticorrelated tRF-gene pairs were then tested for potential binding affinity using RNA22—if a heteroduplex can form via complementary binding, this would support the hypothesized RNA silencing mechanism. Four tRFs were significantly dysregulated in PTC tissue (p < 0.05), with only AsnGTT 3′-tRF being upregulated. Binding affinity analysis revealed that tRF-30-RY73W0K5KKOV (AsnGTT 3′-tRF) exhibits sufficient complementarity to potentially bind to and regulate transcripts of SLC26A4, SLC5A8, DIO2, and TPO, which were all found to be downregulated in PTC tissue. In the present study, we identified dysregulated tRFs in PTC and found that AsnGTT 3′-tRF is a potential post-transcriptional regulator and biomarker.

Lateral flow assay sensitivity and signal enhancement via laser µ-machined constrains in nitrocellulose membrane

Lateral flow assay (LFA) is a handful diagnostic technology that can identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other common respiratory viruses in one strip, which can be tested at the point-of-care without the need for equipment or skilled personnel outside the laboratory. Although its simplicity and practicality make it an appealing solution, it remains a grand challenge to substantially enhance the colorimetric LFA sensitivity. In this work, we present a straightforward approach to enhance the sensitivity of LFA by imposing the flow constraints in nitrocellulose (NC) membranes via a number of vertical femtosecond laser micromachined microchannels which is important for prolonged specific binding interactions. Porous NC membrane surfaces were structured with different widths and densities µ-channels employing a second harmonic of the Yb:KGW femtosecond laser and sample XYZ translation over a microscope objective-focused laser beam. The influence of the microchannel parameters on the vertical wicking speed was evaluated from the video recordings. The obtained results indicated that µ-channel length, width, and density in NC membranes controllably increased the immunological reaction time between the analyte and the labeled antibody by 950%. Image analysis of the colorimetric indicators confirmed that the flow rate delaying strategy enhanced the signal sensitives by 40% compared with pristine NC LFA.

Innovative COVID-19 Point-of-Care Diagnostics Suitable for Tuberculosis Diagnosis: A Scoping Review.

Rapid and accurate point-of-care (POC) tuberculosis (TB) diagnostics are crucial to bridge the TB diagnostic gap. Leveraging recent advancements in COVID-19 diagnostics, we explored adapting commercially available POC SARS-CoV-2 tests for TB diagnosis in line with the World Health Organization (WHO) target product profiles (TPPs). A scoping review was conducted following PRISMA-ScR guidelines to systematically map POC antigen and molecular SARS-CoV-2 diagnostic tests potentially meeting the TPPs for TB diagnostic tests for peripheral settings. Data were gathered from PubMed/MEDLINE, bioRxiv, medRxiv, publicly accessible in vitro diagnostic test databases, and developer websites up to 23 November 2022. Data on developer attributes, operational characteristics, pricing, clinical performance, and regulatory status were charted using standardized data extraction forms and evaluated with a standardized scorecard. A narrative synthesis of the data is presented. Our search yielded 2003 reports, with 408 meeting eligibility criteria. Among these, we identified 66 commercialized devices: 22 near-POC antigen tests, 1 POC molecular test, 31 near-POC molecular tests, and 12 low-complexity molecular tests potentially adaptable for TB. The highest-scoring SARS-CoV-2 diagnostic tests were the near-POC antigen platform LumiraDx (Roche, Basel, Switzerland), the POC molecular test Lucira Check-It (Pfizer, New York, NY, USA), the near-POC molecular test Visby (Visby, San Jose, CA, USA), and the low-complexity molecular platform Idylla (Biocartis, Lausanne, Switzerland). We highlight a diverse landscape of commercially available diagnostic tests suitable for potential adaptation to peripheral TB testing. This work aims to bolster global TB initiatives by fostering stakeholder collaboration, leveraging SARS-CoV-2 diagnostic technologies for TB, and uncovering new commercial avenues to tackle longstanding challenges in TB diagnosis.

B-117 Rapid Classification of Cleanliness Grades of Vaginal Discharge through Computational Analysis of SERS Spectra via Variational Auto-Encoder Model And Long Short-Term Memory Networks

Abstract Background Classification of cleanliness grades of vaginal discharge plays a pivotal role in clinical laboratories, serving as indicators for the presence of various vaginal infections such as bacterial vaginosis and candidal vulvovaginitis, etc. The conventional method for assessing vaginal discharge cleanliness involves manual microscopy, a technique susceptible to variations based on the examiner's expertise and subjective judgment, hindering the attainment of swift and accurate diagnoses. Methods This study introduces a novel approach by integrating Surface-Enhanced Raman Spectroscopy (SERS) with a deep learning algorithm for the rapid classification of vaginal discharge cleanliness grades. The proposed classification method combines the Variational Auto-Encoder (VAE) and Long Short-Term Memory (LSTM) to process SERS spectra. The quality of the training data is assessed using the signal-to-noise ratio (SNR) as a standard indicator. Results The application of the VAE-LSTM algorithm significantly enhances classification performance, providing swift and accurate results while reducing the required time. Comparative analyses with various classical machine learning algorithms and evaluation metrics reveal the superior predictive capability and time efficiency of the VAE-LSTM algorithm, demonstrating 100% accuracy and a fitting time of 2 seconds. Blind testing of SERS spectra collected from unknown patients further validates the reliability of the proposed method. Conclusions This study presents a promising and practical approach for the rapid classification of vaginal discharge cleanliness grades. The integration of the SERS technique with the VAE-LSTM algorithm not only ensures accuracy and efficiency in clinical diagnosis but also lays the foundation for advancements in non-invasive diagnostic technologies in gynecological healthcare.

Optimization and correction of breast dynamic optical imaging projection data based on deep learning

Breast cancer poses a significant health threat to women, necessitating advancements in diagnostic technologies. Breast dynamic optical imaging (DOI) technology, recognized for its non-invasive and radiation-free properties, is extensively utilized for the early screening and quantitative analysis of breast tumors. The integration of deep learning, a robust technology for automatic image feature extraction, with breast DOI has the potential to enhance tumor detection and diagnosis significantly. This paper introduces a deep learning-enhanced image optimization approach to overcome challenges such as poor image quality and distorted projection data commonly encountered in existing DOI methods. The approach utilizes convolutional neural networks (CNNs) to extract features from raw images and employs generative adversarial networks (GANs) to enhance these images, thereby improving their quality and contrast. Additionally, a novel correction algorithm is developed to address projection data distortion, enabling the reconstruction and correction of this data for more accurate and reliable imaging results. Experimental findings confirm that the proposed method markedly enhances both image quality and projection data accuracy in breast DOI, offering a reliable foundation for clinical diagnosis. This study not only provides a new perspective and methodology for the early screening and diagnosis of breast cancer but also holds substantial clinical importance and prospective applications.

Aggregation-induced emission luminogens for intracellular bacteria imaging and elimination

Intracellular bacterial infections are a serious threat to human health due to their ability to escape immunity and develop drug resistance. Recent attention has been devoted to identifying and ablating intracellular bacteria with fluorescence probes. Aggregation-induced emission luminogens (AIEgens) photosensitizers as fluorescence probes possess excellent photostability and rapid response, which have emerged as powerful fluorescent tools for intracellular bacterial detection and antibacterial therapy. This review is intended to highlight the current advances in AIEgens on intracellular bacteria imaging and elimination, which covers topics from intracellular AIE mechanism, intracellular bacteria imaging of AIEgens to the elimination of intracellular bacteria with AIEgens. AIEgens utilized different interactions to detect intracellular bacteria, emitting bright light due to restricted intramolecular movement to visualize intracellular bacteria. Photosensitive AIEgens generate reactive oxygen species (ROS) in the aggregate state to elimate intracellular bacteria. Moreover, the prospects and application of AIEgens in intracellular bacteria imaging and elimination are also discussed, which provides insights for the development of AIE-based diagnostic and therapeutic materials and technologies.

A deep learning algorithm for the detection of aortic dissection on non-contrast-enhanced computed tomography via the identification and segmentation of the true and false lumens of the aorta.

Aortic dissection is a life-threatening clinical emergency, but it is often missed and misdiagnosed due to the limitations of diagnostic technology. In this study, we developed a deep learning-based algorithm for identifying the true and false lumens in the aorta on non-contrast-enhanced computed tomography (NCE-CT) scans and to ascertain the presence of aortic dissection. Additionally, we compared the diagnostic performance of this algorithm with that of radiologists in detecting aortic dissection. We included 320 patients with suspected acute aortic syndrome from three centers (Beijing Anzhen Hospital Affiliated to Capital Medical University, Fujian Provincial Hospital, and Xiangya Hospital of Central South University) between May 2020 and May 2022 in this retrospective study. All patients underwent simultaneous NCE-CT and contrast-enhanced CT (CE-CT). The cohort comprised 160 patients with aortic dissection and 160 without aortic dissection. A deep learning algorithm, three-dimensional (3D) full-resolution U-Net, was continuously trained and refined to segment the true and false lumens of the aorta to determine the presence of aortic dissection. The algorithm's efficacy in detecting dissections was evaluated using the receiver operating characteristic (ROC) curve, including the area under the curve (AUC), sensitivity, and specificity. Furthermore, a comparative analysis of the diagnostic capabilities between our algorithm and three radiologists was conducted. In diagnosing aortic dissection using NCE-CT images, the developed algorithm demonstrated an accuracy of 93.8% [95% confidence interval (CI): 89.8-98.3%], a sensitivity of 91.6% (95% CI: 86.7-95.8%), and a specificity of 95.6% (95% CI: 91.2-99.3%). In contrast, the radiologists achieved an accuracy of 88.8% (95% CI: 83.5-94.1%), a sensitivity of 90.6% (95% CI: 83.5-94.1%), and a specificity of 94.1% (95% CI: 72.9-97.6%). There was no significant difference between the algorithm's performance and radiologists' mean performance in accuracy, sensitivity, or specificity (P>0.05). The algorithm proficiently segments the true and false lumens in aortic NCE-CT images, exhibiting diagnostic capabilities comparable to those of radiologists in detecting aortic dissection. This suggests that the algorithm could reduce misdiagnoses in clinical practice, thereby enhancing patient care.

Overexpression of SEZ6L2 and Immune Infiltration in Cancer Based on Gene Image Diagnosis.

With the rapid advancement of optical image diagnostic technology, researchers are delving into the potential applications in the field of cancer diagnosis and treatment. The exact link between the SEZ6L2 gene and cancer immune infiltration remains elusive. This study aims to investigate the relationship between SEZ6L2 gene overexpression and cancer immune infiltration using optical image diagnostic technology, thereby presenting novel insights for enhancing cancer diagnosis and treatment strategies. Tissue samples obtained from cancer patients were meticulously analyzed to quantitatively assess the expression of the SEZ6L2 gene through light image diagnostic technology. Additionally, immunohistochemical techniques were employed to assess the nature and quantity of immune infiltrating cells within the cancerous tissues. The enrichment pathways were found to include complement activation, circulating immunoglobulin mediated humoral immune response, protein activation cascade, immunoglobulin complex, and immunoglobulin. In addition, the expression of SEZ6L2 is closely related to the infiltration level of tumor infiltrating immune cells (TIICs), and there is a potential relationship between the expression of SEZ6L2 and different marker genes of TIIC. Increased SEZ6L2 mRNA expression in breast invasive carcinoma was significantly associated with negative prognosis and immune invasion. SEZ6L2 may be a novel prognostic biomarker and a potential immunotherapeutic target in BRCA.

Machine learning-based synthesis of diagnostic algorithms for electromechanical actuators to improve the aerospace flight safety

The relevance of research aimed at developing diagnostic technologies for electromechanical actuators is due to the need to improve flight safety in conditions of increasing intensity of highly electrified aircraft and spacecraft operations. The paper discusses one of the promising approaches to electromechanical actuator health management, which involves the use of machine learning methods to synthesize health monitoring algorithms. Machine learning methods make it possible to build classification models based on empirical data, which are used to generate recommendations for making operational decisions. Empirical data, which is a source of valuable experience and the basis of a training sample necessary for formalizing patterns in classification models, can be formed as a result of life tests, mathematical modeling, and actuator operation. In order to improve the safety of space flights, the article focuses on the integration of electromechanical actuator mathematical model methods, optimal space filling, and machine learning. Optimal space filling methods are used to reduce the computational costs associated with representative training sampling. Examples of developing classification models are given to determine failures associated with changes in gear (backlash, Coulomb friction and viscous friction) which is the most critical actuator link. As a result of computational studies, the main advantages of the proposed approach to the synthesis of electromechanical actuator health assessment algorithms are shown.