Monitoring Of Progression Research Articles

Machine Learning-Assisted High-Throughput Identification and Quantification of Protein Biomarkers with Printed Heterochains.

Advanced in vitro diagnosis technologies are highly desirable in early detection, prognosis, and progression monitoring of diseases. Here, we engineer a multiplex protein biosensing strategy based on the tunable liquid confinement self-assembly of multi-material heterochains, which show improved sensitivity, throughput, and accuracy compared to standard ELISA kits. By controlling the material combination and the number of ligand nanoparticles (NPs), we observe robust near-field enhancement as well as both strong electromagnetic resonance in polymer-semiconductor heterochains. In particular, their optical signals show a linear response to the coordination number of the semiconductor NPs in a wide range. Accordingly, a visible nanophotonic biosensor is developed by functionalizing antibodies on central polymer chains that can identify target proteins attached to semiconductor NPs. This allows for the specific detection of multiple protein biomarkers from healthy people and pancreatic cancer patients in one step with an ultralow detection limit (1 pg/mL). Furthermore, rapid and high-throughput quantification of protein expression levels in diverse clinical samples such as buffer, urine, and serum is achieved by combining a neural network algorithm, with an average accuracy of 97.3%. This work demonstrates that the heterochain-based biosensor is an exemplary candidate for constructing next-generation diagnostic tools and suitable for many clinical settings.

The Use of the Severity Dependence Scale (SDS) as an Outcome in Studies of Alcohol and Other Drug Use a Systematic Review

Introduction: The Severity Dependence Scale (SDS) is a validated measure of the severity of dependence on alcohol or drugs. SDS scores can be used to guide treatment planning, monitor progress, and evaluate treatment outcomes. Objectives: We aimed to review studies that analysed SDS as an outcome in studies of alcohol and drug use (AoD), with a particular focus on the methodology used to examine the changes in SDS. Methods: The search was performed using the literature databases Embase, PubMed and Medline. Articles were included when the outcome was SDS in AoD. Studies that examined SDS, but not among the AoD population, studies that reported SDS as predictors, qualitative research, study protocols, conference papers, and studies in non-English language were excluded. Results: Among 179 articles identified, 15 were included in the systematic review. Two studies conducted cannabis research, two for methamphetamine, one for cannabis and amphetamine, one for cocaine, one for ketamine, one for ecstasy and seven for general illicit drugs. Out of 15 studies, ten used the t-test for statistical analysis of the SDS, one used a generalised estimating equation, one used a Spearman non-parametric test, and one used a linear mixed model, one reported the baseline score for the SDS and did not report the SDS at follow-up, one reported a descriptive analysis of the SDS. Conclusions: In the absence of a standardised cut-off score and a minimal important difference, more attention should be paid in analysing the discrete scale of the SDS to ensure analysis accuracy.

Teaching and Learning Optics: A Bibliometric Analysis with a Detailed Future Insight Overview

Optics research has recently proven important for scientific, educational, and industrial applications. This study examines the increase and distribution of literature on learning and teaching optics. This analytical strategy uses quantitative and statistical methods to identify trends, evaluate quality, and monitor progress. From the 530 documents obtained using the Scopus database between 1944 and 2023, 464 were chosen. With a total of 49 documents, 2014 had the greatest substantial increase in the number of documents since 1944 till this data was collected. Writers from the United States contributed to 115 documents with 36% citations. They ranked first, followed by authors from Germany (37 documents and 12% citations) and China (36 documents), with citation rankings just below the top 10. Most publications appeared in Physics Education (15 documents; Q2 SJR 0.41). The International Journal of Science Education received the most citations for publications on teaching and learning optics (159 citations; Q1 SJR 1.15) and was ranked 1 in its category. The current core literature on teaching and learning optics shows that this area is quickly evolving but with insufficient international research collaboration. Research collaboration in this field must be strengthened to improve the worldwide response to teaching and learning optics. There is a need to broaden the scope of study regarding assessment instruments, technological engineering, learning approaches, learning methods, learning strategies, and learning media within the context of teaching and learning optics from elementary school to the university level.

Using Self-regulated strategy of development in Teaching writing for Iraqi EFL University Students

Self-Regulated Strategy of Development (SRSD) is an intervention designed to improve students' academic skills through a six-step process that teaches students specific academic strategies and self-regulation skills (mention them here). When it comes to writing, self-regulation allows students to manage tasks and monitor progress (also called self-monitoring), among other key skills. There are many ways to use strategy instruction to help students improve their writing skills in English.

Biotechnology in rheumatology and immunology for researchers: An overview

Biotechnology has revolutionized the fields of rheumatology and immunology, providing innovative solutions for understanding and treating autoimmune diseases. Traditionally, these fields relied on basic immunological techniques and limited therapeutic options. However, advancements in biotechnology have introduced novel diagnostic tools, targeted therapies, and personalized medicine approaches, significantly improving patient outcomes. The aim of this overview is to provide researchers with a comprehensive understanding of the current biotechnological advancements in rheumatology and immunology, highlighting their impact on disease diagnosis, management, and treatment. The primary objective is to explore the various biotechnological innovations that have transformed rheumatology and immunology. This includes the development of biologics, biomarkers, and genetic tools that have enabled precise diagnosis and tailored treatments. Additionally, the overview seeks to examine the ongoing research and future directions in these fields, emphasizing the potential for further breakthroughs. Biotechnology has significantly advanced the fields of rheumatology and immunology, offering new hope for patients with autoimmune diseases. The introduction of biologics has been a game-changer, allowing for targeted therapies that have improved disease management and patient quality of life. Biomarker discovery has enhanced diagnostic accuracy and enabled the monitoring of disease progression. Genetic and genomic tools have paved the way for personalized medicine, tailoring treatments to individual patient profiles. As research continues to evolve, the integration of biotechnology in these fields promises to bring even more innovative solutions, ultimately transforming the landscape of autoimmune disease treatment and management. Researchers must stay abreast of these developments to continue driving progress and improving patient outcomes.

Verification of the AIS service availability model based on dynamic data streams recorded from three receiving stations in the Polish coastal area

The reliability aspects of the operation of radio navigation systems constitute a crucial element for the safety of maritime navigation.. Technological progress in ship traffic monitoring is achieved through the design of ship systems and shore infrastructure equipped with Automatic Identification System (AIS) devices. One of the issues with AIS operation is the limited availability of the service in the form of data streams with an extended data age recorded on the receiving side. Another problem is the emission and reception by ships of incomplete positional reports without navigational parameters. Such situations render the system operationally unfit in terms of processed information. Therefore, it is essential to investigate the operational characteristics of radio navigation systems and develop tools to monitor the AIS service status on the receiving side. This article presents the development of a model for the availability of an AIS for vessels based on the determined mean time of the occurrence of incomplete navigation parameter values in AIS messages and the results of research in the domain of time and frequency using a mathematical method of the Fast Fourier Transform (FFT). The study results refer to six basic navigation parameters and show a varying service availability factor for the navigation parameters under study, i.e. the latitude (LAT), longitude (LON), speed over ground (SOG), course over ground (COG), heading (HDT), and the rate of turn (ROT). The data recorded by three receiving AIS stations on the Polish coast, i.e. PLKOL, PLSZZ, and PLSWI, were used as a key source of practical knowledge on the limitations of the AIS service availability. The experiment observed interruptions in the regular transmission of data from navigation equipment in the AIS service operational zone. As a result, the functional relationship was described based on the spectral analysis of the frequency of occurrence of times between the service repair (Time To Repair, TTR), and the model was proposed to be applied to the study of other variables. The presented model is a tool that allows for improving the monitoring of vessel traffic in terms of reliability, which directly affects the improvement of maritime traffic safety.

Monitoring Strategies Teachers of English Use with Visually Impaired Learners in Integrated Schools in Nairobi City County, Kenya

This study explored monitoring strategies teachers of English use with learners with visual impairment (LWVI) in integrated secondary schools in Nairobi City County, Kenya. English contains concepts that build on each other, underscoring the need to understand initial ideas as a foundation for comprehending subsequent concepts. Thus, the study was based on Lev Vygotsky’s Cognitive Development Theory (1934). The study objective was to identify monitoring strategies. Learners' performance in English was the dependent variable, while progress monitoring was the independent variable. The researcher sampled sixty-four participants in two schools including deputy principals, teachers of English and Heads of Department (HoD), LWVI, and regular learners. Simple random sampling was used to select participants from among learners, while purposive sampling was used to select teachers. As a qualitative research, data was collected through interviews, observation, and focus groups. The researcher used thematic analysis by coding the data into subsets to capture the major themes. The results revealed that verbal communication and use of sighted deskmates were predominantly used to monitor progress due to the teachers’ incompetence in braille. The following recommendations were drawn from these inferences: The Teachers Service Commission (TSC) should employ SNE-trained teachers in integrated schools, and the Kenya Institute of Curriculum Development (KICD) should provide braille learning materials.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

Optimising Extracellular Vesicle Metabolomic Methodology for Prostate Cancer Biomarker Discovery.

Conventional diagnostic tools for prostate cancer (PCa), such as prostate-specific antigen (PSA), transrectal ultrasound (TRUS), digital rectal examination (DRE), and tissue biopsy face, limitations in individual risk stratification due to invasiveness or reliability issues. Liquid biopsy is a less invasive and more accurate alternative. Metabolomic analysis of extracellular vesicles (EVs) holds a promise for detecting non-genetic alterations and biomarkers in PCa diagnosis and risk assessment. The current research gap in PCa lies in the lack of accurate biomarkers for early diagnosis and real-time monitoring of cancer progression or metastasis. Establishing a suitable approach for observing dynamic EV metabolic alterations that often occur earlier than being detectable by other omics technologies makes metabolomics valuable for early diagnosis and monitoring of PCa. Using four distinct metabolite extraction approaches, the metabolite cargo of PC3-derived large extracellular vesicles (lEVs) was evaluated using a combination of methanol, cell shearing using microbeads, and size exclusion filtration, as well as two fractionation chemistries (pHILIC and C18 chromatography) that are also examined. The unfiltered methanol-microbeads approach (MB-UF), followed by pHILIC LC-MS/MS for EV metabolite extraction and analysis, is effective. Identified metabolites such as L-glutamic acid, pyruvic acid, lactic acid, and methylmalonic acid have important links to PCa and are discussed. Our study, for the first time, has comprehensively evaluated the extraction and separation methods with a view to downstream sample integrity across omics platforms, and it presents an optimised protocol for EV metabolomics in PCa biomarker discovery.

Table tennis as part of an elective course in physical education

Importance. One of the methods to improve the level of physical education teaching is the introduction of elective disciplines. Elective disciplines allow the student to satisfy his professional interests and personal inclinations, and give the teacher the opportunity to organize the educational process most effectively.Materials and Methods. One of the issues of teaching is carrying out constant monitoring and recording of progress, the correct distribution of points for completing assignments. Continuous monitoring allows the teacher to identify shortcomings and issues that arise when teaching elective disciplines and timely adjust the educational process.Results and Discussion. Using the pedagogical principles of sports-oriented physical development, we are developed a system of criteria that allows us to effectively assess the preparation of students in the process of studying the elective discipline “Table Tennis”. The assessment system is focused on the content of educational material in the module and took into account the main areas of training such as special technical training, theoretical and methodological foundations of tennis, general physical training. The assessment of the level of students’ training in special technical training is carried out by testing the skills of performing certain technical techniques. The quality and speed of performing these techniques are translated into points. During the learning process, the set of technical techniques becomes more complex or the requirements for the speed and quality of its implementation increase.Conclusion. The presented assessment system made it possible to effectively assess the level of students’ preparation when studying the elective discipline “Table Tennis”.

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons' speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n = 36) and non-bulbar onset (n = 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care.

Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes

Gait impairments are among the most common and disabling symptoms of Parkinson’s disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 (p < 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring.

Consensus Guidance for Monitoring Individuals With Islet Autoantibody-Positive Pre-Stage 3 Type 1 Diabetes.

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care.

Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.Graphical

Design and development of new charge controller for GPS tracker for electric vehicles

This investigation introduces a new charge controller for GPS trackers, with the objective of offering long-lasting and efficient tracking solutions. The article emphasizes the widespread use of GPS trackers in various sectors, as well as the critical need for sustainable power sources to maintain their effectiveness. The charge controller ensures optimal performance and durability by meticulously choosing components. Experimental investigations confirm the efficacy of the charge controller to deliver a steady power supply to the GPS tracker. The integration of GPS technology with solar PV charging allows for continuous monitoring. The results show that the solar photovoltaic (PV) system reached a peak output voltage of 10 V, ensuring that the battery remained at a stable level of 12.4 V and continually providing the GPS tracker with a steady 5 V. These findings validate the system's capacity to deliver dependable and enduring electricity, signifying notable progress in remote monitoring, asset tracking, and fleet management.

SDG 6 progress analyses in sub-Saharan Africa from 2015–2020: the need for urgent action

This study used data from various international databases to track the progress of SDG 6 target indicators in sub-Saharan Africa (SSA) region between 2015 and 2020. Findings showed that although minimal positive progress was realized in the evaluation period, more than half of SSA residents had no access to safe drinking water services, improved sanitation facilities, hygiene services and safe wastewater treatment. The computed SDG 6 profile index conformed with the trend of indicators as progress of the region to the goal’s realization was rated as medium–low. The trend was further compounded by limited data, preferential access to water and sanitation services to the urban rich and poor monitoring of progress. The analysis also noted that despite these challenges the region was putting concerted efforts to promote transboundary cooperation in water management and had 6% of water stressed resources. The study recommended on the need to revamp water data collection and monitoring efforts to enable decision-making and planning on management actions, use of technology to treat and reuse wastewater as well as device new sources of water, enhanced participation of communities in water projects and improved water use efficiency to steer progress towards SDG 6 realization in SSA region.

Interaction between α-Synuclein and Bioactive Lipids: Neurodegeneration, Disease Biomarkers and Emerging Therapies.

The present review provides a comprehensive examination of the intricate dynamics between α-synuclein, a protein crucially involved in the pathogenesis of several neurodegenerative diseases, including Parkinson's disease and multiple system atrophy, and endogenously-produced bioactive lipids, which play a pivotal role in neuroinflammation and neurodegeneration. The interaction of α-synuclein with bioactive lipids is emerging as a critical factor in the development and progression of neurodegenerative and neuroinflammatory diseases, offering new insights into disease mechanisms and novel perspectives in the identification of potential biomarkers and therapeutic targets. We delve into the molecular pathways through which α-synuclein interacts with biological membranes and bioactive lipids, influencing the aggregation of α-synuclein and triggering neuroinflammatory responses, highlighting the potential of bioactive lipids as biomarkers for early disease detection and progression monitoring. Moreover, we explore innovative therapeutic strategies aimed at modulating the interaction between α-synuclein and bioactive lipids, including the development of small molecules and nutritional interventions. Finally, the review addresses the significance of the gut-to-brain axis in mediating the effects of bioactive lipids on α-synuclein pathology and discusses the role of altered gut lipid metabolism and microbiota composition in neuroinflammation and neurodegeneration. The present review aims to underscore the potential of targeting α-synuclein-lipid interactions as a multifaceted approach for the detection and treatment of neurodegenerative and neuroinflammatory diseases.

Time-dependent relations between emotion regulation, frustration, and metacognitive strategy use in technology-mediated learning

ABSTRACT Understanding how learners regulate their emotions and engage in metacognitive strategies is crucial for fostering self-regulated learning, particularly in technology-mediated learning. This study examines the temporal relationships between two emotion regulation (ER) strategies, reappraisal and suppression, frustration, and use of progress monitoring as metacognitive strategy, within the context of an educational game on financial literacy. The study involved 82 undergraduate students whose levels of frustration, progress monitoring behaviour, ER strategies were assessed at various points during the learning task. Findings revealed that the use of both reappraisal and suppression decreased during the learning task. Additionally, both ER strategies were negatively associated with frustration, although the relationship between reappraisal and frustration diminished over time. Frustration was negatively related to progress monitoring, indicating that effective emotion regulation can help maintain engagement in metacognitive strategies by keeping cognitive resources available. Notably, suppression and progress monitoring showed a positive relation that increased over time, highlighting the potential usefulness of suppression in extended learning tasks, despite its generally lower effectiveness compared to reappraisal. The results highlight the importance of considering temporal dynamics in the application of ER strategies during extended learning. Practical implications for the design of technology-mediated learning environments and educational interventions are proposed.

Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer

BackgroundAmong gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups.MethodsThirty-four patients, categorized into three groups—10 with EC, 12 with Hy, and 12 CO—between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).ResultsIn all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance.ConclusionThe identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence.

Ocular surface squamous neoplasia (OSSN) is a common eye surface tumour, characterized by the growth of abnormal cells on the ocular surface. OSSN includes invasive squamous cell carcinoma (SCC), in which tumour cells penetrate the basement membrane and infiltrate the stroma, as well as non-invasive conjunctival intraepithelial neoplasia, dysplasia, and SCC in-situ thereby presenting a challenge in early detection and diagnosis. Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments, such as topical medicines, whereas advanced invasive lesions may need orbital exenteration, which carries a risk of death. Artificial intelligence (AI) has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management. AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN, aiding ophthalmologists in early detection and diagnosis. AI can also track and monitor lesion progression over time, providing objective measurements to guide treatment decisions. Furthermore, AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response. This manuscript highlights the role of AI in OSSN, specifically focusing on its contributions in early detection and diagnosis, assessment of lesion progression, treatment planning, telemedicine and remote monitoring, and research and data analysis.