Disease Monitoring Research Articles

New clinical treatment plan for acute lymphoblastic leukemia after whole genome sequencing: An update of the development of new technologies for genome editing

Acute lymphoblastic leukemia (ALL) is now being reclassified into newly identified subtypes due to genetic abnormalities, Philadelphia chromosome‐like B‐lineage ALL is one of the new high‐risk subtypes characterized by genetic alterations that activate various signaling pathways, including those involving cytokine receptors, tyrosine kinases, and epigenetic modifiers. Philadelphia chromosome‐like ALL is essentially heterogeneous; however, deletion mutations in the IKZF1 gene encoding the transcription factor IKAROS underlie many cases as a key factor inducing aggressive phenotypes and poor treatment responses. WGS (whole genome sequencing) studies of ALL patients and ethnically matched controls also identified inherited genetic variations in lymphoid neoplasm‐related genes, which are likely to increase ALL susceptibility. On the contrary, Genome editing technologies offers new opportunities for tackling diseases, and genome-editing tools such as CRISPR-Cas9 are an important means of advancing functional studies of ALL through the incorporation, elimination, and modification of somatic mutations. These findings are directly relevant to clinical hematology, and further studies on this aspect could contribute to accurate diagnosis, effective monitoring of residual disease, and patient‐oriented therapies.

Association of NOTCH3 Variant Risk Category With 2-Year Clinical and Radiologic Small Vessel Disease Progression in Patients With CADASIL.

Pathogenic variants in NOTCH3 are the main cause of hereditary cerebral small vessel disease (SVD). SVD-associated NOTCH3 variants have recently been categorized into high risk (HR), moderate risk (MR), or low risk (LR) for developing early-onset severe SVD. The most severe NOTCH3-associated SVD phenotype is also known as cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). We aimed to investigate whether NOTCH3 variant risk category is associated with 2-year progression rate of SVD clinical and neuroimaging outcomes in CADASIL. A single-center prospective 2-year follow-up study was performed of patients with CADASIL. Clinical outcomes were incident stroke, disability (modified Rankin Scale), and executive function (Trail Making Test B given A t-scores). Neuroimaging outcomes were mean skeletonized mean diffusivity (MSMD), normalized white matter hyperintensity volume (nWMHv), normalized lacune volume (nLV), and brain parenchymal fraction (BPF). Cox regression and mixed-effect models, adjusted for age, sex, and cardiovascular risk factors, were used to study 2-year changes in outcomes and differences in disease progression between patients with HR-NOTCH3 and MR-NOTCH3 variants. One hundred sixty-two patients with HR (n = 90), MR (n = 67), and LR (n = 5) NOTCH3 variants were included. For the entire cohort, there was 2-year mean progression for MSMD (β = 0.20, 95% CI 0.17-0.23, p = 7.0 × 10-24), nLV (β = 0.13, 95% CI 0.080-0.19, p = 2.1 × 10-6), nWMHv (β = 0.092, 95% CI 0.075-0.11, p = 8.8 × 10-20), and BPF (β = -0.22, 95% CI -0.26 to -0.19, p = 3.2 × 10-22), as well as an increase in disability (p = 0.002) and decline of executive function (β = -0.15, 95% CI -0.30 to -3.4 × 10-5, p = 0.05). The HR-NOTCH3 group had a higher probability of 2-year incident stroke (hazard ratio 4.3, 95% CI 1.4-13.5, p = 0.011), and a higher increase in MSMD (β = 0.074, 95% CI 0.013-0.14, p = 0.017) and nLV (β = 0.14, 95% CI 0.034-0.24, p = 0.0089) than the MR-NOTCH3 group. Subgroup analyses showed significant 2-year progression of MSMD in young (n = 17, β = 0.014, 95% CI 0.0093-0.019, p = 1.4 × 10-5) and premanifest (n = 24, β = 0.012, 95% CI 0.0082-0.016, p = 1.1 × 10-6) individuals. In a trial-sensitive time span of 2 years, we found that patients with HR-NOTCH3 variants have a significantly faster progression of major clinical and neuroimaging outcomes, compared with patients with MR-NOTCH3 variants. This has important implications for clinical trial design and disease prediction and monitoring in the clinic. Moreover, we show that MSMD is a promising outcome measure for trials enrolling premanifest individuals.

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Animal Shelter Wastewater Sampling for Pathogens With Epidemic Potential

Canine distemper virus (CDV), canine parvovirus (CPV), and Streptococcus zooepidemicus (S. zoo) represent a range of infectious agents that can cause epidemic disease in dogs, particularly those housed in animal shelters. Outbreaks of disease associated with these agents are relatively common in shelters, resulting in isolation, quarantine, euthanasia, and in some cases, shelter closure in efforts to manage the outbreak. In recent years, outbreaks of CDV and S. zoo have been particularly challenging for shelters in Arizona. Wastewater-based epidemiology (WBE) has proven useful in disease monitoring and outbreak prediction in human populations, including congregate locations, and we hypothesized that it may also be useful in surveillance and prediction for agents responsible for either gastrointestinal (GI) or respiratory disease in shelter animals. Our specific aims were to develop and validate a protocol for detecting infectious agents from wastewater samples collected from shelter run sections and to investigate associations with agent burden and counts of animals with clinical signs. Once protocols for sampling and preparation were optimized, environmental wastewater samples were collected weekly between November 2023 and April 2024 at a large open intake municipal animal shelter in Phoenix, Arizona. The samples were collected from seven drains located in seven run sections in three separate buildings, and were evaluated for the presence of the three agent nucleic acids using quantitative, multiplex PCR. The counts of dogs actively showing GI and respiratory symptoms, graded as mild, moderate, and severe, were collected the afternoon of the day of sampling. Biosafety committee approval was granted for the viruses in this study. Shedding of CPV was found to be widespread and variable over time. S. zoo was detected five times on five different weeks in five different run sections. CDV was found once. There were only six dogs reported with GI signs during the study period. Counts of respiratory disease ranged from 42-132 dogs per run section with most cases being mild in severity and reported in the isolation run section. There is no apparent association between counts of either respiratory or GI cases and the three agents detected through wastewater sampling. During the study there was a single incident of a S. zoo PCR positive dog being isolated and later dying at the shelter. S. zoo was detected through wastewater sampling the week prior to the incident, however, in a run section located in a different building than where the PCR positive dog was housed. To the authors’ knowledge, this study is the first of its kind to develop protocols to detect critical pathogens in wastewater samples from an animal shelter. CPV has been the pathogen most frequently detected, which we speculate may be largely due to the shedding typically observed in dogs post-vaccination, given the absence of GI signs. Further investigation is warranted to better characterize the absence of amplifiable CDV nucleic acids and to examine these samples for other common shelter pathogens.

Comprehensive insights into AML relapse: genetic mutations, clonal evolution, and clinical outcomes.

Acute myeloid leukemia (AML) is a complex hematologic malignancy characterized by uncontrolled proliferation of myeloid precursor cells within bone marrow. Despite advances in understanding of its molecular underpinnings, AML remains a therapeutic challenge due to its high relapse rate and clonal evolution. In this retrospective study, we analyzed data from 24 AML patients diagnosed at a single institution between January 2017 and August 2023. Comprehensive genetic analyses, including chromosomal karyotyping, next-generation sequencing, and gene fusion assays, were performed on bone marrow samples obtained at initial diagnosis and relapse. Clinical data, treatment regimens, and patient outcomes were also documented. Mutations in core genes of FLT3, NPM1, DNMT3A, and IDH2 were frequently discovered in diagnostic sample and remained in relapse sample. FLT3-ITD, TP53, KIT, RUNX1, and WT1 mutation were acquired at relapse in one patient each. Gene fusion assays revealed stable patterns, while chromosomal karyotype analyses indicated a greater diversity of mutations in relapsed patients. Clonal evolution patterns varied, with some cases showing linear or branching evolution and others exhibiting no substantial change in core mutations between diagnosis and relapse. Our study integrates karyotype, gene rearrangements, and gene mutation results to provide a further understanding of AML heterogeneity and evolution. We demonstrate the clinical relevance of specific mutations and clonal evolution patterns, emphasizing the need for personalized therapies and measurable residual disease monitoring in AML management. By bridging the gap between genetics and clinical outcome, we move closer to tailored AML therapies and improved patient prognoses.

Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease.

Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition. We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy. AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified "fast cognitive decliners" among study participants who had AD. Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development. Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.

Bibliometric analysis of hotspots and trends of global myopia research.

To gain insights into the global research hotspots and trends of myopia. Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

Evaluation of the clinical use of MGMT methylation in extracellular vesicle-based liquid biopsy as a tool for glioblastoma patient management

Glioblastoma (GB) is a devastating tumor of the central nervous system characterized by a poor prognosis. One of the best-established predictive biomarker in IDH-wildtype GB is O6-methylguanine-DNA methyltransferase (MGMT) methylation (mMGMT), which is associated with improved treatment response and survival. However, current efforts to monitor GB patients through mMGMT detection have proven unsuccessful. Small extracellular vesicles (sEVs) hold potential as a key element that could revolutionize clinical practice by offering new possibilities for liquid biopsy. This study aimed to determine the utility of sEV-based liquid biopsy as a predictive biomarker and disease monitoring tool in patients with IDH-wildtype GB. Our findings show consistent results with tissue-based analysis, achieving a remarkable sensitivity of 85.7% for detecting mMGMT in liquid biopsy, the highest reported to date. Moreover, we suggested that liquid biopsy assessment of sEV-DNA could be a powerful tool for monitoring disease progression in IDH-wildtype GB patients. This study highlights the critical significance of overcoming molecular underdetection, which can lead to missed treatment opportunities and misdiagnoses, possibly resulting in ineffective therapies. The outcomes of our research significantly contribute to the field of sEV-DNA-based liquid biopsy, providing valuable insights into tumor tissue heterogeneity and establishing it as a promising tool for detecting GB biomarkers. These results have substantial implications for advancing predictive and therapeutic approaches in the context of GB and warrant further exploration and validation in clinical settings.

Alzheimer's Disease and Small Vessel Disease Differentially Affect White Matter Microstructure.

Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non-invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE-ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non-demented older adults. Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid β1-42 and p-Tau181 and MRI scans, including DTI scans. Longitudinal scans (mean follow-up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed-effects model for each tract. AD pathology, APOE-ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE-ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect. Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches.

IoMT Tsukamoto Type-2 fuzzy expert system for tuberculosis and Alzheimer’s disease

Accurate disease monitoring is an extremely time-consuming task for medical experts and technocrats involved, requiring technical support for diagnostic systems. To overcome this situation, we developed an Internet of Medical Things (IoMT) based on Tsukamoto Type 2 Fuzzy Inference System (TT2FIS) that can easily handle diagnostic and predictive aspects in the medical field. In the proposed system, we developed a Tsukamoto type 2 fuzzy inference system that takes the patient’s symptoms as input factors and the medical device as the output factor of the result. The aim of this work is to demonstrate the usefulness of type 2 fuzzy sets in Tuberculosis and Alzheimer’s disease diagnostic system. Numerical calculations are also performed to illustrate the applicability of the proposed method. A validation of the proposed derivation of the proposed IoMT model is also discussed in the results and conclusions section.

Soluble biomarkers for Neuromyelitis Optica Spectrum Disorders: a mini review

The Neuromyelitis Optica Spectrum Disorders (NMOSD) constitute a spectrum of rare autoimmune diseases of the central nervous system characterized by episodes of transverse myelitis, optic neuritis, and other demyelinating attacks. Previously thought to be a subtype of multiple sclerosis, NMOSD is now known to be a distinct disease with unique pathophysiology, clinical course, and treatment options. Although there have been significant recent advances in the diagnosis and treatment of NMOSD, the field still lacks clinically validated biomarkers that can be used to stratify disease severity, monitor disease activity, and inform treatment decisions. Here we review many emerging NMOSD biomarkers including markers of cellular damage, neutrophil-to-lymphocyte ratio, complement, and cytokines, with a focus on how each biomarker can potentially be used for initial diagnosis, relapse surveillance, disability prediction, and treatment monitoring.

Electrically Oriented Antibodies on Transistor for Monitoring Several Copies of Methylated DNA.

An antibody transistor is a promising biosensing platform for the diagnosis and monitoring of various diseases. Nevertheless, the low concentration and short half-life of biomarkers require biodetection at the trace-molecule level, which remains a challenge for existing antibody transistors. Herein, we demonstrate a graphene field-effect transistor (gFET) with electrically oriented antibody probes (EOA-gFET) for monitoring several copies of methylated DNA. The electric field confines the orientation of antibody probes on graphene and diminishes the distance between graphene and methylated DNAs captured by antibodies, generating more induced charges on graphene and amplifying the electric signal. EOA-gFET realizes a limit of detection (LoD) of ∼0.12 copy μL-1, reaching the lowest LoD reported before. EOA-gFET shows a distinguishable signal for liver cancer clinical serum samples within ∼6 min, which proves its potential as a powerful tool for disease screening and diagnosis.

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today’s culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. This review highlights the substantial influence of social media in advancing public health objectives. The key themes explored encompass the utilization of social media to advance health initiatives, monitor diseases, track behaviors, and interact with communities. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings show how social media has been used as a tool for research, implementing health campaigns, and health promotion. Social media integration with artificial intelligence (AI) and Generative Pre-Trained Transformers (GPTs) can impact and offer an innovative approach to tackle the problems and difficulties in health informatics. The research shows how social media will keep growing and evolving and, if used effectively, has the potential to help close public health gaps across different cultures and improve population health.

Synthesis, modifications, and applications of iron-based nanoparticles.

Magnetic nanoparticles (MNPs) are appealing materials as assistant to resolve environmental pollution issues and as recyclable catalysts for the oxidative degradation of resistant contaminants. Moreover, they can significantly influence the advancement of medical applications for imaging, diagnostics, medication administration, and biosensing. On the other hand, due to unique features, excellent biocompatibility, high curie temperatures and low cytotoxicity of the Iron-based nanoparticles, they have received increasing attention in recent years. Using an external magnetic field, in which the ferrite magnetic nanoparticles (FMNPs) in the reaction mixtures can be easily removed, make them more efficient approach than the conventional method for separating the catalyst particles by centrifugation or filtration. Ferrite magnetic nanoparticles (FMNPs) provide various advantages in food processing, environmental issues, pharmaceutical industry, sample preparation, wastewater management, water purification, illness therapy, identification of disease, tissue engineering, and biosensor creation for healthcare monitoring. Modification of FMNPs with the proper functional groups and surface modification techniques play a significant role in boosting their capability. Due to flexibility of FMNPs in functionalization and synthesis, it is possible to make customized FMNPs that can be utilized in variety of applications. This review focuses on synthesis, modifications, and applications of Iron-based nanoparticles.

Simplifying Alzheimer’s Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire

Simplifying Alzheimer’s Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire

Clinical research on neurological and psychiatric diagnosis and monitoring using wearable devices: A literature review

AbstractWearable devices have opened up exciting possibilities for monitoring and managing home health, particularly in the realm of neurological and psychiatric diseases. These devices capture signals related to physiological and behavioral changes, including heart rate, sleep patterns, and motor functions. Their emergence has resulted in significant advancements in the management of such conditions. Traditional clinical diagnosis and assessment methods heavily rely on patient reports and evaluations conducted by healthcare professionals, often leading to a detachment of patients from their home environment and creating additional burdens for both patients and healthcare providers. The increasing popularity of wearable devices offers a potential solution to these challenges. This review focuses on the clinical utility of wearable devices in diagnosing and monitoring neurological and psychiatric diseases. Through research findings and practical examples, we highlight the potential role of wearable devices in diagnosing and monitoring conditions such as autism spectrum disorder, depression, epilepsy, stroke prognosis, Parkinson's disease, dementia, and other diseases. Additionally, the review discusses the benefits and limitations of wearable devices in clinical applications, while highlighting the challenges they face. Finally, it provides prospects for enhancing the value of wearable devices in the monitoring and diagnosis of neurological and psychiatric diseases.

Non-enzymatic sensor based on doped polyindole/multi-walled carbon nanotube for detecting neurotransmitter acetylcholine

A facile non-enzymatic acetylcholine sensor composed of doped-polyindole/multi-walled carbon nanotube (dPIn-MWCNT) composite was designed, fabricated, and presented here. The negative current response toward acetylcholine concentration was first found and reported here. The confirmation of acetylcholine detection mechanism was elucidated by XPS, where the acetylcholine interaction resulted in a decrease in the % (dPIn doping level and an increase of the dPIn structural defect. The response optimizations with dPIn concentrations, applied potentials, and incubation times were systematically investigated. The 45dPIn-15MWCNT operated at + 0.6 V vs. Ag/AgCl with 5 min of incubation time was the optimum condition to obtain the highest acetylcholine current response. Chronoamperometric acetylcholine detection revealed two distinct current regimes from the acetylcholine surface interaction and the de-doping of dPIn. Regime I was related to the diffusion-controlled process as observed in the low concentration range of 10−8–10−5 M. Regime II corresponded to the mixed diffusion-controlled and adsorption-controlled process as observed in the concentration range of 10−5–10−2 M. For regime I, the calculated LOD was as low as 1.27 nM with a very high acetylcholine response sensitivity. Regime I is suggested for the point-of-care utility, where the detectable concentration range covered the acetylcholine level normally found in the healthy human blood. Regime II can be applicable and useful in the treatment monitoring of neurological diseases.

Deciphering Cognitive Impairments in Huntington's Disease: A Comparative Study of Stroop Test Variations.

Huntington's disease (HD) is a neurodegenerative disorder marked by cognitive impairment, movement abnormalities, and behavioral disturbances. The Stroop Color Word Test (SCWT) is a widely used tool to detect cognitive decline in HD. Variations in SCWT formats-horizontal (original) and vertical (Golden)-may influence performance, given HD's impact on cognitive and oculomotor abilities. This study aimed to compare the effectiveness of the horizontal and Golden vertical SCWT formats in detecting cognitive decline in HD, and to determine how performance may have been influenced by eye movement abnormalities. Forty-five participants with genetically confirmed HD were recruited. Both SCWT formats were administered to each participant in a counterbalanced fashion. Individual performance of all three sections on each format was standardized across 2 different norms. Raw and normed scores on each variation were compared and correlated with eye movement ratings on the Unified Huntington's Disease Rating Scale. The Golden variation elicited significantly slower responses, particularly in the Word Reading section, across two benchmark norms. Statistical analysis revealed significant performance differences between the two formats. Correlations between vertical eye movement ratings and performance on the Golden SCWT were highly significant, highlighting the impact of oculomotor coordination on cognitive assessments in HD. This study underscores the importance of considering test format in cognitive assessments for HD. The Golden vertical SCWT demonstrates increased sensitivity in detecting deficits in HD, possibly linked to vertical saccade abnormalities. These insights are important for improving the sensitivity of cognitive assessments and monitoring disease progression in HD research and clinical practice.

Research on Entity Relationships in the Knowledge Graph of Disease Monitoring in Grotto Temples

Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.