Severe Stage Research Articles

Clinical Profile of Thyroid Eye Disease and Factors Predictive of Disease Severity in Chennai District of Tamil Nadu

Abstract Background: Thyroid eye disease (TED) is a possibly sightthreatening ocular disease generally occurring in the patients diagnosed with hyperthyroidism. TED, also known as Graves orbitopathy, is typically self-limiting auto-immune disease linked with a dysthyroid status. Based on the ocular symptoms and signs, the current grading systems of TED are the VISA classification and the EUGOGO classification; each parameter of VISA score is separately graded and scored. Worsening in any of the VISA parameters is graded as an active disease in the geographic region of South India; there are no studies on clinical profiles of TED; hence, we planned to do this study to analyse the demographic profile and clinical manifestations of TED in patients. Material and Methodology: This study was done as an observational study for a period of 12 months. All patients with systemic thyroid disorder are included in the study. If lid retraction is seen, the presence of laboratory evidence of thyroid dysfunction, exophthalmos, optic nerve dysfunction, or extra ocular muscle involvement will be considered as TED. In case of no lid retraction in a patient with laboratory evidence of thyroid dysfunction, the presence of exophthalmos, optic nerve involvement, or restrictive myopathy will be considered as TED. Patients with diagnosis of TED were further evaluated. TED was graded according to VISA and EUGOGO classification. Results: This study was done as an observational study in 50 patients diagnosed with TED. In our study population, 32 (64%) were hyperthyroid, 11 (22%) were hypothyroidism, and the rest 7 (14%) were euthyroid. The most common presenting complaints were dryness of eye in 46% of patients, followed by blurred vision in 38% of patients. We evaluated severity of disease using EUGOGO score, and 42 patients had mild disease, while six had moderate disease and two patients had severe disease. Based on VISA score, in our study population, only two patients had active disease and the rest 48 had inactive disease. Conclusion: Thyroid status has a direct and positive correlation with active stage of disease and severity. Hyperthyroidism and active disease were risk factors for severe and sight-threatening disease, emphasising the need for close follow-up and prompt detection and management of severe and sight-threatening disease, especially in newly diagnosed hyperthyroid patients. It is important to diagnose TED at the earliest to prevent vision-threatening complications. Early identification and management will help in reducing the severity of disease.

Predicting Alzheimer's Disease Progression Using a Versatile Sequence-Length-Adaptive Encoder-Decoder LSTM Architecture.

Detecting Alzheimer's disease (AD) accurately at an early stage is critical for planning and implementing disease-modifying treatments that can help prevent the progression to severe stages of the disease. In the existing literature, diagnostic test scores and clinical status have been provided for specific time points, and predicting the disease progression poses a significant challenge. However, few studies focus on longitudinal data to build deep-learning models for AD detection. These models are not stable to be relied upon in real medical settings due to a lack of adaptive training and testing. We aim to predict the individual's diagnostic status for the next six years in an adaptive manner where prediction performance improves with the number of patient visits. This study presents a Sequence-Length Adaptive Encoder-Decoder Long Short-Term Memory (SLA-ED LSTM) deep-learning model on longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative archive. In the suggested approach, decoder LSTM dynamically adjusts to accommodate variations in training sequence length and inference length rather than being constrained to a fixed length. We evaluated the model performance for various sequence lengths and found that for inference length one, sequence length nine gives the highest average test accuracy and area under the receiver operating characteristic curves of 0.920 and 0.982, respectively. This insight suggests that data from nine visits effectively captures meaningful cognitive status changes and is adequate for accurate model training. We conducted a comparative analysis of the proposed model against state-of-the-art methods, revealing a significant improvement in disease progression prediction over the previous methods.

Diabetic Retinopathy and Vision Loss: A Public Health Concern

Diabetic Retinopathy (DR), a known complication of diabetes, is one of the top three leading causes of blindness in the U.S. Minorities are disproportionately affected and, most often, diagnosed at advanced stages. This review consolidates current epidemiological data, pathophysiological insights, clinical classifications, and treatment approaches for DR. Despite advancements in therapies like anti-VEGF drugs and laser treatments, significant disparities persist, particularly affecting racial minorities and those with lower socioeconomic status. Early detection via regular eye exams and improved healthcare access are crucial in mitigating DR's increasing global impact. Public health strategies must address these disparities and ensure inclusivity in clinical research to improve DR management and outcomes. Continued awareness of primary and secondary forms of prevention is crucial in all populations with DR, especially minorities, because progression to severe stages can be recognized, treated, and ameliorated. Although anti-vascular endothelial growth factors have advanced, further research is needed to aid understanding of DR in populations disproportionately affected by severe diseases.

A Study of the Prevalence of Diabetic Retinopathy in Patients With Ischemic Heart Disease and Diabetes Mellitus.

Background Diabetes mellitus is one of the most important and common chronic diseases worldwide and is expected to increase in prevalence. Diabetic retinopathy (DR) is one of the most prevalent microvascular sequelae of diabetes mellitus (DM), and ischemic heart disease is a macrovascular sequela. This study was conducted to find out the relation between the degree of DR and ischemic heart disease severity in Indian patients. Materials and methods This cross-sectional, descriptive, hospital-based study was conducted in the ophthalmology department at Dr. D. Y. Patil Medical Hospital, Pune, Maharashtra, India, from September 2022 to June 2024. A total of 200 eyes from 100 patients who were diagnosed with cases of ischemic heart disease and diabetes mellitus were included in the study. Patients with corneal pathology like endothelial dystrophies, corneal degenerations, corneal scars, or trauma preventing good visualization of the posterior segment were excluded from the study. Patients with active uveitis, patients with a history of undergoing any previous vitreoretinal surgery or laser procedures, non-compliant patients, patients not willing to undergo the procedure, or those not consenting to the study were also excluded. Written informed consent was obtained from each patient. Data was entered in Microsoft Exceland statistical analysis was done using IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. As the continuous variables showed a skewed distribution, we used the Mann-Whitney test and the Kruskal-Wallis test to test the significance of the difference between continuous and categorical variables. A chi-square test was employed to check the association between categorical variables. Significance was assumed at an alpha error of 5%. Results The prevalence of diabetic retinopathy was found to be 95%. The mean age of patients with DR and patients with no diabetic retinopathy was 58.38 and 59.40 years, respectively, with the majority of the patients being in the age group of 60-69 years (46%). The majority of the patients were males (65%), while 35% were females. There was a significant association between the severity of diabetic retinopathy and the higher HbA1c levels, the use of insulin as a treatment modality, and the higher blood sugar levels in our study population. It was observed that the patients in our study with an ejection fraction of <40% had significantly higher severity of diabetic retinopathy in the form of PDR and high-risk PDR. The severity of the DR was directly correlated with the severity of IHD in our study, with most of the IHD patients with a 40-60% ejection fraction having moderate NPDR and patients with a >60% ejection fraction having mild or moderate NPDR. Conclusion The prevalence of diabetic retinopathy among the IHD patients with diabetes was 95% in our study, with moderate NPDR being the most common stage of DR seen among the patients. It was observed that more severe stages of diabetic retinopathy were seen in patients who were on treatment with insulin than in patients who were on treatment with OHA. Severe stages of diabetic retinopathy were associated with higher blood sugar levels (BSL) and higher glycated hemoglobin levels. In the present study, it was observed that a lower ejection fraction (<40%), which is a marker of reduced cardiac function, was associated with more severe stages of diabetic retinopathy.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers.

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

Glaucoma Severity Stage at First Visit at a University Eye Clinic in Congo.

Initial presentation of glaucoma at a major eye clinic mostly features advanced disease with a high proportion of blindness. This is likely a microcosm of a nationwide issue requiring concerted strategies to detect glaucoma early. To characterize the severity stage of new glaucoma patients and identify the predictors of late presentation in Congolese attending a university eye clinic. New glaucoma patients (n=118) were labelled as early or late presenters based visual field sensitivity in the worse eye. Predictors of late presentation were determined through logistic regression analytical models. Mean age was 58.9±15.7 years, 51.7% were males. Overall, the worse eye had advanced whereas the better eye had moderate glaucoma, with an asymmetry of -7.4dB (P<0.001). Blindness was present in 30.5% of worse and 5.1% of better eyes. Seventy-two patients (61.0%) were late presenters. Visual acuity was lower (P<0.001), intraocular pressure (IOP) higher (P=0.02), cup-to-disc ratio larger (P=0.011), and retinal nerve fiber layer thinner (P=0.001) in late presenters' worse than better eye. The worse and better eyes of late presenters had advanced glaucoma, with a -9.6dB interocular asymmetry (P<0.001); 40.3% and 8.3% were blind, respectively. In early presenters, the worse and better eye had moderate and early disease, respectively; the asymmetry was -3.2dB (P<0.001), and 15.2% were blind. Overall, 58.5% and 65.3% presented with advanced disease in the worse eye based on visual field and cup-to-disc ratio criteria of the Canadian Ophthalmological Society staging scale, respectively. Most new glaucoma patients had bilateral advanced but asymmetric disease. These findings call for the establishment of community-based measures for early detection of glaucoma and a referral network system connecting community healthcare to tertiary eye clinics.

Circulating Markers of Necroptosis in Acute Pancreatitis

ObjectivesNecroptosis, a programmed inflammatory cell death, is involved in the pathogenesis of acute pancreatitis (AP). We compared levels of interleukin (IL)-33 (released upon necroptosis), sST2 (soluble IL-33 receptor), MLKL, RIPK1 and RIPK3 (necroptosis executioner proteins), and proinflammatory cytokines IL-6, TNF and IL-1β at various severity categories and stages of AP.MethodsPlasma from 20 patients with early mild AP (MAP) (symptom onset < 72 h), 7 with severe AP (SAP) without and 4 with persistent organ failure (OF) at sampling, 8 patients with late SAP and 20 healthy controls (HC) were studied by ELISAs.ResultsEarly sST2 and IL-6 levels predicted the development of SAP and were higher in both MAP and early and late SAP than in HC. RIPK3 levels were higher than in HC in the patients who had or would later have SAP. MLKL levels were associated with the presence of OFs, particularly in the late phase, but were also higher in MAP than in HC.ConclusionssST2, RIPK3 and IL-6 levels may have prognostic value in AP. Elevated MLKL levels are associated with OF in AP. Better understanding of necroptosis in AP pathophysiology is needed to evaluate whether inhibiting and targeting necroptosis is a potential therapeutic option in AP.

Developing heart stroke prediction model using deep learning with combination of fixed row initial centroid method with Navie Bayes, Decision Tree, and Artificial Neural Network

The present research and study, aimed to develop a new predictive model that easily navigate to the challenges of risk factors causing a heart stroke and accurately detect the early chances of having the stroke. Knowledge of risk factors impacting in the deterioration of a patient's health in causing the severity of heart stroke, helps future research work in prognosis of heart stoke and implement feature selection techniques by considering all such risk factors. Clustering of binary classes perfectly, without any noise, is a major advantage in predicting heart stroke patients' condition in their early stages of severity. Novel initial centroid selection method FRM is developed which considered the best until date resulting in 100 % clustering of patients into binary classes, which significantly contribute to the enhancement of accuracy results. Major objective is integrating clustering technique with classification algorithms Naïve Bayes, Decision Tree and Artificial Neural Network and built three prediction systems, FRM-NB, FRM-DT, and FRM-ANN performed the best when compared to existing systems, resulting in the best accuracy values in predicting early risk of heart stroke and reducing chances of recurrent heart strokes. We have achieved the best accuracy of 94 % (FRM-NB) and 97 % (FRM-DT), sensitivity of 90 % (FRM-NB) and 95 % (FRM-DT), specificity score of 97 % (FRM-NB) AND 90 % (FRM-DT) and AUC-ROC score of 0.976 (FRM-NB) and 0.953(FRM-DT). FRM-ANN model achieved an accuracy of 98 % with 100 % sensitivity and 0.99 score of AUC, which till date no existing research has achieved.

Comprehensive analysis of clinical outcomes, infectious complications and microbiological data in newly diagnosed multiple myeloma patients: a retrospective observational study of 92 subjects

Patients with multiple myeloma (MM) have an increased risk of sepsis due to underlying disease- and treatment-related immunosuppression. However, data on sepsis incidence, causative pathogens, and impact on outcomes in newly diagnosed MM (NDMM) are limited. We conducted a retrospective observational study of 92 NDMM patients who developed sepsis between 2022 and 2023 at a tertiary care center in Italy. Patient characteristics, sepsis criteria [Quick Sequential Organ Failure Assessment, Systemic Inflammatory Response Syndrome (SIRS)], microbiology results, and associations with progression-free survival (PFS) were analyzed. In this cohort of 92 critically-ill patients, pathogenic organisms were identified via microbiological culture in 74 cases. However, among the remaining 18 culture-negative patients, 9 exhibited a SIRS score of 2 and another 9 had a SIRS score of 4, suggestive of a clinical presentation consistent with sepsis despite negative cultures. Common comorbidities included renal failure (60%), anemia (71%), and bone disease (83%). Gram-negative (28%) and Gram-positive (23%) bacteria were frequent causative organisms, along with fungi (20%). Cox Univariate analyses for PFS showed statically significant HR in patients with albumin ≥ 3.5 vs < 3.5 (HR = 5.04, p < 0.001), Karnofsky performance status ≥ 80 vs < 80 (HR = 2.01, p = 0.002), and early-stage vs late-stage disease by International Staging System (HR = 4.76 and HR = 12.52, both p < 0.001) and Revised International Staging System (R-ISS III vs R-ISS I, HR = 7.38, p < 0.001). Sepsis is common in NDMM and associated with poor outcomes. Risk stratification incorporating sepsis severity, comorbidities, and disease stage may help guide preventive strategies and optimize MM management.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images

BackgroundChronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning.MethodsThe retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier.Results104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87.ConclusionsThe proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

Prevalence Estimation of Dementia/Alzheimer’s Disease Using Health and Retirement Study Database in the United States

BackgroundUpdated prevalence estimates along the continuum of Alzheimer’s disease (AD) can foster a more nuanced and effective approach to managing AD within the current healthcare landscape.ObjectivesThis study aims to estimate the prevalence and severity distribution of dementia/AD (including mild, moderate, and severe stages) and all-cause mild cognitive impairment (MCI) in the United States using data from the Health and Retirement Study (HRS).DesignRetrospective study.SettingData from the bi-annual HRS surveys involving in-depth interviews of a representative sample of Americans aged >50 years.ParticipantsDementia/AD and all-cause MCI patients from the 4 most recent HRS surveys (2014, 2016, 2018 and 2020).MeasurementsAD was identified based on diagnosis (self-report). Cognitive performance (modified Telephone Interview of Cognitive Status [TICS-m]) scores in the dementia/AD range were also captured; all-cause MCI was similarly identified using the TICS-m. Dementia/AD and MCI prevalence, as well as the distribution by dementia/AD stage (mild, moderate, or severe), were estimated. Sampling weights developed by HRS were applied to ensure the sample’s representativeness of the target population and unbiased estimates for population parameters.ResultsAcross the four HRS surveys, the total number of HRS respondents ranged from 15,000 to 21,000 (unweighted); 7,000 to 14,000 had TICS-m scores. The estimated prevalence of AD (all severity categories combined) in the 2014, 2016, 2018, and 2020 HRS surveys was 1.2%, 1.2%, 1.3% and 1.0%, respectively using the diagnosis-based approach; using the cognitive performance-based approach, 23–27% patients had scores in the dementia/AD ranges across the 4 surveys. The estimated prevalence of all-cause MCI was consistently 23% in each survey. In the 2020 survey, the distribution of mild, moderate, and severe disease stages was 34%, 45%, and 21%, respectively, in patients self-reporting an AD diagnosis, and 55%, 40%, and 5%, respectively in all patients meeting TICS-m threshold for dementia/AD.ConclusionThe prevalence of AD diagnosis based on self-report was approximately 1% across the 4 most recent HRS surveys and may reflect the proportion of patients who have actively sought healthcare for AD. Among HRS survey respondents with cognitive scores available, over 20% were in the dementia/AD range. The distribution of disease by stage differed for self-reported AD diagnosis vs dementia/AD based on cognitive scores. Discordance in estimates of dementia/AD and stage distributions underscores a need for better understanding of clinical practice patterns in AD diagnosis, use of clinical assessment tools, and severity classification in the United States. Accurate patient identification is needed, especially early in the AD disease continuum, to allow for timely and appropriate initiation of new anti-amyloid treatments.

Using Thermography Infrared to Detect Diabetic Foot Ulcers in Asia Pacific Countries: Scoping Review

Introduction: Diabetes Mellitus (DM) is a growing global health concern, particularly in Indonesia, where the prevalence is projected to rise significantly. Diabetic Foot Ulcer (DFU) is a severe complication of DM that escalates the risk of amputations. Early detection of DFUs plays a crucial role in preventing associated complications Objective: This review aimed to assess the efficacy of infrared thermography in detecting DFUs in the Asia-Pacific region. Methods: A scoping review methodology adhering to PRISMA guidelines was employed. Databases were searched from 2011-2021. PICO strategy used keyword Diabetic foot ulcer, Skin ulcer, Leg ulcer, Foot ulcer, Diabetic neuropathic, Thermography, Temperature mapping, and Detection from PubMed, Scopus, Google Scholar, and Science Direct. Quality of article assessed by JBI. Results: Four (4) studies meeting the inclusion criteria were identified, primarily cross-sectional in design. The results indicated promising sensitivity and specificity of infrared thermography in detecting DFUs among DM patients. The technology's ability to capture thermal images revealing temperature variations associated with tissue inflammation, a common DFU indicator, underscores its efficacy. This non-invasive technique allows for early detection and monitoring of DFUs, facilitating timely interventions and improving patient outcomes. Additionally, infrared thermography provides a quantitative and objective assessment of tissue health, complementing traditional visual inspection and palpation methods. Its effectiveness lies in detecting subtle skin temperature changes, aiding in early identification and management of DFUs before they progress to more severe stages. Conclusion: Infrared thermography shows significant potential in detecting DFUs early, which is vital for effective management and prevention of severe complications. Despite the promising results, broader and more rigorous studies are necessary to validate these findings across different populations and healthcare settings. Further research across diverse settings and study designs is essential to reinforce these findings.

The Assessment of the Association of Proton Pump Inhibitor Usage with Chronic Kidney Disease Progression through a Process Mining Approach.

Previous studies have suggested an association between Proton Pump Inhibitors (PPIs) and the progression of chronic kidney disease (CKD). This study aims to assess the association between PPI use and CKD progression by analysing estimated glomerular filtration rate (eGFR) trajectories using a process mining approach. We conducted a retrospective cohort study from 1 January 2006 to 31 December 2011, utilising data from the Stockholm Creatinine Measurements (SCREAM). New users of PPIs and H2 blockers (H2Bs) with CKD (eGFR < 60) were identified using a new-user and active-comparator design. Process mining discovery is a technique that discovers patterns and sequences in events over time, making it suitable for studying longitudinal eGFR trajectories. We used this technique to construct eGFR trajectory models for both PPI and H2B users. Our analysis indicated that PPI users exhibited more complex and rapidly declining eGFR trajectories compared to H2B users, with a 75% increased risk (adjusted hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.49 to 2.06) of transitioning from moderate eGFR stage (G3) to more severe stages (G4 or G5). These findings suggest that PPI use is associated with an increased risk of CKD progression, demonstrating the utility of process mining for longitudinal analysis in epidemiology, leading to an improved understanding of disease progression.

Prevalence and severity of periodontal disease in the host community and Rohingya refugees living in camps in Bangladesh.

To assess the prevalence and severity of periodontal disease of the Rohingya refugees and host community in Bangladesh. An unpublished pilot was conducted for the sample size calculation. Two-stage cluster sampling method was used to select 50 participants from refugee camps and 50 from the host community. Structured questionnaire and periodontal examination were completed. Composite measures of periodontal disease were based on the World Workshop (WW) and Centers for Disease Control and Prevention-American Academy of Periodontology. Linear regression models, for clinical attachment level and periodontal pocket depth (PPD) and ordered logistic regression models, for composite measures, were fitted to test the association of periodontal measures and refugee status. Compared to the host community, a smaller percentage of refugees reported good oral health-related behaviours. Refugees exhibited lower levels of bleeding on probing but higher PPD, hence a higher proportion had severe stages of periodontitis. As per the WW, prevalence of periodontal disease was 88% and 100% in the host and refugee groups, respectively. In the unadjusted models, refugees were three times more likely to have severe stages of periodontitis; this association was attenuated when adjusted for confounders (sociodemographic variables and oral health-related behaviours). Prevalence of periodontitis was high both in the host community and refugees. The refugees exhibited a more severe disease profile. The oral health of both groups is under-researched impacting the response of the health system. Large-scale research systematically exploring the oral health of both groups will inform the design and delivery of community-based interventions.

Unveiling metabolic patterns in dementia: Insights from high-resolution quantitative blood-oxygenation-level-dependent MRI.

Oxygen extraction fraction (OEF) and deoxyhemoglobin (DoHb) levels reflect variations in cerebral oxygen metabolism in demented patients. Delineating the metabolic profiles evident throughout different phases of dementia necessitates an integrated analysis of OEF and DoHb levels. This is enabled by leveraging high-resolution quantitative blood oxygenation level dependent (qBOLD) analysis of magnitude images obtained from a multi-echo gradient-echo MRI (mGRE) scan performed on a 3.0 Tesla scanner. Achieving superior spatial resolution in qBOLD necessitates the utilization of an mGRE scan with only four echoes, which in turn limits the number of measurements compared to the parameters within the qBOLD model. Consequently, it becomes imperative to discard non-essential parameters to facilitate further analysis. This process entails transforming the qBOLD model into a format suitable for fitting the log-magnitude difference (L-MDif) profiles of the four echo magnitudes present in each brain voxel. In order to bolster spatial specificity, the log-difference qBOLD model undergoes refinement into a representative form, termed as r-qBOLD, particularly when applied to class-averaged L-MDif signals derived through k-means clustering of L-MDif signals from all brain voxels into a predetermined number of clusters. The agreement between parameters estimated using r-qBOLD for different cluster sizes is validated using Bland-Altman analysis, and the model's goodness-of-fit is evaluated using a -test. Retrospective MRI data of Alzheimer's disease (AD), mild cognitive impairment (MCI), and non-demented patients without neuropathological disorders, pacemakers, other implants, or psychiatric disorders, who completed a minimum of three visits prior to MRI enrolment, are utilized for the study. Utilizing a cohort comprising 30 demented patients aged 65-83 years in stages 4-6 representing mild, moderate, and severe stages according to the clinical dementia rating (CDR), matched with an age-matched non-demented control group of 18 individuals, we conducted joint observations of OEF and DoHb levels estimated using r-qBOLD. The observations elucidate metabolic signatures in dementia based on OEF and DoHb levels in each voxel. Our principal findings highlight the significance of spatial patterns of metabolic profiles (metabolic patterns) within two distinct regimes: OEF levels exceeding the normal range (S1-regime), and OEF levels below the normal range (S2-regime). The S1-regime, accompanied by low DoHb levels, predominantly manifests in fronto-parietal and perivascular regions with increase in dementia severity. Conversely, the S2-regime, accompanied by low DoHb levels, is observed in medial temporal (MTL) regions. Other regions with abnormal metabolic patterns included the orbitofrontal cortex (OFC), medial-orbital prefrontal cortex (MOPFC), hypothalamus, ventro-medial prefrontal cortex (VMPFC), and retrosplenial cortex (RSP). Dysfunction in the OFC and MOPFC indicated cognitive and emotional impairment, while hypothalamic involvement potentially indicated preclinical dementia. Reduced metabolic activity in the RSP suggested early-stage AD related functional abnormalities. Integrated analysis of OEF and DoHb levels using r-qBOLD reveals distinct metabolic signatures across dementia phases, highlighting regions susceptible to neuronal loss, vascular involvement, and preclinical indicators.

Oral alkalinizing supplementation suppressed intrarenal reactive oxidative stress in mild-stage chronic kidney disease: a randomized cohort study.

The beneficial effects of oral supplements with alkalinizing agents in patients with chronic kidney disease (CKD) have been limited to the severe stages. We investigated whether two types of supplements, sodium bicarbonate (SB) and potassium citrate/sodium citrate (PCSC), could maintain renal function in patients with mild-stage CKD. This was a single-center, open-labeled, randomized cohort trial. Study participants with CKD stages G2, G3a, and G3b were enrolled between March 2013 and January 2019 and randomly assigned by stratification according to age, sex, estimated glomerular filtration rate (eGFR), and diabetes. They were followed up for 6months (short-term study) for the primary endpoints and extended to 2years (long-term study) for the secondary endpoints. Supplementary doses were adjusted to achieve an early morning urinary pH of 6.8-7.2. We observed renal dysfunction or new-onset cerebrovascular disease and evaluated urinary surrogate markers for renal injury. Overall, 101 participants were registered and allocated to three groups: standard (n = 32), SB (n = 34), and PCSC (n = 35). Two patients in the standard group attained the primary endpoints (renal stones and overt proteinuria) but were not statistically significant. There was one patient in the standard reduced eGFR during the long-term study (p = 0.042 by ANOVA). SB increased proteinuria (p = 0.0139, baseline vs. 6months), whereas PCSC significantly reduced proteinuria (p = 0.0061, baseline vs. 1year, or p = 0.0186, vs. 2years) and urinary excretion of 8-hydroxy-2'-deoxyguanosine (p = 0.0481, baseline vs. 6months). This study is the first to report supplementation of PCSC reduced intrarenal oxidative stress in patients with mild-stage CKD.

Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data

X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson’s Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.

Detecting severity of Diabetic Retinopathy from fundus images: A transformer network-based review

Diabetic Retinopathy (DR) is considered one of the significant concerns worldwide, primarily due to its impact on causing vision loss among most people with diabetes. The severity of DR is typically comprehended manually by ophthalmologists from fundus photography-based retina images. This paper deals with an automated understanding of the severity stages of DR. In the literature, researchers have focused on this automation using traditional machine learning-based algorithms and convolutional architectures. However, the past works hardly focused on essential parts of the retinal image to improve the model performance. In this study, we adopt and fine-tune transformer-based learning models to capture the crucial features of retinal images for a more nuanced understanding of DR severity. Additionally, we explore the effectiveness of image transformers to infer the degree of DR severity from fundus photographs. For experiments, we utilized the publicly available APTOS-2019 blindness detection dataset, where the performances of the transformer-based models were quite encouraging.

Prognostic Assessment of HLM Score in Heart Failure Due to Ischemic Heart Disease: A Pilot Study.

Background: Ischemic heart disease (IHD) represents the main cause of heart failure (HF). A prognostic stratification of HF patients with ischemic etiology, particularly those with acute coronary syndrome (ACS), may be challenging due the variability in clinical and hemodynamic status. The aim of this study is to assess the prognostic power of the HLM score in a population of patients with ischemic HF and in a subgroup who developed HF following ACS. Methods: This is an observational, prospective, single-center study, enrolling consecutive patients with a diagnosis of ischemic HF. Patients were stratified according to the four different HLM stages of severity, and the occurrence of CV death, HFH, and worsening HF events were evaluated at 6-month follow-up. A sub-analysis was performed on patients who developed HF following ACS at admission. Results: The study included 146 patients. HLM stage predicts the occurrence of CV death (p = 0.01) and CV death/HFH (p = 0.003). Cox regression analysis confirmed HLM stage as an independent predictor of CV death (OR: 3.07; 95% IC: 1.54-6.12; p = 0.001) and CV death/HFH (OR: 2.45; 95% IC: 1.43-4.21; p = 0.001) in the total population of patients with HF due to IHD. HLM stage potentially predicts the occurrence of CV death (p < 0.001) and CV death/HFH (p < 0.001) in patients with HF following ACS at admission. Conclusions: Pathophysiological-based prognostic assessment through HLM score is a potentially promising tool for the prediction of the occurrence of CV death and CV death/HFH in ischemic HF patients and in subgroups of patients with HF following ACS at admission.

Development and Validation of Paradigms Based on the Global-First Topological Approach for Alzheimer's Disease Severity Staging.

Conventional methods like patient history, neuropsychological testing, cerebrospinal fluid examination, and magnetic resonance imaging are widely used to diagnose cases in the current clinical setting but are limited in classifying Alzheimer's disease (AD) stages. Patients with AD exhibit visual perception deficits, which may be a potential target to assess the severity of the disease according to visual paradigms. However, owing to the inconsistent forms of perceived objects, the defects of current visual processing paradigms often lead to inconsistent results and a lack of sensitivity and specificity. We develop two paradigms based on global-first topological approach of visual perception, which avoids inconsistent results and lack of sensitivity and specificity owing to the inconsistent forms of perceived objects in traditional paradigms, delineate a unique detection strategy from perception organization (Experiment 1) and visual working memory (VWM) (Experiment 2). Except for the significant differences of the reaction times (RTs) between groups, significant differences were found when AD subjects recognize small figures due to the consistency of global and local figures in similarity test. The difference of RTs between recognizing global and local figures can be recognized in AD and mild cognitive impairment (MCI) group compared to healthy elderly (HE) in similarity test (Experiment 1). The memory capacity of AD patients was significantly lower than MCI group. Topological interference effect was observed in MCI and HE group, whereas MCI patients may have a greater difference trend in non-topological and topological changes than HE (Experiment 2). Our paradigms provide a new strategy, which can assist clinical severity staging and linking topological approach of visual perception with pathophysiological processes in AD.