Track Disease Progression Research Articles

Incorporating intrathoracic pressure fluctuation can improve noninvasive prediction of left ventricular end-diastolic pressure

Abstract Introduction Heart failure (HF) is a leading cause of mortality and morbidity, with a prevalence above 10% in elderly population (1). Left ventricular end-diastolic pressure (LVEDP) is a known indicator for all types of HF. An invasive pressure catheter is the gold standard for measuring LVEDP (2). However, it involves an in-hospital procedure and is costly, which limits the accessibility. A noninvasive strategy would improve early diagnosis and reduce the burden on secondary care. Several noninvasive classifiers are able to detect elevated LVEDP (3-5), with some receiving FDA clearance (6). While beneficial for triaging patients, these classifiers cannot quantify the severity and track disease progression. Developing a noninvasive LVEDP regression model requires accounting for respiration dynamics. Even invasively, the dynamics make measuring LVEDP challenging. This study uses an error metric from ANSI/AAMI/ISO 81060-2:2019 (AAMI) (7) to evaluate the effect of respiration on noninvasive LVEDP regression results. Methods The dataset (147 patients, 21+ yrs old, mean 65 ± 10 yrs; 68% male) consisted of patients referred for nonemergent left heart catheterisation inclusive of direct assessment of LVEDP. Noninvasive data was collected by a modified brachial cuff with a single-lead electrocardiogram (ECG). Invasive data was acquired using a Millar pressure catheter. LVEDP was derived based on the time of the R-wave of the ECG (8). For each patient, 40 secs of noninvasive data with synchronised catheter signals were analysed to build an LVEDP regression model. Inputs were generated from the ECG and brachial pressure waves based on fiducials identified in both signals. Leave-one-subject-out cross-validation (LOSO CV) was used to train and test the algorithm. The impact of respirophasic variation on the model was analysed by comparing the prediction with the mean LVEDP and the range of the mean ± standard deviation (std) (i.e. AAMI error metric). Results The results, evaluated using Bland-Altman and correlation analysis, showed a decrease in the mean absolute error (MAE) from 3.6 mmHg to 1.4 mmHg, the std from 4.9 mmHg to 3.0 mmHg, and the correlation (r) from 0.57 to 0.77 when incorporating intrathoracic pressure swings (Fig.1 and Fig.2). A Levene test on the residuals of the two error determinations showed a significant difference (p-value < 0.01). Conclusion Irrespective of the error metric, the model performance meets the AAMI standard for blood pressure (BP) measurement. Moreover, when respiratory motion is included, agreement with invasive LVEDP improves considerably. From a clinical perspective, this emphasises the importance of targeting end-expiratory LVEDP. With respect to noninvasive BP measurement, it suggests that both brachial BP evaluation and LVEDP prediction should attempt to quantify fluctuations. While the challenge remains, these results indicate that noninvasive LVEDP prediction could be realised in the near future.Fig.1 Prediction vs mean LVEDP.Fig. 2 Prediction vs mean +/- std.

Enhancing chronic disease management through physical activity and pedometry-based health monitoring.

Physical activity (PA) is a vital component in the management of chronic diseases, including cardiovascular, metabolic, and musculoskeletal conditions. In rheumatic diseases (RDs) such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and osteoarthritis (OA), PA has been shown to alleviate symptoms, enhance functional capacity, and improve quality of life. Given these advantages, wearable trackers and smartphone apps have revolutionized PA monitoring, offering quantitative reports of step counts, sedentary behavior, intensity, and energy expenditure. Pedometry-based health monitoring aids in evaluating patients' adherence to exercise regimens, tracking disease progression, and customizing interventions to meet individual needs. Patients with RDs often encounter barriers to maintaining regular PA, including joint pain, fatigue, and comorbidities, which complicate adherence and elevate the risk of adverse events. This overview delves into the dual role of PA in fostering health and managing disease in RD patients. Despite enormous benefits, it is essential to recognize limitations and risks of PA, particularly for individuals with high disease burden and multiple comorbidities. The findings emphasize the importance of integrating tailored PA programs within a multidisciplinary care framework to optimize patient outcomes.

Delineating three distinct spatiotemporal patterns of brain atrophy in Parkinson's disease.

The clinical manifestation of Parkinson's disease exhibits significant heterogeneity in the prevalence of non-motor symptoms and the rate of progression of motor symptoms, suggesting that Parkinson's disease can be classified into distinct subtypes. In this study, we aimed to explore this heterogeneity by identifying a set of subtypes with distinct patterns of spatiotemporal trajectories of neurodegeneration. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that combined disease progression modelling with clustering methods, to cortical and subcortical neurodegeneration visible on 3 T structural MRI of a large cross-sectional sample of 504 patients and 279 healthy controls. Serial longitudinal data were available for a subset of 178 patients at the 2-year follow-up and for 140 patients at the 4-year follow-up. In a subset of 210 patients, concomitant Alzheimer's disease pathology was assessed by evaluating amyloid-β concentrations in the CSF or via the amyloid-specific radiotracer 18F-flutemetamol with PET. The SuStaIn analysis revealed three distinct subtypes, each characterized by unique patterns of spatiotemporal evolution of brain atrophy: neocortical, limbic and brainstem. In the neocortical subtype, a reduction in brain volume occurred in the frontal and parietal cortices in the earliest disease stage and progressed across the entire neocortex during the early stage, although with relative sparing of the striatum, pallidum, accumbens area and brainstem. The limbic subtype represented comparative regional vulnerability, which was characterized by early volume loss in the amygdala, accumbens area, striatum and temporal cortex, subsequently spreading to the parietal and frontal cortices across disease stage. The brainstem subtype showed gradual rostral progression from the brainstem extending to the amygdala and hippocampus, followed by the temporal and other cortices. Longitudinal MRI data confirmed that 77.8% of participants at the 2-year follow-up and 84.0% at the 4-year follow-up were assigned to subtypes consistent with estimates from the cross-sectional data. This three-subtype model aligned with empirically proposed subtypes based on age at onset, because the neocortical subtype demonstrated characteristics similar to those found in the old-onset phenotype, including older onset and cognitive decline symptoms (P < 0.05). Moreover, the subtypes correspond to the three categories of the neuropathological consensus criteria for symptomatic patients with Lewy pathology, proposing neocortex-, limbic- and brainstem-predominant patterns as different subgroups of α-synuclein distributions. Among the subtypes, the prevalence of biomarker evidence of amyloid-β pathology was comparable. Upon validation, the subtype model might be applied to individual cases, potentially serving as a biomarker to track disease progression and predict temporal evolution.

MicroRNAs as Biomarkers in Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is a severe neurodegenerative disease caused by the loss of the survival motor neuron (SMN) protein, leading to degeneration of anterior motor neurons and resulting in progressive muscle weakness and atrophy. Given that SMA has a single, well-defined genetic cause, gene-targeted therapies have been developed, aiming to increase SMN production in SMA patients. The SMN protein is likely involved in the synthesis of microRNAs (miRNAs), and dysregulated miRNA expression is increasingly associated with the pathophysiology of SMA. Currently, there is a lack of reliable biomarkers to monitor SMA; therefore, the search for novel SMA biomarkers, including miRNAs, is crucial as reliable tools are needed to track disease progression, predict the response to therapy and understand the different clinical outcomes of available treatments. In this review, we compile data on miRNAs associated with SMA pathogenesis and their potential use as biomarkers. Based on current knowledge, the most frequently deregulated miRNAs between SMA patients and controls, as well as pre- and post-treatment in SMA patients, include miR-1-3p, miR-133a-3p, miR-133b, and miR-206. These findings offer promising possibilities for improving patient classification and monitoring disease progression and response to treatment. Additionally, these findings provide insights into the broader molecular mechanisms and networks of SMA that could inform the development of future therapeutic strategies.

Iron accumulation/overload and Alzheimer's disease risk factors in the precuneus region: A comprehensive narrative review

AbstractAlzheimer's disease (AD) is a neurodegenerative disease that is characterized by amyloid plaques, neurofibrillary tangles, and neuronal loss. Early cerebral and body iron dysregulation and accumulation interact with AD pathology, particularly in the precuneus, a crucial functional hub in cognitive functions. Quantitative susceptibility mapping (QSM), a novel post‐processing approach, provides insights into tissue iron levels and cerebral oxygen metabolism and reveals abnormal iron accumulation early in AD. Increased iron deposition in the precuneus can lead to oxidative stress, neuroinflammation, and accelerated neurodegeneration. Metabolic disorders (diabetes, non‐alcoholic fatty liver disease (NAFLD), and obesity), genetic factors, and small vessel pathology contribute to abnormal iron accumulation in the precuneus. Therefore, in line with the growing body of literature in the precuneus region of patients with AD, QSM as a neuroimaging method could serve as a non‐invasive biomarker to track disease progression, complement other imaging modalities, and aid in early AD diagnosis and monitoring.

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

B-241 Next-generation monitoring: M-inSightclinical application in multiple myeloma

Abstract Background The improvement of multiple myeloma (MM) therapy effectiveness has increased the need for precise, sensitive, and dynamic monitoring of minimal residual disease (MRD). Current MRD assessment methods that are based on bone marrow aspirates, pose limitations due to the invasiveness of the procedure and potential heterogeneous sampling. Recognizing the need for non-invasive, frequent testing, a targeted mass spectrometry, M-InSight®, (MS)-based MRD blood-test was developed. This innovative assay identifies and tracks clonotypic peptides from the patient's monoclonal immunoglobulin, offering a blood-based alternative with equivalent sensitivity to existing bone marrow reliant techniques.Here we employed M-InSight® to sequence and select clonotypic peptides, enabling highly specific, ultra-sensitive quantification of the M-protein. This study examines the correlation between M-protein quantification using M-InSight® and conventional serum protein electrophoresis (SPEP) values routinely obtained in clinical settings. Given the established role of M-protein as a biomarker for MM diagnosis and longitudinal monitoring, ensuring concordance between emerging methodologies and existing standards is vital. Methods Serum samples were obtained from the clinical laboratory at the University of Miami and characterized using SPEP (capillary electrophoresis, Sebia), immunotyping (Sebia), and free light chain quantitation (The binding site). Patients with M-protein concentrations ≥0.2 g/dL by SPEP were included. Longitudinal samples were collected. Subsequently, all samples were analyzed using M-InSight®. The initial sample for each patient underwent de novo sequencing of the M-protein by mass spectrometry and was also used to calibrate the concentration with the known value from SPEP. An in-house bioinformatics algorithm aligned, sequenced, and selected patient-specific clonotypic peptides. These peptides were targeted for the M-inSight® follow up assay allowing the quantification of M-protein. Results A variety of M-protein isotypes were successfully sequenced (IgGK, IgGL, IgAK, IgAL, IgMK, IgML, free K, free L, IgDL, biclonal) at concentrations ranging from 0.2 to 2.9 g/dL. M-protein was successfully sequenced and clonotypic peptides were identified in 57 out of 59 patients using the initial sample. A total of 109 serum samples collected during maintenance or post treatment were analyzed, 50 of them were follow-up samples and were used to correlate M-protein values from both techniques. M-protein quantification by M-Insight showed high concordance with the one obtained by SPEP, with a correlation value of 0.88 (intercept set to 0, slope 0.91. Pearson = 0.886, p-value &amp;lt;0.001). Out of 57 patients whose M-protein was sequenced, 55 were positive by SPEP and were successfully quantified by M-InSight®. Furthermore, M-InSight® detected and quantified M-protein on samples of the 2 remaining patients that showed M-protein values below SPEP detection limits, highlighting the higher sensitivity of M-InSight®. Conclusions M-inSight® assay successfully identified clonotypic peptides in 57 out of 59 patients which were used to quantify M-protein concentration and showed a strong concordance with SPEP and affirming the utility of M-InSight® as a reliable method for routine monitoring of MM. The ability to seamlessly integrate M-InSight® into routine monitoring protocols offers clinicians a valuable tool for MRD monitoring, tracking disease progression and response to therapy with enhanced sensitivity and specificity.

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device.

Freezing of Gait (FOG) is prevalent in people with Parkinson's disease (PD) and severely disrupts mobility. Detecting the exact boundaries of FOG episodes may facilitate new technologies in "breaking" FOG in real-time. This study investigates the performance of automatic device-based FOG detection. Eight machine-learning classifiers (including Neural Networks, Ensemble methods, and Support Vector Machines) were developed using (i) accelerometer and (ii) combined accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed mobility tasks designed to elicit FOG. Two clinicians independently annotated exact FOG episodes using synchronized video and a flowchart algorithm based on international guidelines. Device-detected FOG episodes were compared to annotated episodes using 10-fold cross-validation and Interclass Correlation Coefficients (ICC) for agreement. Development used 50,962 windows of data and annotated activities (>10 hours). Strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity, and ICC1,1 = 0.97 for total FOG duration). Device performance varied by method, complexity, and cost matrix. The Neural Network using 67 accelerometer features achieved high sensitivity to FOG (89% sensitivity, 81% specificity, and ICC1,1 = 0.83) and stability (validation loss 5%). The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The strong clinician agreement indicates room for improvement in future device-based FOG detection. This study may enhance PD care by reducing reliance on visual FOG inspection, demonstrating that high sensitivity in automatic FOG detection is achievable.

A Review on Asthma and Allergy: Current Understanding on Molecular Perspectives.

Asthma, a complex disease characterized by persistent airway inflammation, remains an urgent global health concern. We explored the critical role of allergic biomarkers and dysregulated immune system in asthma through an extensive literature review in databases such as Web of Science, PubMed, EMBASE, Scopus, and Google Scholar. This review summarizes the growing data on the pivotal role of allergic biomarkers and dysregulated immune system in the development and evolution of asthma. Recent studies have uncovered several biomarkers that elucidate intrinsic allergic mechanisms in individuals with asthma. This article highlights these biomarkers' potential in predicting asthma onset, assessing its intensity, guiding therapeutic interventions, and tracking disease progression. We also explore the innovative therapeutic prospects arising from the convergence of allergy and dysregulated immune system in asthma and emphasize the potential for precision medicine approaches. Understanding allergic biomarkers intertwined with a dysregulated immune system heralds a new era in asthma treatment and points to improved and individualized treatment modalities.

Glymphatic System Dysfunction in Congenital Sensorineural Hearing Loss: A DTI-ALPS Study

The neural mechanisms underlying congenital sensorineural hearing loss (CSNHL) remain elusive. This study evaluated the function of the glymphatic system in children with CSNHL compared to normal-hearing children using the DTI-ALPS approach, which utilizes diffusion tensor imaging along the perivascular space. Twenty-six children with CSNHL and 30 age- and sex-matched healthy controls (HCs) with normal hearing thresholds were recruited. The DTIALPS index was calculated for each group. We analyzed the discrepancies in the DTI-ALPS index between patients with CSNHL and healthy controls. Additionally, Spearman's correlation analysis was performed to investigate the relationship between the DTI-ALPS index and age in children with CSNHL. Significant differences in the DTI-ALPS index were observed between the two groups. Compared with HCs, the DTI-ALPS index in CSNHL patients was significantly lower (1.49388±0.11441 vs. 1.61402±0.15430, p=0.002). In addition, diffusivity along the z-axis in the association fiber (Dzzassoc) index was significantly higher in the CSNHL group than in the HC group (0.00041±0.00006 vs. 0.00036±0.00004, p=0.003). Furthermore, we discovered a noteworthy downward correlation between the DTI-ALPS index and age in children with CSNHL (rho = -0.544, p=0.005). In this present study, glymphatic system activity in CSNHL children was investigated for the first time using the DTI-ALPS index. A significant decrease in glymphatic system function was detected in CSNHL children, which correlated well with age. The DTI-ALPS index could serve as a valuable biomarker for tracking disease progression and treatment in CSNHL and unraveling the neural mechanisms of early hearing deprivation in children with CSNHL.

Long-term outcomes in children with riboflavin transporter deficiency and surveillance recommendations.

The aim of this longitudinal case series was to describe long-term functional outcome in a group of individuals with riboflavin transporter deficiency (RTD) treated with high-dose oral riboflavin. Data were collected between 2012 to 2022. Eleven individuals with RTD were assessed at 12-month intervals for monitoring of disease progression. Patients had commenced high-dose oral riboflavin from the time of genetic diagnosis. Individuals for whom riboflavin supplementation was initiated early after disease onset had better outcomes compared to those in whom diagnosis was delayed. Despite ongoing riboflavin supplementation, the Charcot-Marie-Tooth disease Pediatric Scale (CMTPedS) total score and the subitems of balance and the 6-Minute Walk Test distance as well as respiratory function worsened, while grip strength improved. There was evidence of improvement in hearing loss and optic atrophy limited to the first 12 months of treatment. While treatment with riboflavin slowed disease progression, patients were left with residual disability. To track disease progression and response to riboflavin supplementation over time, we recommend using the RTD Pediatric Scale and provide a list of clinical measures for regular surveillance of children with RTD.

Clinical Profile and Outcomes of Patients With Systemic Lupus Erythematosus.

Background Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by relapsing-remitting immune system activation, affecting multiple organ systems. Despite significant advances in understanding SLE's pathogenesis, there remains a need for comprehensive clinical profiling at the time of diagnosis to improve early detection and management. This study addresses this gap by providing a detailed analysis of the clinical presentation, disease activity, and patient outcomes using the Systemic Lupus International Collaborating Clinics (SLICC) criteria and Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) index. Methodology This cross-sectional observational study included 80 patients diagnosed with SLEusing the 2012 SLICC criteria. Patients were recruited from the Rheumatology department and other wards of Byramjee JeejeebhoyGovernment Medical Collegeand Sassoon General Hospital, Pune, India. All participants provided informed consent and institutional ethical approval was obtained. Data were collected through detailed clinical history, physical examinations, and standard tests such aschest X-rays, CBC, RFT, LFT, urine microscopy, creatine phosphokinase, ANA, AntiDsDNA, complement consumption, and Coombs' tests, with 2D echocardiography performed as needed. Follow-ups every three months over 1.5 years assessed disease activity using SLEDAI criteria. Patients aged 12 and above who met the SLICC criteria were included and those with other connective tissue disorders were excluded. Associations between clinical symptoms and organ involvement were analyzed using thechi-square testwith a p-value of <0.05 considered significant. Results The study evaluated 80 patients with SLE, revealing a predominantly female cohort (80%) with a mean age of 29.4 years and a standard deviation of 8.3 years, skewed towards younger age groups. Clinical manifestations were diverse; the most common symptoms were (83.75%), oral ulcers (98.75%), and alopecia (95%). Anemia (66.25%) was the most prevalent abnormality, followed by albuminuria and renal abnormalities. Organ involvement was highest in the renal system (50%) and mucocutaneous features, with lower incidences in cardiac, gastrointestinal, and vascular systems. Gender-specific analyses indicated significant differences in SLE nephritis (p=0.048) and autoimmune hemolytic anemia (p=0.046). Autoantibody profiles showed high positivity for ANA (98.8%) and DsDNA (61.3%). Clinical outcomes demonstrated that 68.8% of patients achieved remission and 16.3% experienced organ damage. The SLEDAI scores significantly improved over time, with substantial reductions from baseline to nine months (p<0.001). Conclusion In conclusion, this study provides a detailed examination of SLE, revealing that it predominantly affects young adults and is characterized by diverse manifestations including mucocutaneous symptoms, significant renal involvement, and notable autoantibody profiles. The high prevalence of anti-nucleosome and anti-dsDNA antibodies underscores their diagnostic and prognostic value. Clinically, the findings highlight the necessity for early detection and targeted management of SLE, particularly in addressing renal and mucocutaneous symptoms. Future research should focus on longitudinal studies to track disease progression, explore genetic and environmental influences, and investigate regional variations to enhance treatment strategies and patient outcomes.

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures.Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons.We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score.Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases.

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

Postpartum Renal Cortical Necrosis: A Case Series

Rationale & ObjectivePostpartum renal cortical necrosis (postpartum RCN) is a severe form of obstetric acute kidney injury. This study aimed to identify clinicopathological features in Chinese postpartum RCN cases to determine how pathologic findings may contribute to the treatment and prognosis. Study DesignSingle-center, case series Setting & ParticipantsTwelve patients with postpartum RCN had kidney biopsies at Peking University First Hospital between 2014 and 2021. The diagnosis of postpartum RCN was made according to typical MRI or pathological features. Clinical, laboratory and pathological data were compared between patients with eGFR <30 (poor outcome) and ≥30 ml/min/1.73 m2 after six months. ObservationsAll postpartum RCN patients presented with stage 3 AKI attributed to a probable atypical hemolytic uremic syndrome. Pregnancy terminations occurred at a median gestational age of 35.5 weeks. Kidney biopsy was performed ranging 18 days to 4 months from delivery. Upon biopsy, hemoglobin, platelet count and LDH levels had been restored to 137 g/L, 214 ×109/L and 231.50±65.01 U/L, respectively. Four patients exhibited poor outcome, demonstrating higher schistocyte count, serum creatinine and mean arterial pressure at onset. Pathologically, glomerular segmental sclerosis was prevalent. The NOS variant was the most common type, followed by collapsing variant, cellular variant, and tip variant. Patients with poor kidney outcome had more glomerular coagulative necrosis, capillary thrombosis, extensive cortical coagulative necrosis, and pronounced arteriole/artery lesions including increased interlobular arteriole intimal edema and fibrin thrombosis, but a lower occurrence of segmental sclerosis. LimitationsLimited sample size and retrospective design. ConclusionsOur research has identified key pathological features in postpartum RCN patients with aHUS, highlighting the necessity for more effective therapeutic options. There is a clear demand for non-invasive biomarkers that can accurately track disease progression and inform treatment duration for long-term outcomes improvement.

Quantitative Computed Tomography Angiography for the Evaluation of Valvular Fibrocalcific Volume in Aortic Stenosis

BackgroundAortic stenosis (AS) is characterized by calcification and fibrosis. The ability to quantify these processes simultaneously has been limited with previous imaging methods. ObjectivesThe purpose of this study was to evaluate the aortic valve fibrocalcific volume by computed tomography (CT) angiography in patients with AS, in particular, to assess its reproducibility, association with histology and disease severity, and ability to predict/track progression. MethodsIn 136 patients with AS, fibrocalcific volume was calculated on CT angiograms at baseline and after 1 year. CT attenuation distributions were analyzed using Gaussian-mixture-modeling to derive thresholds for tissue types enabling the quantification of calcific, noncalcific, and fibrocalcific volumes. Scan-rescan reproducibility was assessed and validation provided against histology and in an external cohort. ResultsFibrocalcific volume measurements took 5.8 ± 1.0 min/scan, demonstrating good correlation with ex vivo valve weight (r = 0.51; P < 0.001) and excellent scan-rescan reproducibility (mean difference −1%, limits of agreement −4.5% to 2.8%). Baseline fibrocalcific volumes correlated with mean gradient on echocardiography in both male and female participants (rho = 0.64 and 0.69, respectively; both P < 0.001) and in the external validation cohort (n = 66, rho = 0.58; P < 0.001). The relationship was driven principally by calcific volume in men and fibrotic volume in women. After 1 year, fibrocalcific volume increased by 17% and correlated with progression in mean gradient (rho = 0.32; P = 0.003). Baseline fibrocalcific volume was the strongest predictor of subsequent mean gradient progression, with a particularly strong association in female patients (rho = 0.75; P < 0.001). ConclusionsThe aortic valve fibrocalcific volume provides an anatomic assessment of AS severity that can track disease progression precisely. It correlates with disease severity and hemodynamic progression in both male and female patients.

Perceptions of medical doctors and patients about imaging for people with low back pain: A qualitative study.

Low-value care is the use of substitutive/ineffective/harmful strategies based on available evidence, and it is considered one of the main contributors to the burden related to low back pain in health care systems. The use of routine imaging for patients with low back pain is the main example of inappropriate care. Therefore, understanding the perceptions of medical doctors and patients from Brazil about this practice may help propose strategies to reduce imaging rates. To investigate the perceptions of medical doctors and patients about imaging for the diagnosis of nonspecific low back pain. A qualitative study using the framework analysis method. Primary and secondary care. Fifteen patients with low back pain and 15 doctors participated in this study. Sociodemographic data were collected from all participants, and the interviews were performed using a set of questions created based on the literature. Patients and doctors believe that the main reason for ordering imaging tests is to identify the source of pain, and imaging could be useful for tracking disease progression over time or if there is a lack of improvement after treatment. Patients' expectations and pressures play a role in the decision to order imaging tests, but clinicians believe that education is the preferred strategy to reduce imaging rates. Identifying the source of pain, tracking the disease progression, and patients' expectations and pressures were the main drivers of imaging requests for low back pain. Educational strategies were suggested to reduce the use of routine imaging.

Comparative proteomic analysis of plasma exosomes reveals the functional contribution of N-acetyl-alphaglucosaminidase to Parkinson's disease.

Parkinson's disease is the second most common progressive neurodegenerative disorder, and few reliable biomarkers are available to track disease progression. The proteins, DNA, mRNA, and lipids carried by exosomes reflect intracellular changes, and thus can serve as biomarkers for a variety of conditions. In this study, we investigated alterations in the protein content of plasma exosomes derived from patients with Parkinson's disease and the potential therapeutic roles of these proteins in Parkinson's disease. Using a tandem mass tag-based quantitative proteomics approach, we characterized the proteomes of plasma exosomes derived from individual patients, identified exosomal protein signatures specific to patients with Parkinson's disease, and identified N-acetyl-alpha-glucosaminidase as a differentially expressed protein. N-acetyl-alpha-glucosaminidase expression levels in exosomes from the plasma of patients and healthy controls were validated by enzyme-linked immunosorbent assay and western blot. The results demonstrated that the exosomal N-acetyl-alpha-glucosaminidase concentration was not only lower in Parkinson's disease, but also decreased with increasing Hoehn-Yahr stage, suggesting that N-acetyl- alpha-glucosaminidase could be used to rapidly evaluate Parkinson's disease severity. Furthermore, western blot and immunohistochemistry analysis showed that N-acetyl-alpha-glucosaminidase levels were markedly reduced both in cells treated with methyl-4-phenylpyridinium (MPP+) and cells overexpressing a-synuclein (α-syn) compared with control cells. Additionally, N-acetyl-alpha-glucosaminidase overexpression significantly increased cell viability and inhibited α-syn expression in MPP+-treated cells. Taken together, our findings demonstrate for the first time that exosomal N-acetyl-alpha-glucosaminidase may serve as a biomarker for Parkinson's disease diagnosis, and that N-acetyl-alpha- glucosaminidase may reduce α-syn expression and MPP+-induced neurotoxicity, thus providing a new therapeutic target for Parkinson's disease.

Bidirectional Relationships between Sarcopenia and Pelvic Floor Disorders.

Sarcopenia and pelvic floor disorders (PFDs) are prevalent and often cooccurring conditions in the aging population. However, their bidirectional relationship and underlying mechanisms remain underexplored. This narrative review aims to elucidate this relationship by exploring potential causative interplays, shared pathophysiological mechanisms, and common risk factors. A comprehensive literature search was conducted to identify relevant studies focusing on epidemiological associations, interaction mechanisms, and implications for patient care. While epidemiological studies demonstrate associations between sarcopenia and PFDs, our findings reveal a cyclical relationship where sarcopenia may exacerbate PFDs through mechanisms such as decreased muscle strength and mobility. Conversely, the presence of PFDs often leads to reduced physical activity due to discomfort and mobility issues, which in turn exacerbate the muscle atrophy associated with sarcopenia. Additionally, shared risk factors such as physical inactivity, nutritional deficiencies, metabolic syndrome, and menopausal hormonal changes likely contribute to the onset and progression of both conditions. These interactions underscore the importance of concurrently integrated care approaches that address both conditions. Effective management requires comprehensive screening, the recognition of contributing factors, and tailored exercise regimens supported by a multidisciplinary approach. Future research should focus on longitudinal studies tracking disease progression and evaluating the efficacy of multidisciplinary care models in optimizing patient outcomes.