HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.

Objective: We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Materials and Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer's Disease Neuroimaging Initiative dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multitask baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline. Results: Out of 634 MCI patients (mean [IQR] age: 72.8 [67-78], 60% male), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all state-of-the-art longitudinal single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC = 0.623 [0.605-0.644]; all P < .05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression. Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected electronic health records (EHR) variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.

Exploring the role of exosomes in the pathogenesis and treatment of cardiomyopathies: A comprehensive literature review.

Exosomes, a subset of small extracellular vesicles that play a crucial role in intercellular communication, have garnered significant attention for their potential applications in the diagnosis and treatment of cardiomyopathies. Cardiomyopathies, which encompass a spectrum of heart muscle disorders, present complex challenges in diagnosis and management. Understanding the role of exosomes in the etiology of cardiomyopathies such as dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic cardiomyopathy (AC), and hypertrophic cardiomyopathy (HCM) may open new possibilities for therapeutic intervention and diagnosis. Exosomes have indeed demonstrated promise as diagnostic biomarkers, particularly in identifying cardiac conditions such as atrial fibrillation (AF) and in the timely classification of high-risk patients with different forms of cardiomyopathy. In DCM, exosomes have been implicated in mediating pathological responses in cardiomyocytes, potentially exacerbating disease progression. Moreover, in RCM, AC, and HCM, exosomes present significant potential as diagnostic biomarkers and therapeutic targets, offering insights into disease pathogenesis and potential avenues for intervention. Understanding the influence of exosomes on disease progression and identifying the specific molecular pathways involved in cardiomyopathy pathogenesis may significantly advance diagnostic and treatment strategies. While key findings highlight the multifaceted role of exosomes in cardiomyopathy, they also emphasize the need for further research to elucidate molecular mechanisms and translate findings into clinical practice. This review highlights the evolving landscape of exosome research in cardiomyopathies and underscores the importance of ongoing investigations to harness the full potential of exosomes in improving patient outcomes.

Contribution of the Gut Microbiome to the Perpetuation of Inflammation in Crohn's Disease: A Systematic Review.

Crohn's disease (CD) is a sub-type of inflammatory bowel disease (IBD) with a characteristic relapsing and remitting inflammation involving the gastrointestinal (GI) tract. Although there are several medications to relieve the symptoms, there is no definite cure for the condition. This paper highlights how CD affects our gut flora, which subsequently leads to the perpetuation of inflammation. This review was conducted according to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines using PubMed, ScienceDirect, Multidisciplinary Digital Publishing Institute (MDPI), and Google Scholar as sources for relevant literature. After applying the quality appraisal tools, we finalized 11 articles for the paper. Inflammation seen in CD leads to dysbiosis, where there is a reduction in beneficial microbes such as Faecalibacterium and Roseburia species and an increase in pathogenic microbes such as Escherichia and Proteus species. This difference in gut microbes disrupts barrier function and immune processes in the intestine, contributing to the worsening of inflammation seen in CD. Several studies have been carried out to understand this complex relationship between the gut microbiome (GM) and CD, as it may serve as a potential novel therapeutic alternative, necessary as CD's burden is increasing globally.

Development of a Cohort Analytics Tool for Monitoring Progression Patterns in Cardiovascular Diseases: Advanced Stochastic Modeling Approach.

BACKGROUND: The World Health Organization (WHO) reported that cardiovascular diseases (CVDs) are the leading cause of death worldwide. CVDs are chronic, with complex progression patterns involving episodes of comorbidities and multimorbidities. When dealing with chronic diseases, physicians often adopt a "watchful waiting" strategy, and actions are postponed until information is available. Population-level transition probabilities and progression patterns can be revealed by applying time-variant stochastic modeling methods to longitudinal patient data from cohort studies. Inputs from CVD practitioners indicate that tools to generate and visualize cohort transition patterns have many impactful clinical applications. The resultant computational model can be embedded in digital decision support tools for clinicians. However, to date, no study has attempted to accomplish this for CVDs. OBJECTIVE: This study aims to apply advanced stochastic modeling methods to uncover the transition probabilities and progression patterns from longitudinal episodic data of patient cohorts with CVD and thereafter use the computational model to build a digital clinical cohort analytics artifact demonstrating the actionability of such models. METHODS: Our data were sourced from 9 epidemiological cohort studies by the National Heart Lung and Blood Institute and comprised chronological records of 1274 patients associated with 4839 CVD episodes across 16 years. We then used the continuous-time Markov chain method to develop our model, which offers a robust approach to time-variant transitions between disease states in chronic diseases. RESULTS: Our study presents time-variant transition probabilities of CVD state changes, revealing patterns of CVD progression against time. We found that the transition from myocardial infarction (MI) to stroke has the fastest transition rate (mean transition time 3, SD 0 days, because only 1 patient had a MI-to-stroke transition in the dataset), and the transition from MI to angina is the slowest (mean transition time 1457, SD 1449 days). Congestive heart failure is the most probable first episode (371/840, 44.2%), followed by stroke (216/840, 25.7%). The resultant artifact is actionable as it can act as an eHealth cohort analytics tool, helping physicians gain insights into treatment and intervention strategies. Through expert panel interviews and surveys, we found 9 application use cases of our model. CONCLUSIONS: Past research does not provide actionable cohort-level decision support tools based on a comprehensive, 10-state, continuous-time Markov chain model to unveil complex CVD progression patterns from real-world patient data and support clinical decision-making. This paper aims to address this crucial limitation. Our stochastic model-embedded artifact can help clinicians in efficient disease monitoring and intervention decisions, guided by objective data-driven insights from real patient data. Furthermore, the proposed model can unveil progression patterns of any chronic disease of interest by inputting only 3 data elements: a synthetic patient identifier, episode name, and episode time in days from a baseline date.

Hsa_circ_0006010 and hsa_circ_0002903 in peripheral blood serve as novel diagnostic, surveillance and prognostic biomarkers for disease progression in chronic myeloid leukemia.

BACKGROUND: In the era of tyrosine kinase inhibitor (TKI) treatment, the progression of chronic myeloid leukemia (CML) remains a significant clinical challenge, and genetic biomarkers for the early identification of CML patients at risk for progression are limited. This study explored whether essential circular RNAs (circRNAs) can be used as biomarkers for diagnosing and monitoring CML disease progression and assessing CML prognosis. METHODS: Peripheral blood (PB) samples were collected from 173 CML patients (138 patients with chronic phase CML [CML-CP] and 35 patients with accelerated phase/blast phase CML [CML-AP/BP]) and 63 healthy controls (HCs). High-throughput RNA sequencing (RNA-Seq) was used to screen dysregulated candidate circRNAs for a circRNA signature associated with CML disease progression. Quantitative real-time PCR (qRT-PCR) was used for preliminary verification and screening of candidate dysregulated genes, as well as subsequent exploration of clinical applications. Receiver operating characteristic (ROC) curve analysis, Spearman's rho correlation test, and the Kaplan-Meier method were used for statistical analysis. RESULTS: The aberrant expression of hsa_circ_0006010 and hsa_circ_0002903 during CML progression could serve as valuable biomarkers for differentiating CML-AP/BP patients from CMP-CP patients or HCs. In addition, the expression levels of hsa_circ_0006010 and hsa_circ_0002903 were significantly associated with the clinical features of CML patients but were not directly related to the four scoring systems. Furthermore, survival analysis revealed that high hsa_circ_0006010 expression and low hsa_circ_0002903 expression indicated poor progression-free survival (PFS) in CML patients. Finally, PB hsa_circ_0006010 and hsa_circ_0002903 expression at diagnosis may also serve as disease progression surveillance markers for CML patients but were not correlated with PB BCR-ABL1/ABL1IS. CONCLUSIONS: Our study demonstrated that PB levels of hsa_circ_0006010 and hsa_circ_0002903 may serve as novel diagnostic, surveillance, and prognostic biomarkers for CML disease progression and may contribute to assisting in the diagnosis of CML patients at risk for progression and accurate management of advanced CML patients.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. OBJECTIVE: We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. METHODS: In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. RESULTS: The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. CONCLUSIONS: The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

Progression of hippocampal subfield atrophy and asymmetry in Alzheimer's disease.

Alzheimer's disease (AD) affects the hippocampus during its progression, but the specific observable changes of hippocampal subfields during disease progression remain poorly understood. In this study, we employed an event-based model (EBM) to determine the sequence of occurrence of hippocampal subfield atrophy in mild cognitive impairment (MCI) and AD cohorts. Subjects (207) were included: 86 MCI, 53 AD, and 68 healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent structural magnetic resonance imaging (MRI) scans to analyse the hippocampal subfields. We assigned each patient to a specific EBM stage, based on the number of their abnormal subfields. A combination of 2-year follow-up MRI scans were applied to demonstrate the longitudinal consistency and utility of the model's staging system. The model estimated that the earliest atrophy occurs in the hippocampal fissure, then spreading to other subregions in both MCI and AD. We identified a marked divergence between the sequences of left and right hippocampal subfields atrophy, so inter-hemispheric asymmetry pattern was further analysed. The sequence of asymmetry index (AI) increases beginning in the molecular and granule cell layers of the dentate gyrus (GC-ML-DG), cornus ammonis (CA) 4, and the molecular layer (ML). Longitudinal analysis confirms the efficacy of the model. In addition, the model stages were significantly correlated with patients' memory scores (p < .05). Collectively, we used a data-driven method to provide new insight into AD hippocampal progression. The present model could aid in understanding of the disease stages, as well as tracking memory decline.

Profiles of disease progression and predictors of mortality in Colombian patients with amyotrophic lateral sclerosis: a comprehensive longitudinal study.

Objective: This study aimed to assess the prognostic value of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) in predicting mortality and characterizing disease progression patterns in ALS patients in Colombia. Methods: We conducted a retrospective longitudinal analysis of 537 ALS patients from the Roosevelt Institute Rehabilitation Service between October 2008 and October 2022. The study excluded nine patients due to incomplete data, resulting in 528 individuals in the analysis. ALS diagnoses were confirmed using the revised El Escorial and Gold Coast criteria. Disease progression was assessed using the ALSFRS-R, and mortality data were sourced from follow-up calls and a national database. Statistical analysis included Cox proportional hazards models to identify mortality predictors and Growth Mixture Modeling (GMM) to explore ALS progression trajectories. Results: The majority of the cohort (63.8%) deceased within the 84-month follow-up period. Survival analysis revealed that each point increase in the ALSFRS-R rate was associated with a 2.22-fold (95% CI =1.99-2.48, p < 0.001) increased risk of mortality. In the population with data from two clinical visits, the ALSFRS-R rate based on initial assessments predicted mortality more effectively over 36 months than the rate based on two evaluations. GMM identified three distinct progression trajectories: slow, intermediate, and rapid decliners. Conclusions: The ALSFRS-R rate, derived from self-reported symptom onset, significantly predicts mortality, underscoring its value in clinical assessments. This study highlights the heterogeneity in disease progression among Colombian ALS patients, indicating the necessity for personalized treatment approaches based on individual progression trajectories. Further studies are needed to refine these predictive models and improve patient management and outcomes.

Human papillomavirus self-sampling and urine-sampling tests and the management and short-term outcomes of cervical intraepithelial neoplasia: A prospective observational study.

AIM: The importance of human papillomavirus (HPV) co-testing using physician-, self-, and urine-collected samples to predict cervical intraepithelial neoplasia (CIN) grade 1-2 prognoses has not been previously reported. Therefore, this study aimed to investigate outcomes of patients with CIN 1-2 who simultaneously underwent physician-, self-, and urine-collection sampling tests. METHODS: This study was conducted in Japan between October 2019 and November 2022 and examined the proportion of cases with CIN 1-2 progressions, the percentage of cases with persistent CIN 1-2, and the outcome differences according to the results of physician-, self-, and urine-sampling tests. RESULTS: There were 105 and 59 CIN 1 and 2 cases, respectively, with progression or persistence in 27 (29.3%) and 21 (50.0%) cases, respectively. The median follow-up was 20 and 12 months, respectively. Progression and persistence of CIN 1 were significantly associated with HPV-positive physician- and self-collected samples. No significant difference was observed between cases with CIN 2 who had HPV-positive and HPV-negative results using any sampling method. CONCLUSIONS: Physician- and self-testing for HPV are crucial for predicting disease progression risk in CIN 1 cases. Future research with an extended observation period and consideration of the progression risks is warranted.

EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease.

BACKGROUND: Predicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia. METHODS: Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology. RESULTS: The converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre-alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, -2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, -2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score. CONCLUSION: Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.

The evolution of envelope function during coinfection with phylogenetically distinct human immunodeficiency virus.

BACKGROUND: Coinfection with two phylogenetically distinct Human Immunodeficiency Virus-1 (HIV-1) variants might provide an opportunity for rapid viral expansion and the emergence of fit variants that drive disease progression. However, autologous neutralising immune responses are known to drive Envelope (Env) diversity which can either enhance replicative capacity, have no effect, or reduce viral fitness. This study investigated whether in vivo outgrowth of coinfecting variants was linked to pseudovirus and infectious molecular clones' infectivity to determine whether diversification resulted in more fit virus with the potential to increase disease progression. RESULTS: For most participants, emergent recombinants displaced the co-transmitted variants and comprised the major population at 52 weeks postinfection with significantly higher entry efficiency than other co-circulating viruses. Our findings suggest that recombination within gp41 might have enhanced Env fusogenicity which contributed to the increase in pseudovirus entry efficiency. Finally, there was a significant correlation between pseudovirus entry efficiency and CD4 + T cell count, suggesting that the enhanced replicative capacity of recombinant variants could result in more virulent viruses. CONCLUSION: Coinfection provides variants with the opportunity to undergo rapid recombination that results in more infectious virus. This highlights the importance of monitoring the replicative fitness of emergent viruses.

Effect of abatacept versus conventional synthetic disease modifying anti-rheumatic drugs on rheumatoid arthritis-associated interstitial lung disease.

BACKGROUND/AIMS: To compare the effects of abatacept and conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs) on the progression and development of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). METHODS: This multi-center retrospective study included RA patients receiving abatacept or csDMARDs who underwent at least two pulmonary function tests and/or chest high-resolution computed tomography (HRCT). We compared the following outcomes between the groups: progression of RA-ILD, development of new ILD in RA patients without ILD at baseline, 28-joint Disease Activity Score with the erythrocyte sedimentation rate (DAS28-ESR), and safety. Longitudinal changes were compared between the groups by using a generalized estimating equation. RESULTS: The study included 123 patients who were treated with abatacept (n = 59) or csDMARDs (n = 64). Nineteen (32.2%) and 38 (59.4%) patients treated with abatacept and csDMARDs, respectively, presented with RA-ILD at baseline. Newly developed ILD occurred in one patient receiving triple csDMARDs for 32 months. Among patients with RA-ILD at baseline, ILD progressed in 21.1% of cases treated with abatacept and 34.2% of cases treated with csDMARDs during a median 21-month follow-up. Longitudinal changes in forced vital capacity and diffusing capacity for carbon monoxide were comparable between the two groups. However, the abatacept group showed a more significant decrease in DAS28-ESR and glucocorticoid doses than csDMARDs group during the follow-up. The safety of both regimens was comparable. CONCLUSION: Abatacept and csDMARDs showed comparable effects on the development and stabilization of RA-ILD. Nevertheless, compared to csDMARDs, abatacept demonstrated a significant improvement in disease activity and led to reduced glucocorticoid use.

Multivariate longitudinal analysis for the association between brain atrophy and cognitive impairment in prodromal Huntington's disease subjects.

Cognitive impairment has been widely accepted as a disease progression measure prior to the onset of Huntington's disease. We propose a sophisticated measurement error correction method that can handle potentially correlated measurement errors in longitudinally collected exposures and multiple outcomes. The asymptotic theory for the proposed method is developed. A simulation study is conducted to demonstrate the satisfactory performance of the proposed two-stage fitting method and shows that the independent working correlation structure outperforms other alternatives. We conduct a comprehensive longitudinal analysis to assess how brain striatal atrophy affects impairment in various cognitive domains for Huntington's disease.

The role of ALDH2 rs671 polymorphism and C-reactive protein in the phenotypes of male ALS patients.

Background: The aldehyde dehydrogenase 2 (ALDH2) rs671 (A) allele has been implicated in neurodegeneration, potentially through oxidative and inflammatory pathways. The study aims to investigate the effects of the ALDH2 rs671 (A) allele and high sensitivity C-reactive protein (hs-CRP) on the clinical phenotypes of amyotrophic lateral sclerosis (ALS) in male and female patients. Methods: Clinical data and ALDH2 rs671 genotype of 143 ALS patients, including 85 males and 58 females, were collected from January 2018 to December 2022. All patients underwent assessment using the Chinese version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). Complete blood count and metabolic profiles were measured. Clinical and laboratory parameters were compared between carriers and non-carriers of the rs671 (A) allele in males and females, respectively. The significant parameters and rs671 (A) Allele were included in multivariate linear regression models to identify potential contributors to motor and cognitive impairment. Mediation analysis was employed to evaluate any mediation effects. Results: Male patients carrying rs671 (A) allele exhibited higher levels of hs-CRP than non-carriers (1.70 mg/L vs. 0.50 mg/L, p = 0.006). The rs671 (A) allele was identified as an independent risk factor for faster disease progression only in male patients (ß = 0.274, 95% CI = 0.048-0.499, p = 0.018). The effect of the rs671 (A) allele on the executive function in male patients was fully mediated by hs-CRP (Indirect effect = -1.790, 95% CI = -4.555--0.225). No effects of the rs671 (A) allele or hs-CRP were observed in female ALS patients. The effects of the ALDH2 rs671 (A) allele and the mediating role of hs-CRP in male patients remained significant in the sensitivity analyses. Conclusion: The ALDH2 rs671 (A) allele contributed to faster disease progression and hs-CRP mediated cognitive impairment in male ALS patients.

A future of AI-driven personalized care for people with multiple sclerosis.

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.

Progress of intracranial metastases during the interval before stereotactic radiosurgery, a retrospective cohort analysis.

INTRODUCTION: The incidence of intracranial metastatic disease is increasing worldwide. As a valuable treatment modality, stereotactic radiosurgery requires detailed imaging, and this study evaluated the differences between imaging obtained on the day of treatment compared to historical or referral imaging. MATERIALS AND METHODS: A retrospective cohort study was performed, evaluating all the patients presenting with eligible referral imaging in a 13-month period and comparing this imaging to the imaging taken on the day of treatment. Numbers of additional metastases, volumes and volume differences among the images were compared. RESULTS: There was a median interval of 19 days between the acquisition of the diagnostic or referral scan and the day of treatment imaging. Even the group that had the shortest interval (up to 2 weeks) showed at least one additional deposit in 50 % of the patients. Volume was increased in 75 % of this group. Longer intervals were associated with higher increases in volume. CONCLUSION: These results demonstrate the increase in the disease burden in patients with intracranial metastatic disease, in relation to number and volume, in the interval between the referral and treatment imaging. This has significant implications for planning pathways, to ensure that metastatic deposits are not missed or undertreated.

Lymphocyte subsets for predicting inflammatory bowel disease progression and treatment response: a systematic review.

Background: Lymphocytes play a key role in the pathogenesis of inflammatory bowel disease (IBD) and are widely explored as promising prognostic indicators. We aimed to outline the existing evidences on the capability of lymphocyte subpopulations to predict disease progression and treatment response in patients with IBD. Methods: The protocol for this review was registered in PROSPERO (registration ID: CRD 42022364126). Systematic retrieval was conducted using PubMed, Embase, and Web of Science databases. Original articles on the prognostic value of lymphocyte subsets in IBD published up to April 8, 2023 were eligible for inclusion. The Newcastle-Ottawa Scale was used to evaluate the risk of bias. Results: Twenty studies were ultimately included: eight evaluated the prediction of disease progression and 12 focused on the prediction of treatment response. According to the Newcastle-Ottawa Scale, three studies were of high quality, 16 were of moderate quality, and only one was of low quality. T-cell subpopulations, including CD4+ T cells, CD8+ T cells, and γδ T cells, are revealed to have prognostic capacity. Transmembrane tumor necrosis factor α-bearing lymphocytes, CD4+ T cells, CD8+ T cells, and Plasma cells are found to have the potential to predict the response to anti-TNFα agents. In contrast memory T cells, CD4+ T cells, and naïve B cells may predict the response to vedolizumab. Conclusions: This systematic review identified several potential lymphocyte subset-related predictors. If verified in large cohort prospective studies, these findings could aid clinical decision-making. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022364126.

ESR Essentials: imaging in fibrotic lung diseases-practice recommendations by the European Society of Thoracic Imaging.

Fibrotic lung diseases (FLDs) represent a subgroup of interstitial lung diseases (ILDs), which can progress over time and carry a poor prognosis. Imaging has increased diagnostic discrimination in the evaluation of FLDs. International guidelines have stated the role of radiologists in the diagnosis and management of FLDs, in the context of the interdisciplinary discussion. Chest computed tomography (CT) with high-resolution technique is recommended to correctly recognise signs, patterns, and distribution of individual FLDs. Radiologists may be the first to recognise the presence of previously unknown interstitial lung abnormalities (ILAs) in various settings. A systematic approach to CT images may lead to a non-invasive diagnosis of FLDs. Careful comparison of serial CT exams is crucial in determining either disease progression or supervening complications. This 'Essentials' aims to provide radiologists a concise and practical approach to FLDs, focusing on CT technical requirements, pattern recognition, and assessment of disease progression and complications. Hot topics such as ILAs and progressive pulmonary fibrosis (PPF) are also discussed. KEY POINTS: Chest CT with high-resolution technique is the recommended imaging modality to diagnose pulmonary fibrosis. CT pattern recognition is central for an accurate diagnosis of fibrotic lung diseases (FLDs) by interdisciplinary discussion. Radiologists are to evaluate disease behaviour by accurately comparing serial CT scans.

Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling.

INTRODUCTION: Neuroanatomical normative modeling captures individual variability in Alzheimer's disease (AD). Here we used normative modeling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS: Cortical and subcortical normative models were generated using healthy controls (n ≈ 58k). These models were used to calculate regional z scores in 3233 T1-weighted magnetic resonance imaging time-series scans from 1181 participants. Regions with z scores < -1.96 were classified as outliers mapped on the brain and summarized by total outlier count (tOC). RESULTS: tOC increased in AD and in people with MCI who converted to AD and also correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of progression from MCI to AD. Brain outlier maps identified the hippocampus as having the highest rate of change. DISCUSSION: Individual patients' atrophy rates can be tracked by using regional outlier maps and tOC. HIGHLIGHTS: Neuroanatomical normative modeling was applied to serial Alzheimer's disease (AD) magnetic resonance imaging (MRI) data for the first time. Deviation from the norm (outliers) of cortical thickness or brain volume was computed in 3233 scans. The number of brain-structure outliers increased over time in people with AD. Patterns of change in outliers varied markedly between individual patients with AD. People with mild cognitive impairment whose outliers increased over time had a higher risk of progression from AD.

Unveiling the disease progression in developmental and epileptic encephalopathies: Insights from EEG and neuropsychology.

OBJECTIVE: Developmental and epileptic encephalopathies (DEEs) are neurological disorders characterized by developmental impairment and epilepsy. Our study aims to assess disease progression by comparing clinical findings, electroencephalography (EEG), and neuropsychological data from seizure onset to the last follow-up evaluation. METHODS: We retrospectively reviewed patients with genetic DEEs who were followed-up at the epilepsy unit of Bambino Gesù Children's Hospital, Rome. We collected information regarding gender, family history, genetic variant, age at onset and at last follow-up, neurological examination, type of seizure, drug resistance, occurrence of status epilepticus, and movement and cognitive and behavioral disorders. We compared EEG background activity, epileptiform abnormalities, and cognitive functions between seizure onset and the last follow-up evaluation using the McNemar-Bowker test (α = 5%). RESULTS: A total of 160 patients (94 female) were included. Genetic analysis revealed a spectrum of pathogenic variants, with SCN1A being the most prevalent (25%). The median age at seizure onset and at the last follow-up was 0.37 (interquartile range [IQR]: 0.09-0.75) and 8.54 years (IQR: 4.32-14.55), respectively. We documented a statistically significant difference in EEG background activity (p = .017) and cognitive impairment (p = .01) from seizure onset to the last follow-up evaluation. No significant differences were detected for epileptiform abnormalities (p = .2). In addition, high prevalence rates were observed for drug resistance (81.9%), movement disorders (60.6%), behavioral and autism spectrum disorders (45%), neurological deficits (31.3%), and occurrence of status epilepticus (23.1%). SIGNIFICANCE: Our study provides evidence that a clinical progression may appear in genetic DEEs, manifesting as development or worsening of cognitive impairment and disruption of EEG background activity. These results highlight the challenging clinical course and the importance of early intervention and personalized care in the management of patients with DEEs.