Patterns Of Progression Research Articles

Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

Early detection of Alzheimer’s disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer’s Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors.

Patterns of loco-regional progression and patient outcomes after definitive-dose radiation therapy for anaplastic thyroid cancer

BackgroundThe aim of this study is to characterize the patterns of loco-regional progression (LRP) and outcomes after definitive-dose intensity modulated radiation therapy (IMRT) for anaplastic thyroid cancer (ATC) with macroscopic neck disease at the time of IMRT. MethodsDisease/treatment characteristics and outcomes for patients with unresected or incompletely resected ATC who received IMRT (≥45 Gy) were retrospectively reviewed. For those with LRP after IMRT, progressive/recurrent gross tumor volumes (rGTV) were contoured on diagnostic CTs and co-registered with initial planning CTs using deformable image registration. rGTVs were classified based on established spatial/dosimetric criteria. ResultsForty patients treated between 2010–2020 formed the cohort. Median IMRT dose was 66 Gy (45–70 Gy); altered fractionation (AF) was used in 24 (60 %). All received concurrent chemotherapy. In addition to areas of gross disease, target volumes (TVs) commonly included: central compartment/upper mediastinum (levels VI/VII), neck levels II-V in an involved, and levels III-IV in an uninvolved lateral neck. Median overall survival was 7.1 m. Median progression free survival was 7.4 m for patients with locoregional disease and 1.8 m for patients with distant metastasis at the time of IMRT. Twenty-one patients (53 %) developed LRP at median of 10.9 m; freedom from LRP at 3 m and 12 m was 71 % (95 %CI 58–87 %) and 47 % (95 %CI 32–68 %). Forty-one individual rGTVs were identified and most occurred within the high dose (HD) TVs: Type A/central HD (n = 29, 71 %) and B/peripheral HD (n = 3, 7 %). ConclusionsDespite an intensive treatment schedule, including AF and concurrent chemotherapy, classic radio-resistant and rapid Type A failures predominated; isolated extraneous dose failures were rare. While these findings support the IMRT and TV delineation strategies described herein, they highlight the importance of identifying novel strategies to further improve LRC for patients with unresectable disease without targetable mutations for contemporary neo-adjuvant strategies.

Latent factors underlying the symptoms of adult-onset myotonic dystrophy type 1 during the clinical course

BackgroundMyotonic dystrophy type 1 (DM1) is a multisystem genetic disorder that classically presents with symptoms associated with myotonia, early onset cataracts, and muscular weakness, although the presentation and pattern of disease progression is quite varied. Presenting symptoms are well documented among adults with DM1. However, less is known about the co-occurrence of symptoms over time. We aimed to use factor analysis to explore the correlation pattern of signs and symptoms (S/S) that emerged during the clinical course.ResultsClinical records of 228 individuals with adult onset DM1 were abstracted using the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from a six-site cohort in the United States during an eight-year study period. Factor analysis was used to group the correlated S/S into latent factors. Three factors were identified. Group 1: ‘Facial Weakness/Myotonia’ includes the two most common S/S, as indicated by its name. Group 2: ‘Skeletal Muscle Weakness’ includes eight muscular S/S and is more frequently reported by males and those with older age at onset. Group 3: ‘Gastrointestinal distress/Sleepiness’ includes four non-muscular S/S and hand stiffness. The abstracted medical records reported that over 63% of individuals had S/S from all three groups. Associations of covariates with factor scores were also examined using linear regression. CTG repeat length was significantly positively associated with higher factor scores for all three factors.ConclusionsThis study identified three latent factors of S/S which accumulated during the clinical course of adult onset DM1.

A Good Diagnosis Saves Lives: The Importance of Teledermatology: Case Report

Chromoblastomycosis (CBM) is one of the most prevalent subcutaneous mycoses, caused by melanized or pigmented fungi. Considered a tropical or subtropical disease, commonly seen in rural areas after traumatic inoculation, mostly associated with agriculture. The lesions tend to present on the limbs and follow a chronic and progressive pattern, which if left untreated or misdiagnosed can lead to loss of functionality and lower quality of life. Teledermatology (TD) has been used mainly to diagnose, monitor and treat patients at distance in neglected areas. Guatemala has a vast rural area and despite the efforts from both private and public sectors, we still cannot reach all the patients and not all the patients can reach us. We present a case report on how with the aid of teledermatology a life was changed for the good.

The Progression of Symptoms in Post COVID-19 Patients: A Multicentre, Prospective, Observational Cohort Study

Background: Although the coronavirus disease 2019 (COVID-19) pandemic is no longer a public health emergency of international concern, 30% of COVID-19 patients still have long-term complaints. A better understanding of the progression of symptoms after COVID-19 is needed to reduce the burden of the post COVID-19 condition. Objective: This study aims to investigate the progression of symptoms, identify patterns of symptom progression, and assess their associations with patient characteristics. Methods: Within the P4O2 COVID-19 study, patients aged 40–65 years were recruited from five Dutch hospitals. At 3–6 and 12–18 months post COVID-19, medical data were collected, and pulmonary function tests were performed. In between, symptoms were assessed monthly with a questionnaire. Latent class mixed modelling was used to identify symptom progression patterns over time, with multinomial logistic regression to examine associations with patient characteristics. Results: Eighty-eight patients (aged 54.4 years, 48.9% males) were included. Three trajectories were identified for fatigue and dyspnoea: decreasing, high persistent, and low persistent. The odds of “decreasing fatigue” was higher for never smokers and participants in the lifestyle intervention and lower for those having a comorbidity. The odds of “decreasing dyspnoea” was higher for moderate COVID-19 patients and lifestyle intervention participants and lower for males, mild COVID-19 patients, and those with a higher age. Conclusions: Three distinct trajectories were identified for fatigue and dyspnoea, delineating patterns of symptom persistence following COVID-19. Sex, age, smoking status, participation in lifestyle interventions and COVID-19 severity were associated with the likelihood of belonging to different trajectories. These findings highlight the heterogeneity of the long-term symptoms experienced by post COVID-19 patients and emphasise the importance of personalised treatment strategies.

Exploring the developmental changes in and the relationship between resilience and anxiety and depression in older gastric cancer survivors: A latent growth mixture model and random intercept cross-lagged panel model analysis.

This study aims to investigate the independent and joint trajectories of resilience, anxiety, and depression symptoms and research the interaction between older gastric cancer survivors' resilience and anxiety-depression during the period of 1 year after surgery. The participants were 381 older gastric cancer survivors, with a mean age of 69 years. Resilience, anxiety, and depression symptoms were assessed immediately after surgery, and at 3, 6, and 12 months post-surgery, respectively. A longitudinal design was employed to capture the dynamic changes in resilience, anxiety, and depression levels among older gastric cancer survivors post-surgery. Latent growth mixture model and random intercept cross-lagged panel model were used to analyse the trajectories of resilience and anxiety-depression and the interaction between them. Two latent trajectories were identified for the resilience, four latent trajectories were identified for the anxiety, and five latent trajectories were identified for the depression, respectively, with three joint trajectories identified: gradual-increasing resilience, gradual-decreasing anxiety, and depression group (10.0%); gradual-decreasing resilience, gradual-increasing anxiety, and depression group (21.2%); and rapid-growth resilience, rapid-decline anxiety, and depression group (68.8%). Resilience among older gastric cancer survivors exhibits significant bidirectional and negative predictive effects on anxiety and depression. Individual differences in resilience, anxiety, and depression trajectories exist among older gastric cancer survivors, with heterogeneous joint progression patterns, and the relationship between resilience and anxiety-depression appears bidirectional. More sophisticated intervention programmes tailored to the unique characteristics of the relevant trajectories are necessary to enhance resilience and mitigate the risk of anxiety and depression.

Development of the Unbiased Disease Network Using Health Insurance Claims Big Data in South Korea

Abstract Background Research on the overall relationship between disease is needed. The disease network model can comprehensively show the complex relationships between diseases and progression patterns. Existing disease network studies were estimated by univariate relative risks. We aim to develop the unbiased disease network. Methods We used NHIS national data of 50 million patients, collected from 2008 to 2022. We used only the primary diagnosis codes (three-digit codes) and defined 603 disease categories based on the special tabulation list for morbidity in the detailed subcategories of ICD-10, prior research, and the frequency of diseases in the dataset. To develop a more unbiased disease network, we first adjusted for age, sex, period, season, and encounter type, then added the confounding disease exposure according to the Directed Acyclic Graph (DAG) and performed Poisson regression to estimate relative risk (RR) applying Inverse Probability Weighting (IPW). We identified statistically significant disease pairs (RR > 1.10, p < 0.001) and repeated the IPW analysis up to the third round because the network would be updated every round. The significant disease pairs identified in the last round were connected to develop the overall network and subnetworks by age and sex. Results We repeated the process up to the third round for all 346,948 disease pairs and fitted 9,024 pairs as significant. We identified general population disease network and subnetwork per age and sex subgroups. Conclusions These disease networks represented the overall progression relationship between diagnosis codes. Key messages • We developed the unbiased disease network that shows progression patterns by adjusting for confounders and using IPW. • The disease network can be used to extract features from various diagnosis codes in EHR/Claims data, analyze causal effects, and predict disease or healthcare usage using RR or graph embedding.

Diffusion capacity and static hyperinflation as markers of disease progression predict 3-year mortality in COPD: Results from COSYCONET.

Chronic obstructive pulmonary disease (COPD) exhibits diverse patterns of disease progression, due to underlying disease activity. We hypothesized that changes in static hyperinflation or KCO % predicted would reveal subgroups with disease progression unidentified by preestablished markers (FEV1, SGRQ, exacerbation history) and associated with unique baseline biomarker profiles. We explored 18-month measures of disease progression associated with 18-54-month mortality, including changes in hyperinflation parameters and transfer factor, in a large German COPD cohort. Analysing data of 1364 patients from the German observational COSYCONET-cohort, disease progression and improvement patterns were assessed for their impact on mortality via Cox hazard regression models. Association of biomarkers and COPD Assessment test items with phenotypes of disease progression or improvement were evaluated using logistic regression and random forest models. Increased risk of 18-54-month mortality was linked to decrease in KCO % predicted (7.5% increments) and FEV1 (20 mL increments), increase in RV/TLC (2% increments) and SGRQ (≥6 points), and an exacerbation grade of 2 at 18 months. Decrease in KCO % predicted ≥7.5% and an increase of RV/TLC ≥2% were the most frequent measures of 18-month disease progression occurring in ~52% and ~46% of patients, respectively. IL-6 and CRP thresholds exhibited significant associations with medium- and long-term disease measures. In a multicentric cohort of COPD, new markers of current disease activity predicted mid-term mortality and could not be anticipated by baseline biomarkers.

Cystitis glandularis: MR imaging characteristics in 27 patients.

To explore the diagnostic characteristics of cystitis glandularis (CG) using magnetic resonance imaging (MRI). A retrospective study was conducted on pathologically confirmed patients who underwent bladder MRI examination between January 2019 and November 2023. Image analysis was jointly conducted, with emphasis on lesion location, morphology, size, signal intensity, and pattern of enhancement, by two genitourinary radiologists with 22 and 15 years of experience, respectively. A total of 27 patients with 27 lesions were included (median age 47 years, 24 males). The lesions were mostly located in the bladder trigone area (18/27). The lesions could be categorized as focal thickening (17/27), nodular (8/27), and diffuse thickening of the entire bladder (2/27) in morphological terms. On T2-weighted imaging (T2WI), 15 of 17 focal thickening lesions appeared as a slightly hyperintense thickened inner layer, with a higher signal in the center of the thickened inner layer, resembling a sandwich sign, and 6 of 8 nodular lesions were slightly hyperintense. On T1-weighted imaging (T1WI), 19 patients showed slight hypointensity. The lesions on DWI showed mainly high (5/27) and slightly high signal (21/27), with an average mean apparent diffusion coefficient (mADC) value of 2.171 ± 0.052 × 10-3mm2/s. Among the 23 patients who underwent dynamic contrast-enhanced (DCE) scanning, 18 lesions showed mild enhancement in the arterial phase (average 1.7 times comparing to unenhanced phase), and the degree of enhancement gradually increased in the venous and delayed phases (average 2.2 and 2.3 times compared to the unenhanced phase, respectively), showing a progressive enhancement pattern. On MRI, the majority of CG manifest as focal thickening or nodules in the bladder trigone area, showing slight hyperintensity on T2WI, slight hypointensity on T1WI, and a progressive enhancement pattern, without significant restriction on DWI. Focal thickening lesions may exhibit a special sandwich sign.

Clinical ocular prediction model of postoperative ametropic amblyopia in patients with congenital ectopia lentis.

Despite prompt and appropriate surgical management, a considerable proportion of patients with congenital ectopia lentis (CEL) suffer from postoperative ametropic amblyopia. To predict and identify at-risk patients early, and ensure timely amblyopia treatment, we conducted a thorough investigation into the onset and progression patterns of postoperative amblyopia in patients with CEL. Moreover, an ocular prediction model was constructed for amblyopia. In this prospective cohort study, amblyopia analysis was conducted to reveal the prevalence of postoperative amblyopia at different time points of follow-up. Comparative analysis and logistic regression analysis were performed for the development of an amblyopia prediction model. Receiver Operating Characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA) were used to evaluate the performance of the model. A nomogram was created to determine the probability of postoperative amblyopia. Amblyopia was diagnosed according to the most recent edition of the Amblyopia Preferred Practice Pattern. A total of 889 eyes from 677 patients operated for CEL were enrolled in this study. In the pediatric cohort, the prevalence of amblyopia showed a decreasing trend with follow-up time from 1 month to 3.5 years. A prediction model based on preoperative best-corrected visual acuity (BCVA) and cardiac phenotype was established to predict postoperative amblyopia. For effective individual prediction, a nomogram was created. With great calibration, discrimination, and clinical usefulness, the prediction model demonstrated good performance. The findings underscore that the prevalence of ametropic amblyopia in pediatric CEL patients who underwent lens surgery exhibited a marked decline over time. The prediction model established with preoperative BCVA and cardiac phenotype can provide accurate and individualized predictions of postoperative amblyopia, and it has the potential to assist ophthalmologists in rapidly identifying high-risk patients.

Large Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.

Osteosarcoma is an aggressive bone cancer lacking robust biomarkers for personalized treatment. Despite its scarcity in humans, it is relatively common in adult pet dogs. This study aimed to analyze clinically annotated bulk tumor transcriptomic datasets of canine and human osteosarcoma patients to identify potentially conserved patterns of disease progression. Bulk transcriptomic data from 245 pet dogs with treatment-naïve appendicular osteosarcoma were analyzed using deconvolution to characterize the tumor microenvironment (TME). The TME of both primary and metastatic tumors derived from the same dog was compared, and its impact on canine survival was assessed. A machine learning model was developed to classify the TME based on its inferred composition using canine tumor data. This model was applied to 8 independent human osteosarcoma datasets to assess its generalizability and prognostic value. This study found three distinct TME subtypes of canine osteosarcoma based on cell type composition of bulk tumor samples: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM), and Immune Desert (ID). These three TME-based subtypes of canine osteosarcomas were conserved in humans and could predict progression-free survival outcomes of human patients, independent of conventional prognostic factors such as percent tumor necrosis post standard of care chemotherapy treatment and disease stage at diagnosis. These findings demonstrate the potential of leveraging data from naturally occurring cancers in canines to model the complexity of the human osteosarcoma TME, offering a promising avenue for the discovery of novel biomarkers and developing more effective precision oncology treatments.

Unsupervised clustering of longitudinal clinical measurements in electronic health records.

Longitudinal electronic health records (EHR) can be utilized to identify patterns of disease development and progression in real-world settings. Unsupervised temporal matching algorithms are being repurposed to EHR from signal processing- and protein-sequence alignment tasks where they have shown immense promise for gaining insight into disease. The robustness of these algorithms for classifying EHR clinical data remains to be determined. Timeseries compiled from clinical measurements, such as blood pressure, have far more irregularity in sampling and missingness than the data for which these algorithms were developed, necessitating a systematic evaluation of these methods. We applied 30 state-of-the-art unsupervised machine learning algorithms to 6,912 systematically generated simulated clinical datasets across five parameters. These algorithms included eight temporal matching algorithms with fourteen partitional and eight fuzzy clustering methods. Nemenyi tests were used to determine differences in accuracy using the Adjusted Rand Index (ARI). Dynamic time warping and its lower-bound variants had the highest accuracies across all cohorts (median ARI>0.70). All 30 methods were better at discriminating classes with differences in magnitude compared to differences in trajectory shapes. Missingness impacted accuracies only when classes were different by trajectory shape. The method with the highest ARI was then used to cluster a large pediatric metabolic syndrome (MetS) cohort (N = 43,426). We identified three unique childhood BMI patterns with high average cluster consensus (>70%). The algorithm identified a cluster with consistently high BMI which had the greatest risk of MetS, consistent with prior literature (OR = 4.87, 95% CI: 3.93-6.12). While these algorithms have been shown to have similar accuracies for regular timeseries, their accuracies in clinical applications vary substantially in discriminating differences in shape and especially with moderate to high missingness (>10%). This systematic assessment also shows that the most robust algorithms tested here can derive meaningful insights from longitudinal clinical data.

Associations between socioeconomic status and dynamic development of physical,psychological and cognitive degenerative multimorbidity among middle aged and older adults in China

Objective: To analyze the dynamic development of physical, psychological, and cognitive degenerative multimorbidity among middle-aged and older Chinese adults (≥45 years old) while estimating the longitudinal association between socioeconomic status (SES) and the progression of multimorbidity. Methods: Based on data from the China Health and Retirement Longitudinal Study (2011-2020), the Sankey diagram was used to show the dynamic development of physical, psychological, and cognitive degenerative multimorbidity from 2011 to 2020. SES was constructed based on the level of education and total household wealth. Logistic regression was used to estimate OR and 95%CI to evaluate the association between SES and the progression of multimorbidity. Results: Of the 5 393 participants included, 4 484 (83.14%) of them developed new diseases, and the prevalence of physical, psychological, and cognitive degenerative multimorbidity increased from 38.04% to 74.23%. Compared to those with no reported disorders at baseline, participants with psychological disorder (for newly developed physical-cognitive multimorbidity: OR=4.59,95%CI: 2.89-7.29), cognitive disorder (for newly developed physical-psychological multimorbidity: OR=2.24,95%CI: 1.40-3.60), or their multimorbidity at baseline were more likely to progress to physical, psychological, and cognitive degenerative multimorbidity. After adjusting covariates, individuals with low SES were more likely to develop physical diseases (OR=1.45, 95%CI: 1.11-1.89), cognitive disorder (OR=1.84, 95%CI: 1.16-2.91), physical-psychological multimorbidity (OR=1.87, 95%CI: 1.37-2.56), physical-cognitive multimorbidity (OR=3.58, 95%CI: 2.54-5.06), psychological-cognitive multimorbidity (OR=5.66, 95%CI: 3.04-10.55), and physical-psychological-cognitive multimorbidity (OR=3.21, 95%CI: 2.06-5.01) in comparison to those with high SES. There is a dose-response relationship between SES and the multimorbidity progression (all trend P<0.001). Conclusions: The prevalence of physical, psychological, and cognitive degenerative multimorbidity increased significantly among middle-aged and older Chinese adults. Lower SES was associated with multiple patterns of physical, psychological, and cognitive disorders progression.

Kidney growth progression patterns in autosomal dominant polycystic kidney disease

BackgroundPrognosis for autosomal dominant polycystic kidney disease (ADPKD), the main inherited cause of kidney failure, relies on estimating cystic growth using linear formulas derived from height-adjusted total kidney volume (Ht-TKV). However, nonlinear renal growth patterns may occur in typical ADPKD. AimsTo determine kidney outcomes of subjects diagnosed with typical ADPKD exhibiting nonlinear, and unpredictable cystic growth during follow-up. MethodsRetrospective cohort study. We categorized TKV changes in individuals with typical ADPKD according to observed kidney growth trajectories. Ht-TKV was calculated from consecutive CT or MRI using the ellipsoid method. We compared estimated glomerular filtration rate (eGFR) trajectories with linear mixed models. ResultsWe included 83 individuals with ADPKD (67% women; age 47 ± 12 years; follow-up 5.2 years [IQR 2.8–9.0]). Three kidney growth patterns were observed: slow progression (24%, <3%/year linear increase), fast progression (39%, ≥3%/year linear increase), and atypical progression (37%, nonlinear growth). Adjusted ht-TKV change in mL/m/year was +1.4 (IQR –4.5 to +10.0), +40.3 (+16.9 to +89.3), and +32.8 (+15.9 to +85.9) for slow, fast, and atypical progressors, respectively (p <0.001). Atypical progressors exhibited a significantly greater decline in eGFR in mL/min/m²/year (–7.9, 95% CI –6.5, –3.9) compared to slow (–0.5, 95% CI –3.1 to +0.5) and fast progressors (–3.4, 95% CI –7.9, –2.0; between-group p <0.001). Atypical progressors had a higher proportion of acute complications, including hemorrhages, infections, and urolithiasis (84%), compared to slow (20%) and fast progressors (31%) (p <0.001). ConclusionIn typical ADPKD, nonlinear, abrupt, and unpredictable cyst growth occurs frequently, leading to a higher risk of acute complications and kidney function decline.

A comparative study of thematic choices and thematic progression patterns in human-written and AI-generated texts

By adopting systemic functional grammar as the theoretical framework, the present research investigates the choices of the textual, the interpersonal, the topical Themes, and thematic progression patterns in human-written and AI-generated argumentative texts to examine how the human author and the robot organize the clause and text. The findings suggest that human-written and AI-generated texts differ significantly in using the subtypes of textual, interpersonal, and marked topical Themes. In terms of textual Themes, the machine tends to use more concession signals to repeat information that has been stated earlier, adopt fewer condition signals since the robot may be less likely to imagine possible or impossible situations, and repetitively rely on clause-initial conjunctive adjuncts of addition to extend the clause to the previous text. As for interpersonal Themes, the robot seldom adopts modal adjuncts or modal verbal operators as interpersonal Themes, which may suggest its lack of awareness of interaction with the reader and its avoidance of expressing its viewpoints in a typical or congruent way. Regarding topical Themes, the less use of marked themes in AI-generated texts demonstrates that AI may less carefully plan the development of the text to foreground the setting or construct coherent text. Considering thematic progression patterns, the frequent use of the constant pattern in AI-generated texts prevents the text from development and makes the text redundant and simplistic like a list of ideas.

MA06.07 Patterns of Progression with Lorlatinib and Insights into Subsequent Anticancer Therapy Efficacy in Advanced ALK+ NSCLC

MA06.07 Patterns of Progression with Lorlatinib and Insights into Subsequent Anticancer Therapy Efficacy in Advanced ALK+ NSCLC

Myopic maculopathy progression: insights into posterior staphyloma and macular involvement.: Short title: Myopic maculopathy progression.

Purpose: To analyze the myopic maculopathy's progression pattern. Design: Retrospective, observational case series. Methods: 824 highly myopic eyes (axial length ≥26mm) of 430 patients examined at Puerta de Hierro-Majadahonda University Hospital (Madrid, Spain) with a minimum follow-up of 2 years. All patients underwent complete ophthalmologic examination, including multimodal imaging. Progression of myopic maculopathy was assessed according to ATN classification system at baseline and throughout the follow-up, as well as associated clinical features. Results: Progression of myopic maculopathy was described in 42% of eyes over a mean follow-up of 4.10±1.28 (range: 2.00 to 6.23) years. Compared to non-progressing eyes, myopic maculopathy progression correlated with worse best-corrected visual acuity (BCVA) at follow-up (p<0.05) and greater BCVA loss (p<0.01). There were no significant differences between groups in axial length, gender, and age (p>0.05). Eyes with posterior staphyloma (PS) and, particularly, those with macular PS showed significantly greater myopic maculopathy progression (p<0.01). The likelihood of myopic maculopathy's progression was 3.94 times higher (OR: 3.94±1.22, p<0.01) in eyes with PS compared to those without PS. Atrophic progression occurred in 21.9% of eyes, with diffuse to patchy atrophy being the most common pattern (54.17%). Tractional progression was observed in 22.8% of eyes, predominantly from non-traction to inner/outer foveoschisis (40%). Neovascular progression affected 11.4% of the eyes, most of them from no-neovascular component to macular lacquer cracks (40%). Conclusions: The study highlights significant myopic maculopathy progression over the natural course of the disease, compromising the BCVA as macular complications progress. Myopic maculopathy progression was significantly associated with PS and, especially, with those with macular involvement.

Multiomics Reveals Biological Mechanisms Linking Macroscale Structural Covariance Network Dysfunction With Neuropsychiatric Symptoms Across the Alzheimer’s Disease Continuum

BackgroundThe highly heterogeneity of neuropsychiatric symptoms (NPSs) hinder further exploration of their role in neurobiological mechanisms and Alzheimer’s disease (AD). We aimed to delineate NPS patterns based on brain macroscale connectomics to understand the biological mechanisms of NPSs on the AD continuum. MethodsWe constructed Regional Radiomics Similarity Networks (R2SN) for 550 participants (AD with NPSs [AD-NPS, n=376], AD without NPSs [AD-nNPS, n=111], and normal controls [n=63]) from CIBL study. We identified R2SN connections associated with NPSs, and then cluster distinct subtypes of AD-NPS. An independent dataset (n=189) and internal validation were performed to assess the robustness of the NPS subtypes. Subsequent multiomics analysis were performed to assess the distinct clinical phenotype and biological mechanisms in each NPS subtype. ResultsAD-NPS patients were clustered into severe (n=187), moderate (n=87), and mild NPS (n=102) subtypes, each exhibiting distinct brain network dysfunction patterns. A high level of consistency in clustering NPS was internally and externally validated. Severe and moderate NPSs showed significant cognitive impairment, increased plasma p-Tau181 levels, extensive decreased brain volume and cortical thickness, and accelerated cognitive decline. Gene set enrichment analysis (GSEA) revealed enrichment of differentially expressed genes in ion transport and synaptic transmission with variations for each NPS subtype. Genome-wide association studies (GWAS) analysis defined the specific gene loci for each subtype of AD-NPS (i.e, logical memory), aligning with clinical manifestations and progression patterns. ConclusionsThis study identified and validated three distinct NPS subtypes, underscoring the role of NPSs in neurobiological mechanisms and progression of the AD continuum.

Adding immune checkpoint inhibitors to chemotherapy confers modest survival benefit in patients with small cell lung cancer and brain metastases: a retrospective analysis.

Brain metastases (BM) are highly prevalent and associated with a poor prognosis in patients with small cell lung cancer (SCLC). However, the evidence regarding the efficacy of immune checkpoint inhibitors (ICIs) in combination with chemotherapy for patients with SCLC and BM remains limited. Therefore, the objective of this study is to evaluate whether the addition of ICIs confers survival benefits for patients with SCLC and BM. This retrospective study enrolled patients with SCLC and BM at the Sun Yat-sen University Cancer Center between January 2018 and December 2022. Clinical characteristics were extracted from medical records. Depending on whether ICIs were added to the first-line treatment, the patients were categorized into the chemotherapy group and the chemoimmunotherapy group. The efficacy of these two treatment approaches was analyzed and compared. A total of 165 patients were enrolled, with 85 in the chemotherapy group and 80 in the chemoimmunotherapy group. The chemoimmunotherapy group showed a tendency towards prolonged intracranial [6.6 vs. 5.9 months, hazard ratio (HR) =0.77; P=0.14] and extracranial (6.9 vs. 6.5 months, HR =0.73; P=0.12) progression-free survival (PFS) and overall survival (OS) (15.6 vs. 14.5 months, HR =0.98; P=0.93) compared to the chemotherapy group. Cox regression analysis identified liver metastases and local treatment for BM as independent prognostic factors for OS in patients. Furthermore, the chemotherapy group and the chemoimmunotherapy group demonstrated similar patterns of initial disease progression. Adding ICIs to chemotherapy confers modest survival benefits in patients with SCLC and BM.

Brain-first forms of Parkinson's disease are over-represented in patients with non-responsive resting tremor

Motor subtypes in Parkinson's Disease (PD) are unstable over time, limiting mechanistic insights and biomarker discovery. We focused on Rest Tremor (RT) as a symptom to test for phenotype stability and link it to specific circuits and disease mechanisms. Using the PPMI cohort data over 5 years we found that RT is more stable than classical Tremor-Dominant definitions, a stability also seen for RT response to therapy. At time of diagnosis, the population of therapy-resistant RT patients was enriched with a brain-first PD profile as predicted by a-Synuclein origin site and connectome (SOC) model. Resistant-RT patients have lower gastrointestinal and cardiovascular symptoms, lower prevalence of probable REM-Sleep behaviour disorder, and higher dopaminergic asymmetry compared to therapy-responsive or no tremor patients. Treating RT as a distinct phenomenon revealed a relative phenotypic stability with treatment response being linked to different patterns of disease progression.