Exploring Thematic Progressions in Two Excerpts From Amma Darko’s Faceless

This study explores thematic progressions in two excerpts from Amma Darko’s Faceless. The study aims at looking into the different types of thematic progressions used in the selected excerpts to decode there-from the deep meanings linguistically encoded by the author. The study further aims at examining how the different thematic progressions used in the studied texts contribute to their cohesion and contextual coherence. The research appeals to the mixed quantitative and qualitative methodology. Via this method, the number of thematic progression patterns identified has been quantified per excerpt to pave the way to the interpretation of the findings that ensued. The study has arrived at impressive results. Among several others available in the interpretation of the findings subsection, Amma Darko has purposefully not used the Split-Rheme Pattern to avoid a complex writing style that would make her writing not accessible to her readership. The simple linear thematic progression and the overriding theme reiteration patterns extensively used in both texts have allowed the author to emphasize the key thematic points of the studied texts. Moreover, the theme reiteration development strategy used in both texts has provided them with clear focuses. Some of these include skin bleaching, tradition and marriage in Africa, street children, women’s life conditions in Ghana to name but a few. For deeper meanings decoding in the studied excerpts, further studies on discourse-semantics, contextual coherence, conjunctive and lexical relations, as well as experiential and interpersonal meanings could pick up from this article findings.

Claims-Based Network Analysis of Disease Progressions in Complex and Non-Complex Older Adults

Older adults are the fastest growing subset of complex patients with high medical, behavioral, and social needs. Understanding differences in disease progression patterns between complex and non-complex older adults is critical for understanding disease risk and tailoring patient-centered interventions. We identified complex patients as those having frequent medical encounters and multiple chronic conditions within the first year of the study period and non-complex patients as the converse. This study compares the disease progression patterns of (a) complex and (b) non-complex older adults by creating disease progression networks (DPN) from claims data of 762,362 patients (mean age = 73) from 2016 to 2020. We characterized the network size and density between the complex patient DPN (C-DPN) and non-complex patient DPN (NC-DPN), and compared disease progression incidence, time-to-progression, and age- and gender-related risk. Results show that the C-DPN was denser and had a wider range of values for risk of progression compared to the NC-DPN. This implies more varied disease progression patterns occurring in the complex adults. We were also able to compare (median) time-to-progressions of diseases relative to each subpopulation and found variation in disease progression time. Furthermore, k-means clustering on the network allowed us to identify highly connected diseases involved in many disease pathways that are prevalent among older adults. (e.g., lipoprotein disorders, hypertension, major depressive disorder). Our results suggest that DPNs can be used to identify important conditions and time-points for tailoring care to the complex and non-complex older adults.

Association of Progression Patterns in Monoclonal Gammopathy of Undetermined Significance (MGUS) with Outcome in Multiple Myeloma: A Cohort Study of over 35 Years

Introduction: Guidelines suggest following monoclonal gammopathy of undetermined significance (MGUS) according to risk of multiple myeloma progression. Follow-up of low-risk MGUS is debated as progression risk is low. Worse myeloma-related outcome was observed in patients followed for low-risk MGUS, potentially due to less optimal follow-up (Bianchi et al. Blood 2010, Sigurdardottir et al. Jama Oncology 2015). However, it is not clear if progressing low-risk MGUS by its nature displays more aggressive biological behavior. To gain better understanding of progression patterns in MGUS, we investigated, independent of follow-up, whether progression from low-risk MGUS is associated with worse outcome in multiple myeloma. The main outcome was overall survival from the time of myeloma diagnosis. Methods: The Northern Sweden Health and Disease Study (NSHDS) is a longitudinal prospective cohort with more than 100,000 participants. Typically, NSHDS participants donate repeated blood samples at intervals of several years. Samples are frozen within one hour and stored at -80° C at Umea University Hospital. Linkage to the Swedish Cancer Registry facilitated identification of myeloma patients with two pre-diagnostic samples in NSHDS (N = 61). Of these, we screened repeated pre-diagnostic samples using protein- and immunofixation electrophoresis and free light chain assays. We identified 42 individuals who had detectable monoclonal gammopathy in both pre-diagnostic samples without MGUS follow-up before myeloma diagnosis, allowing to investigate natural progression patterns in relation to outcome. Overall survival was determined using Kaplan-Meier plots. Hazard ratios and 95% confidence intervals were calculated using multivariable Cox regression including known prognostic factors. Fisher's exact test was used to compare categorical variables. Results: The first pre-diagnostic sample was collected in November 1986 and the last follow-up since myeloma diagnosis was in February 2021, resulting in a 35-year study duration. Median age at myeloma diagnosis was 62 (range 48-84) with a median follow-up of 7 years (range 0.2-18). At first pre-diagnostic blood draw, 12 had low-risk and 30 had MGUS of other risk (Mayo Clinic criteria). Characteristics at myeloma diagnosis except sex were similarly distributed between the two groups. Comorbidities, myeloma treatment and access to novel drugs were balanced between groups. Bone disease (osteolytic lesions and/or vertebral compression fractures attributable to myeloma) at myeloma diagnosis was more common in patients who had low-risk MGUS at first pre-diagnostic blood draw (P = 0.04). This association was pronounced excluding light chain myeloma (P = 0.01). Access to other radiographic imaging than conventional skeletal survey such as CT or MRI was similar for both groups. In low-risk vs. other MGUS, median overall survival since myeloma diagnosis was 2.3 (95% CI, 1.8-2.9) years and 7.5 (95% CI, 4.8-10.2) years (Figure A). Results were similar investigating overall survival since frontline therapy start (excluding 5 patients not requiring therapy) (Figure B). Sex and bone disease were not associated with survival. At repeated pre-diagnostic blood draw (in median 3.7 years prior myeloma diagnosis), 67% vs. 19% had low- or low-intermediate risk MGUS in patients with low-risk vs. other MGUS at first pre-diagnostic blood draw (P = 0.01), suggesting more rapid progression close to myeloma diagnosis in patients with low-risk MGUS at first blood draw. Investigating this further, we plotted M spikes in low-risk vs. other MGUS of IgG isotype (Figures C-D). M spike trajectories were largely similar between groups, although the annual median M spike increase from repeated pre-diagnostic blood draw to myeloma diagnosis was 6.0 g/L in low-risk vs. 2.3 g/L in other MGUS (P = 0.14). Conclusions: Progression from low-risk MGUS is, independent of MGUS follow-up, associated with a higher proportion of bone disease and worse survival. Based on the known phenotypic heterogeneity in multiple myeloma, we speculate that low-risk MGUS in case of malignant progression belongs to a group of more aggressive tumors. Our results need to be interpreted carefully because of the small sample size. Replication and further investigation are needed. If replicated, these findings could help to improve current MGUS follow-up strategies, which are solely based on progression risk. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost

Background Glioblastoma peritumoral edema (PE) extent is associated with survival and progression pattern after tumor resection and radiotherapy (RT). To increase tumor control, proton beam was adopted to give high-dose boost (> 90 Gy). However, the correlation between PE extent and prognosis of glioblastoma after postoperative high-dose proton boost (HDPB) therapy stays unknown. We intend to utilize the PE status to classify the survival and progression patterns. Methods Patients receiving HDPB (96.6 GyE) were retrospectively evaluated. Limited peritumoral edema (LPE) was defined as PE extent < 3 cm with a ratio of PE extent to tumor maximum diameter of < 0.75. Extended progressive disease (EPD) was defined as progression of tumors extending > 1 cm from the tumor bed edge. Results After long-term follow-up (median 88.7, range 63.6–113.8 months) for surviving patients with (n = 13) and without (n = 32) LPE, the median overall survival (OS) and progression-free survival (PFS) were 77.2 vs. 16.7 months (p = 0.004) and 13.6 vs. 8.6 months (p = 0.02), respectively. In multivariate analyses combined with factors of performance, age, tumor maximum diameter, and tumor resection extent, LPE remained a significant factor for favorable OS and PFS. The rates of 5-year complete response, EPD, and distant metastasis with and without LPE were 38.5% vs. 3.2% (p = 0.005), 7.7% vs. 40.6% (p = 0.04), and 0% vs. 34.4% (p = 0.02), respectively. Conclusions The LPE status effectively identified patients with relative long-term control and specific progression patterns after postoperative HDPB for glioblastoma.

Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data

Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings. Here we propose ‘Ordinal SuStaIn’, an ordinal version of the SuStaIn algorithm that uses a scored events model of disease progression to enable the application of SuStaIn to ordinal data. We demonstrate the validity of Ordinal SuStaIn by benchmarking the performance of the algorithm on simulated data. We further demonstrate that Ordinal SuStaIn out-performs the existing continuous version of SuStaIn (Z-score SuStaIn) on discrete scored data, providing much more accurate subtype progression patterns, better subtyping and staging of individuals, and accurate uncertainty estimates. We then apply Ordinal SuStaIn to six different sub-scales of the Clinical Dementia Rating scale (CDR) using data from the Alzheimer’s disease Neuroimaging Initiative (ADNI) study to identify individuals with distinct patterns of functional decline. Using data from 819 ADNI1 participants we identified three distinct CDR subtype progression patterns, which were independently verified using data from 790 ADNI2 participants. Our results provide insight into patterns of decline in daily activities in Alzheimer’s disease and a mechanism for stratifying individuals into groups with difficulties in different domains. Ordinal SuStaIn is broadly applicable across different types of ratings data, including visual ratings from imaging, neuropathological ratings and clinical or behavioural ratings data.

Comprehensively modeling heterogeneous symptom progression for Parkinson's disease subtyping

The Parkinson's disease (PD) is a heterogeneous neurodegenerative disease, of which the etiological and pathological mechanisms remain unclear to date. PD has been associated with diverse movement dysfunctions and non-motor symptoms (i.e., symptom heterogeneity) and progression patterns of these symptoms differ from patient to patient (i.e., progression heterogeneity). To address these, the present investigation aims at comprehensively considering full progression course of early PDs to identify subtypes, each of which can reflect unique PD progression pattern. We retrospectively analyzed the Parkinson's Progression Markers Initiative (PPMI) and the Parkinson Disease Biomarkers Program (PDBP) as the development and validation cohorts, respectively. An unsupervised deep learning model was built to model progression trajectories in diverse clinical manifestations and cerebrospinal fluid (CSF) biomarkers to produce a representation vector for each patient, encoding his/her symptom progression profile. Then by performing clustering analysis on the patients' representation vectors, we identified three subtypes with distinct PD progression patterns in the PPMI cohort: Subtype I, mild baseline severity and mild symptom progression; mild baseline severity and moderate progression; and Subtype III, rapid symptom progression. Replication in the PDBP validation cohort demonstrated reproducibility of the subtypes. After that, we explored demographic factors, CSF biomarkers, neuroimaging biomarkers in brain regional atrophy, and genetic factors of the subtypes. Last, to enhance usability of the subtypes, predictive model of subtypes that relies on data at baseline and 1-year follow-up was trained. In conclusion, the identified subtypes revealed significant symptom progression patterns of PDs. Patients with similar baseline severities can even suffer from different progression pattern, leading to distinct prognosis. Demographic factors, biomarkers, and genetic components of the subtypes suggested distinct biological mechanisms and pathways potentially leading to those progression patterns. Our findings may benefit pathophysiological study, clinical practice, and clinical trials to advance PD.