Chronic myeloid leukemia (CML) is treated with tyrosine kinase inhibitors (TKI) that target the pathological BCR-ABL1 fusion oncogene. The objective of this statistical meta-analysis was to assess the prevalence of other hematological adverse events (AEs) that occur during or after predominantly first-line treatment with TKIs. Data from seventy peer-reviewed, published studies were included in the analysis. Hematological AEs were assessed as a function of TKI drug type (dasatinib, imatinib, bosutinib, nilotinib) and CML phase (chronic, accelerated, blast). AE prevalence aggregated across all severities and phases was significantly different between each TKI (p < 0.05) for anemia—dasatinib (54.5%), bosutinib (44.0%), imatinib (32.8%), nilotinib (11.2%); neutropenia—dasatinib (51.2%), imatinib (29.8%), bosutinib (14.1%), nilotinib (14.1%); thrombocytopenia—dasatinib (62.2%), imatinib (30.4%), bosutinib (35.3%), nilotinib (22.3%). AE prevalence aggregated across all severities and TKIs was significantly (p < 0.05) different between CML phases for anemia—chronic (28.4%), accelerated (66.9%), blast (55.8%); neutropenia—chronic (26.7%), accelerated (63.8%), blast (36.4%); thrombocytopenia—chronic (33.3%), accelerated (65.6%), blast (37.9%). An odds ratio (OR) with 95% confidence interval was used to compare hematological AE prevalence of each TKI compared to the most common first-line TKI therapy, imatinib. For anemia, dasatinib OR = 1.65, [1.51, 1.83]; bosutinib OR = 1.34, [1.16, 1.54]; nilotinib OR = 0.34, [0.30, 0.39]. For neutropenia, dasatinib OR = 1.72, [1.53, 1.92]; bosutinib OR = 0.47, [0.38, 0.58]; nilotinib OR = 0.47, [0.42, 0.54]. For thrombocytopenia, dasatinib OR = 2.04, [1.82, 2.30]; bosutinib OR = 1.16, [0.97, 1.39]; nilotinib OR = 0.73, [0.65, 0.82]. Nilotinib had the greatest fraction of severe (grade 3/4) hematological AEs (30%). In conclusion, the overall prevalence of hematological AEs by TKI type was: dasatinib > bosutinib > imatinib > nilotinib. Study limitations include inability to normalize for dosage and treatment duration.

This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

Parkinson’s disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa–carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.

We perform a large-scale meta-analysis of 51 peer-reviewed 3xTg-AD mouse publications to compare Alzheimer's disease (AD) quantitative clinical outcome measures, including amyloid-β (Aβ), total tau, and phosphorylated tau (pTau), with cognitive performance in Morris water maze (MWM) and Novel Object Recognition (NOR). "High" levels of Aβ (Aβ 40 , Aβ 42 ) showed significant but weak trends with cognitive decline (MWM: slope = 0.336, R 2 = 0.149, n = 259, p < 0.001; NOR: slope = 0.156, R 2 = 0.064, n = 116, p < 0.05); only soluble Aβ or directly measured Aβ meaningfully contribute. Tau expression in 3xTg-AD mice was within 10-20 of wild type and not associated with cognitive decline. In contrast, increased pTau is directly and significantly correlated with cognitive decline in MWM (slope = 0.408, R 2 = 0.275, n = 371, p < < 0.01) and NOR (slope = 0.319, R 2 = 0.176, n = 113, p < 0.05). While a variety of pTau epitopes (AT8, AT270, AT180, PHF-1) were examined, AT8 correlated most strongly with cognition (slope = 0.586, R 2 = 0.521, n = 185, p < < 0.001). Multiple linear regression confirmed pTau is a stronger predictor of MWM performance than Aβ. Despite pTau's lower physical concentration than Aβ, pTau levels more directly and quantitatively correlate with 3xTg-AD cognitive decline. pTau's contribution to neurofibrillary tangles well after Aβ levels plateau makes pTau a viable treatment target even in late-stage clinical AD. Principal component analysis, which included hyperphosphorylation induced by kinases (pGSK3β, GSK3β, CDK5), identified phosphorylated ser9 GSK3β as the primary contributor to MWM variance. In summary, meta-analysis of cognitive decline in preclinical AD finds tauopathy more impactful than Aβ. Nonetheless, complex AD interactions dictate successful therapeutics harness synergy between Aβ and pTau, possibly through the GSK3 pathway.

Objective: It is hypothesized earlier non-invasive (NIV) ventilation benefits Amyotrophic Lateral Sclerosis (ALS) patients. NIV typically consists of the removable bi-level positive airway pressure (Bi-PAP) for adjunctive respiratory support and/or the cough assist intervention for secretion clearance. Historical international standards and current USA insurance standards often delay NIV until percent predicted forced vital capacity (FVC %predict) is < 50. We identify the optimal point for Bi-PAP initiation and the synergistic benefit of daily Bi-PAP and cough assist on associative increases in survival duration. Methods: Study population consisted of a retrospective ALS cohort (Emory University, Atlanta, GA, USA). Primary analysis included 474 patients (403 Bi-PAP users, 71 non-users). Survival duration (time elapsed from baseline onset until death) is compared on the basis of Bi-PAP initiation threshold (FVC %predict); daily Bi-PAP usage protocol (hours/day); daily cough assist usage (users or non-users); ALS onset type; ALSFRS-R score; and time elapsed from baseline onset until Bi-PAP initiation, using Kruskal-Wallis one-way analysis of variance and Kaplan Meier. Results: Bi-PAP users' median survival (21.03 months, IQR = 23.97, N = 403) is significantly longer (p < 0.001) than non-users (13.84 months, IQR = 11.97, N = 71). Survival consistently increases (p < 0.01) with FVC %predict Bi-PAP initiation threshold: < 50% (20.3 months); ≥50% (23.60 months); ≥80% (25.36 months). Bi-PAP usage > 8 hours/day (23.20 months) or any daily Bi-PAP usage with cough assist (25.73 months) significantly (p < 0.001) extends survival compared to Bi-PAP alone (15.0 months). Cough assist without Bi-PAP has insignificant impact (14.17 months) over no intervention (13.68 months). Except for bulbar onset Bi-PAP users, higher ALSFRS-R total scores at Bi-PAP initiation significantly correlate with higher initiation FVC %predict and longer survival duration. Time elapsed since ALS onset is not a good predictor of when NIV should be initiated. Conclusions: The "optimized" NIV protocol (Bi-PAP initiation while FVC %predict ≥80, Bi-PAP usage > 8 h/day, daily cough assist usage) has a 30. 8 month survival median, which is double that of a "standard" NIV protocol (initiation FVC %predict < 50, usage > 4 h/day, no cough assist). Earlier access to Bi-PAP and cough assist, prior to precipitous respiratory decline, is needed to maximize NIV synergy and associative survival benefit.

Objective: The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the “best” survival predictors and how their predictive ability changes as a function of disease progression. Methods: Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model). Dimensionality reduction and feature clustering enabled decomposition of clinical variable contributions. Thirty-eight metrics were utilized, including Medical Research Council (MRC) muscle scores; respiratory function, including forced vital capacity (FVC) and FVC % predicted, oxygen saturation, negative inspiratory force (NIF); the Revised ALS Functional Rating Scale (ALSFRS-R) and its activities of daily living (ADL) and respiratory sub-scores; body weight; onset type, onset age, gender, and height. Prognostic random forest models confirm the dominance of patient age-related parameters decline in classifying survival at thresholds of 30, 60, 90, and 180 days and 1, 2, 3, 4, and 5 years. Results: Collective prognostic insight derived from the overall investigation includes: multi-dimensionality of ALSFRS-R scores suggests cautious usage for survival forecasting; upper and lower extremities independently degenerate and are autonomous from respiratory decline, with the latter associating with nearer-to-death classifications; height and weight-based metrics are auxiliary predictors for farther-from-death classifications; sex and onset site (limb, bulbar) are not independent survival predictors due to age co-correlation. Conclusion: The dimensionality and fluctuating predictors of ALS survival must be considered when developing predictive models for clinical trial development or in-clinic usage. Additional independent metrics and possible revisions to current metrics, like the ALSFRS-R, are needed to capture the underlying complexity needed for population and personalized forecasting of survival.

Multiple studies have shown that antecedent diseases are less prevalent in amyotrophic lateral sclerosis (ALS) patients than the general age-matched population, which suggests possible neuroprotection. Antecedent disease could be protective against ALS or, conversely, the asymptomatic early physiological underpinnings of ALS could be protective against other antecedent disease. Elucidating the impact of antecedent disease on ALS is critical for assessing diagnostic risk factors, prognostic outcomes, and intervention timing. The objective of this study was to examine the relationship between antecedent conditions and ALS onset age and disease duration (i.e. survival). Medical history surveys for 1439 Emory ALS Clinic patients (Atlanta, GA, USA) were assessed for antecedent hypertension, hyperlipidemia, diabetes, obesity, asthma, arthritis, chronic obstructive pulmonary disease (COPD), thyroid, kidney, liver, and other non-ALS neurological diseases. The ALS onset age and disease duration are compared between the antecedent and non-antecedent populations using chi square, Kaplan-Meier, and ordinal logistic regression. When controlled for confounders, antecedent hypertension (high blood pressure), hyperlipidemia (high cholesterol), arthritis, COPD, thyroid disease, and non-ALS neurological disease are found to be statistically associated with a delayed ALS onset age, whereas antecedent obesity [body mass index (BMI) > 30] was correlated to earlier ALS onset age. With the potential exceptions of liver disease and diabetes (the latter without other common comorbid conditions), antecedent disease is associated with overall shorter ALS disease duration. The unique potential relationship between antecedent liver disease and longer ALS disease duration warrants further investigation, especially given liver disease was found to be a factor of 4-7 times less prevalent in ALS. Notably, most conditions associated with delayed ALS onset are also associated with shorter disease duration. Pathological homeostatic instability exacerbated by hypervigilant regulation (over-zealous homeostatic regulation due to too high regulatory feedback gains) is a viable hypothesis for explaining the early-life protection against antecedent disease and the overall lower antecedent disease prevalence in ALS patients; the later ALS onset age in patients with antecedent disease; and the inverse relationship between ALS onset age and disease duration.

Numerous sub-cellular through system-level disturbances have been identified in over 1300 articles examining the superoxide dismutase-1 guanine 93 to alanine (SOD1-G93A) transgenic mouse amyotrophic lateral sclerosis (ALS) pathophysiology. Manual assessment of such a broad literature base is daunting. We performed a comprehensive informatics-based systematic review or field analysis to agnostically compute and map the current state of the field. Text mining of recaptured articles was used to quantify published data topic breadth and frequency. We constructed a nine-category pathophysiological function-based ontology to systematically organize and quantify the field's primary data. Results demonstrated that the distribution of primary research belonging to each category is: systemic measures an motor function, 59%; inflammation, 46%; cellular energetics, 37%; proteomics, 31%; neural excitability, 22%; apoptosis, 20%; oxidative stress, 18%; aberrant cellular chemistry, 14%; axonal transport, 10%. We constructed a SOD1-G93A field map that visually illustrates and categorizes the 85% most frequently assessed sub-topics. Finally, we present the literature-cited significance of frequently published terms and uncover thinly investigated areas. In conclusion, most articles individually examine at least two categories, which is indicative of the numerous underlying pathophysiological interrelationships. An essential future path is examination of cross-category pathophysiological interrelationships and their co-correspondence to homeostatic regulation and disease progression.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive degradation of motoneurons in the central nervous system (CNS). Astrocytes are key regulators for inflammation and neuromodulatory signaling, both of which contribute to ALS. The study goal was to ascertain potential temporal changes in astrocyte-mediated neuromodulatory regulation with transgenic ALS model progression: glutamate, GTL-1, GluR1, GluR2, GABA, ChAT activity, VGF, TNFα, aspartate, and IGF-1. We examine neuromodulatory changes in data aggregates from 42 peer-reviewed studies derived from transgenic ALS mixed cell cultures (neurons + astrocytes). For each corresponding experimental time point, the ratio of transgenic to wild type (WT) was found for each compound. ANOVA and a student's t-test were performed to compare disease stages (early, post-onset, and end stage). Glutamate in transgenic SOD1-G93A mixed cell cultures does not change over time (p > 0.05). GLT-1 levels were found to be decreased 23% over WT but only at end-stage (p < 0.05). Glutamate receptors (GluR1, GluR2) in SOD1-G93A were not substantially different from WT, although SOD1-G93A GluR1 decreased by 21% from post-onset to end-stage (p < 0.05). ChAT activity was insignificantly decreased. VGF is decreased throughout ALS (p < 0.05). Aspartate is elevated by 25% in SOD1-G93A but only during end-stage (p < 0.05). TNFα is increased by a dramatic 362% (p < 0.05). Furthermore, principal component analysis identified TNFα as contributing to 55% of the data variance in the first component. Thus, TNFα, which modulates astrocyte regulation via multiple pathways, could be a strategic treatment target. Overall results suggest changes in neuromodulator levels are subtle in SOD1-G93A ALS mixed cell cultures. If excitotoxicity is present as is often presumed, it could be due to ALS cells being more sensitive to small changes in neuromodulation. Hence, seemingly unsubstantial or oscillatory changes in neuromodulators could wreak havoc in ALS cells, resulting in failed microenvironment homeostasis whereby both hyperexcitability and hypoexcitability can coexist. Future work is needed to examine local, spatiotemporal neuromodulatory homeostasis and assess its functional impact in ALS.

Associations of modulators of quality of life (QoL) and survival duration are assessed in the fatal motor neuron disease, Amyotrophic Lateral Sclerosis. Major categories include clinical impression of mood (CIM); physical health; patient social support; and usage of interventions, pharmaceuticals, and supplements. Associations were assessed at p < 0.05 and p < 0.001 significance thresholds using applicable methods (Chi-square, t-test, ANOVA, logistical regression, random forests, Fisher's exact test) within a retrospective cohort of 1585 patients. Factors significantly correlated with positive (happy or normal) mood included family support and usage of bi-level positive airway pressure (Bi-PAP) and/or cough assist. Decline in physical factors like presence of dysphagia, drooling, general pain, and decrease in ALSFRS-R total score or forced vital capacity (FVC) significantly correlated with negative (depressed or anxious) mood (p < 0.05). Use of antidepressants or pain medications had no association with ALS patient mood (p > 0.05), but were significantly associated with increased survival (p < 0.05). Positive patient mood, Bi-PAP, cough assist, percutaneous endoscopic gastrostomy (PEG), and accompaniment to clinic visits associated with increased survival duration (p < 0.001). Of the 47 most prevalent pharmaceutical and supplement categories, 17 associated with significant survival duration increases ranging +4.5 to +16.5 months. Tricyclic antidepressants, non-opioids, muscle relaxants, and vitamin E had the highest associative increases in survival duration (p < 0.05). Random forests, which examined complex interactions, identified the following pharmaceuticals and supplements as most predictive to survival duration: Vitamin A, multivitamin, PEG supplements, alternative herbs, antihistamines, muscle relaxants, stimulant laxatives, and antispastics. Statins, metformin, and thiazide diuretics had insignificant associations with decreased survival.