Abstract: Cancer is one of the most significant global health problems and a leading cause of death worldwide. The origins of cancer are diverse and may stem from natural biological processes over time or directly result from anthropogenic activities. The complete elimination of all cancer-causing events within a living organism is highly unlikely. A more promising strategy would be to prevent tumors entirely by making the organism an unlivable environment where cancerous cells cannot survive. Surprisingly, this plausible alternative remains virtually unexplored. For the most part, it is not about the odds of cancer cells emerging in a hostile environment, rather than about their ability to adapt and persist within it. Plants have a broad-spectrum mechanism of defense against pathogens called non-host resistance (NHR), when an entire plant species is resistant to all isolates of a microbial species. While the NHR is effective against pathogens whereas cancer cells are perceived by an organism as “self” or “altered self”, they are still de facto “foreign intruders” since they generate neoantigens, novel proteins absent from normal tissues. Considering this, what are the theoretical possibilities of making a human organism an absolute non-host for cancer? As nearly all basic mechanisms and components of the NHR in plants have similarities to cancer responses in vertebrates, the task might be more feasible than it appears.

Abstract: Background: Urothelial carcinoma (UC) of the bladder exhibits low- and high-grade papillary forms with distinct prognoses. High mobility group proteins (HMGA1, HMGA2, HMGB1) and miR‑106a‑5p are involved in tumor progression, but their interplay in UC remains incompletely understood. The aim of this study was to compare the expression of these parameters in low- and high-grade papillary UC. Methods: Tissue samples from 80 patients (40 low-grade and 40 high-grade) undergoing transurethral resection or cystectomy were analyzed, with control samples consisting of tumor-adjacent tissues without histopathological alterations obtained from the same patients. HMGA1, HMGA2, and HMGB1 protein expression was assessed immunohistochemically. Gene expression was quantified by real-time PCR, and miR‑106a‑5p levels were measured by droplet digital PCR. Statistical analysis was conducted using Statistica 13.3, applying one-way ANOVA with Tukey’s post hoc test and correlation analysis, with p < 0.05 considered significant. Results: Expression of HMGA1 and HMGB1 was reduced in low-grade papillary urothelial carcinoma compared to control tissues, whereas both proteins were significantly increased in high-grade lesions. HMGA2 expression was minimal in low-grade tumors but partially restored in high-grade tumors. Analysis revealed the highest levels of miR-106a-5p in normal urothelium, slightly decreaseed in low-grade tumors, and significantly reduced in high-grade carcers. Conclusions: HMG proteins and miR‑106a‑5p demonstrate distinct expression patterns in low- versus high-grade papillary UC, which correlates with tumor aggressiveness. These molecules may serve as diagnostic and prognostic biomarkers and potential therapeutic targets. Further research is needed to clarify the underlying mechanisms and validate clinical applicability.

Abstract: As a renewable energy source, solar energy holds significant potential for addressing future energy and environmental challenges. Concurrently, hydrogen (H2), as a clean and renewable energy carrier, has garnered substantial attention. Photoelectrocatalytic water splitting to produce H2 represents an emerging green technology for converting solar energy into hydrogen energy, which has been highly valued by researchers. The key to advancing this technology lies in identifying photoelectrode materials with high catalytic activity and stability. In this study, dendritic α-Fe was synthesized via electrodeposition, and the photoelectrocatalytic performance of α-Fe2O3@Fe was enhanced through partial oxidation. This approach effectively addressed the issue of the short carrier transport distance in α-Fe2O3. Specifically, dendritic α-Fe2O3 was partially oxidized after annealing at 300°C for 6 h. The resulting partially oxidized α-Fe2O3@Fe exhibited a photocurrent that was 2.23 times higher than that of the fully oxidized counterpart. The influence of deposition potential on the photoelectrocatalytic performance was systematically explored, and an optimal deposition potential was identified. Additionally, surface modification with Pt was employed to further improve the photocatalytic performance of α-Fe2O3. After continuous operation for 2 h, the photocurrent of the surface-modified sample decreased by only 6.5%, indicating a substantial enhancement in stability.

Abstract: Sub-Saharan Africa is undergoing a rapid nutritional and epidemiological transition, characterized by a rising incidence of Type 2 Diabetes (T2D) among lean populations. While the "Westernization" of the gut microbiome is well-documented globally, the specific ecological shifts driving metabolic dysfunction in the African context remain under-synthesized. This systematic review aims to catalog diet-driven taxonomic alterations and evaluate their mechanistic link to T2D. A systematic search was conducted following PRISMA guidelines using PubMed, Scopus, and Google Scholar (2015–2026). Studies were included if they compared the gut microbiomes of urban vs. rural African populations and reported metabolic outcomes. Quality was assessed using the Newcastle-Ottawa Scale. Twelve studies meeting inclusion criteria were identified, representing a pooled cohort of N=6,097 participants across 22 countries. The analysis revealed a consistent "rural signature" marked by the enrichment of fiber-degrading Treponema and Prevotella ("VANISH" taxa). Urbanization was associated with the significant depletion of these guilds and a compensatory bloom of Bacteroides and Bifidobacterium. This taxonomic shift was statistically correlated with elevated fasting blood glucose, HbA1c, and diabetes prevalence in urban cohorts. Mechanistically, the loss of Treponema likely compromises SCFA-mediated insulin signaling, while the rise of mucin-degrading Bacteroides may drive metabolic endotoxemia. Urbanization in Africa is associated with the loss of key ancestral microbes that protect metabolic health. Given that commercial probiotics often contain taxa already enriched in urban Africans (Bifidobacterium), we propose that indigenous fermented foods (e.g., Ogi, Kunu), which provide a Lactobacillus-rich acidic niche, represent a more ecologically appropriate therapeutic strategy to restore metabolic homeostasis. All identified associations are observational, and causal inferences cannot be established.

Abstract: Introduction: Sequencing therapy in CD is currently intensively discussed due to the development of novel drugs and lack of standardized criteria for drug positioning in first and further treatment lines. The aim of this study was to compare the efficacy of a second line advanced therapy in Romanian patients with CD who have failed an anti-TNF agent. Methods: We performed a multicenter retrospective study that included adult patients with CD who had secondary loss of response after an initial response with an an-ti-TNF drug. The main outcome was clinical remission at 12 weeks of second-line treatment (CDAI < 150). Secondary outcomes included clinical response (decrease of CDAI ≥ 25%), persistence of therapy at 1 year and rates of adverse events. Results: From 2008 to 2024, 216 patients were either switched to another anti-TNF or swapped to another therapeutic class due to failure of a first anti-TNF drug. Secondary lines of treatment in-cluded infliximab (IFX), adalimumab (ADA), vedolizumab (VDZ), ustekinumab (UST). The highest rate of clinical remission (81%) was obtained with the sequence ADA-IFX in 26/32(81%) patients and ADA-UST in 62/82(76%) patients, followed by IFX-UST in 22/33(67%) and IFX-ADA 34/51(67%). Persistence on therapy at 1 year was better for the sequence ADA-UST(73%) and IFX-UST(67%) and ADA-IFX(63%) compared to IFX-ADA(59%) and IFX-VDZ(44%)(p< 0.001). Conclusions: In patients with CD who have failed a first anti-TNF, the highest rate of clinical re-mission at 12 weeks was obtained with second line IFX and UST whilst vedolizumab showed lower efficacy. UST demonstrated the most favorable long-term treatment persistence at 1 year.

Abstract: Biodiversity in Ethiopia is under mounting pressure from deforestation, agricultural expansion, climate change, and other anthropogenic pressures, yet conventional conservation approaches often fail to protect species and ecosystems effectively. Dominated by Western scientific paradigms, these approaches treat the ecosystems as isolated from human activity, relying on centralized management, protected areas, and technical interventions. In human-dominated landscapes, such models frequently overlook the adaptive capacities embedded within Indigenous Knowledge (IK) systems. Developed through generations of observation, experimentation, and social regulation, IK encodes a detailed understanding of species behavior, landscape dynamics, and sustainable resource use, functioning as a dynamic ecological system. This essay argues that integrating Indigenous knowledge into biodiversity conservation is an Ecological imperative rather than a moral preference. Evidence from Ethiopian church forests, pastoralist rangelands, and Konso terraces shows that IK sustains biodiversity, ecosystem resilience, and livelihoods. While highly effective, IK is not uniform or universally sufficient; demographic change, market pressures, and internal social inequalities can constrain its impact. Recognizing both the strengths and limits of IK, and embedding it within multi-level governance systems is essential for designing resilient, socially legitimate conservation strategies. Thoughtful integration transforms Indigenous stewardship from a cultural practice into a strategic tool for sustaining ecosystems in climatically variable, human-occupied landscapes.

Abstract:

Background: Intraoperative hypotension (IOH) is strongly associated with postoperative myocardial injury, acute kidney injury, and mortality. Current monitoring relies on reactive threshold alarms, often alerting clinicians only after hemodynamic compromise has occurred. We hypothesized that a machine learning (ML) approach utilizing engineered hemodynamic volatility features could predict IOH five minutes before its occurrence. Methods: A retrospective observational study was conducted using high-resolution intraoperative monitoring data from the VitalDB registry. The cohort included 1,750 adult patients undergoing non-cardiac surgery. We developed and compared three ML algorithms Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) trained on physiological features including arterial pressure trends and rolling volatility indices. Performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC) for discrimination and the Brier Score for calibration. Results: All models demonstrated robust predictive capability. The Random Forest model achieved the highest discrimination (AUROC 0.837), outperforming LR (0.824) and XGBoost (0.803). However, XGBoost demonstrated superior calibration with a Brier Score of 0.0825 (vs. 0.153 for RF), indicating more reliable probabilistic risk estimates. Feature importance analysis consistently identified hemodynamic volatility (rolling standard deviation of MAP) as the dominant predictor across all models. At the optimal threshold, the system demonstrated a sensitivity of 69.5% and specificity of 75.3%. Conclusions: We identified a trade-off between discrimination and calibration: Random Forest offers the best ranking for early warning, while XGBoost provides the most accurate risk probability. Crucially, hemodynamic instability was identified as a critical prodromal marker, suggesting that oscillatory variance precedes hypotension.

Abstract: Closed-form analytic inverses allow explicit tracking of parameter effects, facilitate interpretation of experimental signals, and support solving inverse problems. Here, we derive a rigorous closed-form expression for the inverse Laplace transform of a class of shifted quasi-rational spectral functions with a square-root radical and a power-law decaying factor. These functions appear in coupled diffusion processes in physics and in the analysis of electromagnetic signal propagation through electrically cascaded networks, signal processing, and related areas. The transform is expressed as a finite sum of three generalized hypergeometric functions–two Kummer functions and one five-parameter Kampé de Fériet function–each multiplied by a monomial depending on the decay parameter. The validity of the result is confirmed by direct Laplace transformation, which recovers the original spectral function. Several known inverse transforms appear as limiting cases, illustrating the generality of the solution. Additionally, reduction formulas for a subclass of Kampé de Fériet functions demonstrate how the general solution encompasses previously known results and highlight the generality of the method.

Abstract: The development of prospective inner and outer space economies focuses on the use of bunch of small space vehicles operated as a quasi-single artificial organism. Such economies include the need for using swarms of small satellites providing communication and surveillance services, being a distributed materials production plant in space, or performing research expedition to study the resources and environments of the new worlds. The use of multiple space vehicles performing tasks as a quasi-single system makes the execution of such missions resilient by reducing the failure risks that is higher for the single-vehicled mission, especially performed in deep space. The core technology for operating distributed space systems is propulsion. From a variety of propulsion technologies ranging from the use of the pressurized cold gas to the implementation of laser beams destroying the surface of solid propellants to generate thrust, some stands out for small spacecraft applications. In this work, the summary on the space-operated propulsion is provided by highlighting the impetus of more frequent use of one technology over other. The discussion on the trends in propulsion is supported by the discussion on the physical, engineering, production, operational, and societal rationales overview. This review serves as the mean for reevaluating of the global propulsion trends and guiding the future inner and outer space propulsion assisting economies effective development.

Abstract: As an effective biological control agent,Propylea japonica (Coleoptera: Coccinellidae) preys on aphids, whiteflies, planthoppers, and small caterpillars, playing a crucial role in pest management within agro-ecological systems. However, the lack of population genomic data has hindered efforts to optimize its use in biological control. We anayzed resequencing data from 166 genomes across 29 populations spanning P. japonica’s distribution in China. This study reconstructed the species’ evolutionary history, assessed population genetic diversity and demographic structure, identified the key environmental factors driving adaptive evolution. Meanwhile, we predicted its suitable habitats across different periods using ecological niche modelling methods. The results indicated that North China (G1, Yellow River Basin) was the likely geographic origin of P. japonica. Northern and southern populations show significant genetic differentiation, with adaptive evolution in the south being the major driver. We identified genomic signatures of selection in adaptive genes associated with increased pesticide resistance and thermal tolerance. Over the past 20,000 years, effective population size of P. japonica experienced an early bottleneck during the Last Glacial Maximum period, and a subsequent rapid expansion. These insights are critical for improving the conservation and application of natural enemies, ultimately enhancing biological control in agricultural systems.

Abstract: Anxiety disorders are the most prevalent mental health conditions worldwide, yet current treatments remain suboptimal, with benzodiazepines carrying risks of tolerance and dependence. These limitations motivate the search for novel anxiolytics. Tropical marine macroalgae represents a promising source of neuroactive metabolites. Here, we investigate the anxiolytic potential of Stypopodium zonale using a neuroproteomics-based approach in Drosophila melanogaster. Crude organic extracts were prepared via ultrasonic-assisted extraction and administered acutely to adult flies for six hours. Proteins from fly heads were quantified and analyzed using LC-MS/MS, revealing 66 significantly differentially abundant proteins (fold change ≥ |1.5|, p ≤ 0.05), 72.7% of which were less abundant in the extract-treated group. Principal Component Analysis demonstrated clear separation between control and experimental samples. Ingenuity Pathway Analysis (IPA) mapped 33 of the differentially abundant proteins to human orthologs and identified significant predicted inhibition of the Protein Kinase A (PKA) signaling pathway. An IPA Interaction Network enabled the construction of a preliminary working model, suggesting that the extract may antagonize Drosophila’s Dop1R2 (DAMB). Overall, this study integrates natural product drug discovery with neuroproteomics in an invertebrate model system, providing a foundation for future behavioral validation and isolation of bioactive compounds from S. zonale.

Abstract: This paper examines the connection between Environmental, Social, and Governance (ESG) factors and the risk of geopolitics, as defined by the Geopolitical Risk (GPR) index. The concept of geopolitical risk is conventionally defined as the direct result of political incidents, war, and international tensions. The current study argues that the concept should be understood in a more structural and sustainable manner, relating to the underlying forces driving geopolitical risk. The main research question is whether and how the three pillars of the ESG factors contribute to the explanation and understanding of cross-country and over-time variations in geopolitical risk. In an effort to avoid the information losses associated with the aggregate nature of the ESG index, the three factors are considered separately and the three pillars are analyzed individually. The empirical context is a balanced cross-country panel data set including 42 countries over the 2000-2023 time period. The data for the three factors is obtained from the World Bank dataset in an effort to standardize and compare the data in a cross-country and cross-time manner. The GPR index is used to measure the level of geopolitical risk and is defined by Dario Caldara and Matteo Iacoviello. The GPR index captures the level of geopolitical tensions based on the analysis of media signals. The combination of the three sources allows for the direct connection and correlation between the three factors and the internationally recognized GPR index. The paper uses an integrated methodological approach that combines the results from three different approaches. The first method uses panel data analysis in an effort to identify the average marginal effects while controlling for unobserved heterogeneity. The second method uses the technique of clustering in an effort to identify structural patterns and divide the countries into groups based on their unique characteristics and risk profiles. The third method uses machine learning regressions and nonparametric analysis in an effort to capture the complex relationships and interactions in the data. The three-step method is used for each pillar in an effort to ensure consistency and comparability. The results suggest that the three factors contribute to the GPR index in a unique manner. The environment and energy structure contribute to the GPR index as a risk multiplier, the social factor is related to the exposure to instability, and the governance factor is a central stabilizing factor. The paper makes a unique contribution to the literature by defining the concept of the three factors and their relationship to the GPR index in a unique and sustainable manner.

Abstract: Background/Objectives: The tumor suppressor gene TP53 is one of the most frequently mutated genes in human cancers, with alterations predominantly affecting its DNA-binding domain (DBD). However, the mutational landscape and functional consequences of TP53 variants remain poorly characterized in African populations. This study aimed to characterize mutations in exons 5-6 of TP53 in oral cavity cancer (OCC), prostate cancer (PC), and breast cancer (BC) in a Senegalese population, and to assess their structural effects, functional consequences, and impact on protein–protein interactions with BCL-2. Methods: Seventy-eight archived tumor DNA samples from Senegalese patients with OCC, PC, and BC were analyzed. Variants were annotated using COSMIC and dbSNP databases. Functional impact was evaluated with PolyPhen-2. Structural stability changes (∆∆G) were predicted using FoldX, conformational dynamics (∆∆S_vib) were assessed with ENCoM, and effects on the p53–BCL-2 interaction were analyzed using DDMut-PPI. Statistical analyses were also performed. Results: BC exhibited the highest TP53 mutation frequency, whereas OCC showed greater mutational diversity. Exon-level analysis revealed a significant enrichment of exon 6 mutations in BC. Structural analyses indicated that exon 5 mutations across all cancers and mutations in OCC were predominantly destabilizing and associated with loss-of-function effects. In contrast, recurrent exon 6 mutations in PC and BC, particularly V217L and V218M, were predicted to stabilize the p53 structure. Conformational dynamics differences between exons were significant only in PC. All analyzed mutations were predicted to stabilize the p53–BCL-2 interaction. Conclusions: This integrative in silico study identifies cancer and exon-specific TP53 mutation patterns in a Senegalese population, highlighting exon 6 as a context-dependent hotspot with potential oncogenic implication in PC and BC. Despite its computational nature, the study provides valuable insights that merit further investigation.

Abstract: This work introduces a rigorous mathematical approach for producing entangled quantum states from classical stochastic dynamics. We show that any density matrix ρAB describing a composite quantum system can be reconstructed from the correlations of two foundational stochastic processes, X(t) and Y(t), which model the random behavior of the individual subsystems. The framework employs a dual temporal scale—micro and macro time—where quantum correlations naturally arise as emergent macro-level correlations derived from fine-grained micro-level interactions. We formulate the Double Covariance Model (DCM), which captures the essential features of quantum mechanics by interpreting the quantum state as a fourth-order statistical structure within an underlying classical probabilistic model.

Abstract: Background/Objectives Nutritional risk is increasingly recognized as a relevant but under-assessed dimension of rheumatoid arthritis (RA), particularly in older adults managed in outpatient settings. Simple nutritional indices such as the Prognostic Nutritional Index (PNI) may help identify individuals at increased nutritional risk beyond conventional disease activity measures. This study aimed to characterize nutritional risk in older adults with RA using the Prognostic Nutritional Index, explore sex-specific patterns, and identify clinical associations of PNI variability, with complementary analyses focusing on high nutritional risk. Methods We conducted an observational cross-sectional study including 275 consecutive adults aged ≥50 years with RA attending routine follow-up at a tertiary rheumatology clinic. Nutritional risk was assessed using the PNI, calculated from serum albumin and total lymphocyte count, and analyzed primarily as a continuous variable and secondarily using established cut-off values. Clinical characteristics, inflammatory markers, body mass index, laboratory parameters, and patient-reported outcomes were recorded. Analyses were stratified by sex. Multivariable linear regression models were used to identify factors associated with PNI variability, and complementary logistic regression analyses were performed to explore factors independently associated with high nutritional risk (PNI < 40). Results More than half of the cohort (53.3%) exhibited PNI values compatible with nutritional risk. Men showed significantly lower PNI values than women, with a higher frequency of nutritional risk (61.5% vs. 49.7%, p < 0.01) and a markedly greater prevalence of high nutritional risk (18.0% vs. 5.0%, p < 0.001). Sex-specific association patterns were observed. In women, PNI showed only a weak inverse association with age. In contrast, in men, lower PNI values were associated with higher inflammatory burden, greater disease activity, lower body mass index, poorer mental health–related quality of life, and lower hemoglobin levels. In multivariable linear regression models restricted to men, hemoglobin emerged as the principal independent correlate of PNI. In complementary logistic regression analyses focusing on high nutritional risk (PNI < 40), hemoglobin remained the sole independent predictor (OR = 0.94, 95% CI 0.91–0.98; p < 0.01), supporting a robust association with clinically relevant nutritional risk. Conclusions Nutritional risk assessed by the PNI is common among older adults with RA and displays pronounced sex-specific patterns, with men showing a substantially higher burden of nutritional risk. The PNI captures a clinically relevant dimension of disease burden that extends beyond joint inflammation and traditional activity indices, supporting its use as a pragmatic nutritional screening tool in routine rheumatology practice.

Abstract: An in-line process analytical technology that measures drag force exerted by wet mass in a high-shear granulator on a thin cylindrical probe enabled real-time identification of dis-tinct stages in high shear wet granulation of acetaminophen. The technology known as Lenterra in-line rheometer outputs two parameters, the mean force pulse magnitude (MFPM), and the coefficient of variation of force pulse magnitude (CVFPM) that characterize granule densification and size uniformity in real time, providing a process fingerprint. The MFPM and CVFPM evolutions measured during granulation of acetaminophen formulations for varied amounts of added water were compared with the results of PSD analysis of the powder released after granulation and with the tablet dissolution tests. The comparison demonstrated a correlation between salient features of the MFPM and CVFPM evolutions and particle size distributions for different water amounts. Based on the measured process fingerprints, it was possible to identify water amount optimal for best granulation output. In addition, MFPM and CVFPM evolutions allowed for prediction of a granulation end point. The results indicate that in-line rheometry can be a useful tool for formulation development and scale-up of high shear wet granulation processes.

Abstract: Alzheimer’s disease (AD) is an aging-associated neurodegenerative disorder in which dysregulated neuroinflammation drives disease progression. Although long noncoding RNAs (lncRNAs) are increasingly implicated in AD, their mechanistic roles remain poorly defined. Here, we identified a novel lncRNA termed LIMASI (LncRNA Inflammation and Mucous associated, Antisense to ICAM1), that is linked with AD-associated neuroinflammation. LIMASI expression is significantly elevated in postmortem AD brain tissues and in the 3xTg-AD mouse model by qPCR and RNA fluorescence in situ hybridization, and its upregulation correlated with increased β-amyloid plaque burden, tau hyperphosphorylation, and heightened neuroinflammatory activation. Cell-type–specific analyses demonstrated inflammation-inducible LIMASI expression in astrocytes and microglia. In an in vitro model of AD-associated neuroinflammation, viral-mimetic poly(I:C) challenge of amyloid precursor protein (APP)–overexpressing neuroblastoma cells elicited coordinated induction of LIMASI and key inflammatory mediators. Mechanistically, computational RNA–RNA interaction modeling predicted multiple energetically favorable binding sites for AD-associated inflammatory microRNAs (miR-1915-3p, miR-122-5p, miR-155-5p, and miR-150-5p), supporting a competing endogenous RNA (ceRNA) model in which LIMASI sequesters miRNAs to modulate neuroinflammatory gene networks. Together, these data identify LIMASI as a putative ceRNA strongly linked to AD-related neuroinflammation and suggest that LIMASI represents a promising therapeutic target for modulating neuroinflammatory signaling and slowing AD-associated neurodegeneration.

Abstract: Building on the ancient and spiritual call to care for self and others, recent research (2020–2025) demonstrates that self‑compassion improves resilience, self-care, emotional regulation, and recovery from workplace stress across diverse sectors including healthcare, education, and public safety. Defined through as self‑kindness, common humanity, and mindfulness, self‑compassion fosters meaning and supports inner hope by helping individuals hold their suffering self-lovingly with understanding rather than self‑criticism. Emerging findings reveal that compassion‑based interventions improve psychological wellbeing, reduce burnout, and enhance physiological markers of recovery such as heart rate variability (HRV). This paper synthesises current evidence and highlights implications for chaplains, carers, and leaders in developing compassionate work environments that support sustainable wellbeing and people’s search for meaning. Some practical suggestions for chaplains and organisational leaders particularly in the areas of using self-kindness and naming to gently acknowledge and releasing stress and not ruminating when mistakes or stress arise. This may require workplace education programs. While more research is needed, recent research affirms that self-compassion leads to notable improvements in self-reflection, psychological empowerment and reduces burnout and workplace stress. Future research directions are also offered.

Abstract: Huntington’s disease (HD) is characterized by progressive striatal atrophy and complex proteomic changes in the central nervous system. Using the ultrasensitive NULISA proteomic platform, we analyzed cerebrospinal fluid (CSF) from 88 persons with HD to dissect the biological correlates of gray matter loss. We identified a two-signal pattern of pathology. The first track, marked by the axonal damage protein Neurofilament Light (NEFL), showed a strong negative correlation with putamen volume (Pearson r = -0.53, p < 0.001), consistent with prior work supporting NEFL as a proxy for neurodegeneration and brain atrophy in HD. The second track was defined by a positive association between the immune regulator TNFRSF8 (CD30) and putamen volume (Pearson r = 0.36, p < 0.001), indicating a loss of putative immune-regulatory signal as atrophy progresses. TNFRSF8 was pre-specified for follow-up given its immune-regulatory role and interpretability in the context of immune dysregulation consistent with an immune-exhaustion-like profile. TNFRSF8 showed an independent association with striatal volume (Beta = 0.24, p = 0.008) after controlling for NEFL, CAG-Age-Product (CAP) score, and sex. These findings suggest that reduced immune-regulatory signaling is a distinct pathological correlate in HD, separable from general cytoskeletal damage, and warrants evaluation in longitudinal and interventional studies.

Abstract: Background/Objectives: Dilated (DCM) and hypertrophic cardiomyopathy (HCM) are common cardiomyopathies associated with heart failure. Electrocardiogram (ECG) screening before an echocardiogram could help streamline diagnosis, particularly in rural areas. Prior ECG machine-learning (ML) studies do not use open-source data when studying cardiomyopathy, and very few proprietary studies directly compare HCM and DCM or address ECG differences within obstructive (HOCM) and non-obstructive HCM (HNCM). Methods: Standard and vectorcardiogram-derived (VCG) ECG features were extracted from the MIMIC-IV-ECG database. The final cohort comprised 599 patients (HCM = 208 [HOCM = 99, HNCM = 53, unknown = 56], DCM = 391 [ischemic cardio-myopathy with left ventricular dilation = 250, non-ischemic = 141]). Logistic regression (LR) and extreme gradient boosting (XGBoost) with five-fold cross-validation separated HCM from ischemic cardiomyopathy with left ventricular dilation (DCM-I) and non-ischemic DCM (DCM-NI), and HOCM from HNCM. Results: Using the area under the receiver operating characteristic curve (AUC-ROC) as the performance metric, LR achieved high discrimination of HCM from DCM-I (0.92) and DCM-NI (0.90). However, differentiating HOCM from HNCM proved more difficult (XGBoost = 0.81; LR = 0.75). Both DCM subtypes (especially ischemic) showed lower QRS amplitudes and right-posterior ventricular gradient orientation; HCM displayed higher amplitudes and larger, more complex T-loops. Within HCM, HOCM had stronger leftward electrical activity and more dipolar to non-dipolar QRS energy after singular value decomposition. Conclusions: Using only open-access data, we demonstrate an interpretable ECG-based pipeline that discriminates cardiomyopathy and highlights distinct features. While detecting ob-struction remains difficult, ECG features provide measurable separation, supporting possible diagnostic screening and offering a reproducible framework for future studies.