Disease States Research Articles

Multi-modal incomplete label information three-way bidirectional decision-making: Applications of disease assessment

Disease assessment involves two stages of decision-making process. The first process is to infer the development trend of the disease through the existing judgment conditions, which is to make a positive estimation (prediction) decision. The second process is to update the judgment conditions for different disease states and optimize the clinical assessment evidence by fusing disease progression data, that is, to make a reverse evaluation (optimization) decision by reviewing historical diagnostic and treatment data to summarize the best judgment conditions for the disease characteristics. Therefore, we construct a three-way bidirectional decision method for multi-modal incomplete labeling information, considering the mixed data features with no labels and labels, and the bidirectional decision features of positive prediction and reverse optimization. Specifically, the method first introduces semi-supervised learning (SSL) into the rough sets, constructs a rough sets model based on cosine similarity relation, generates pseudo-labels for incomplete information, and explains the positive prediction decision process principle. Second, the gradient space is constructed on the predictive information system by the three-way decision-making principle, the loss function formed by the combination of KL divergence and cross entropy loss function is used to find the direction of the fastest decline in attribute information value, and the optimal decision rule and the automatic updating strategy of the threshold value are obtained. At the same time, the principle of the reverse optimization decision process is explained. Finally, multimodal information from pathological tissue images is used to evaluate clinical progress problem. A pathological tissue images dataset includes 2000 colorectal cancer and 300 kidney disease cases used for simulation experiments. The proposed three-way bidirectional decision-making model can be widely applied to actual decision-making scenarios for evaluating clinical progress of different diseases. Compared with previous studies, this method can realize objective calculation and automatic update of threshold and loss values and effectively solve complex prediction and optimization decision-making problems.

The Cotard Delusion in a Patient With Neuropsychiatric Systemic Lupus Erythematosus: The Challenges of Autoimmune Psychosis.

The clinical features of neuropsychiatric systemic lupus erythematosus (NPSLE) are heterogeneous. Furthermore, therapeutic decision-making for NPSLE depends on the recognition of clinical syndromes that have not been sufficiently studied. This report describes the case of a 36-year-old woman with NPSLE who exhibited severe cognitive dysfunction and affective psychosis with persistent nihilistic delusions such as those described in the Cotard delusion. The patient insisted for several months that she was already dead. CSF analysis showed elevated levels of anti-ribosomal P antibodies and a positive determination of oligoclonal bands. Additionally, 18F-FDG PET/CT imaging revealed severe bilateral frontal hypermetabolism suggestive of brain inflammation and occipital hypometabolism. Results from the Systematic Lupus Erythematosus Disease Activity Index 2000 and the Systemic Lupus Erythematosus Disease Activity Score were consistent with an active state of the immunological disease. We then determined by an algorithm that this neuropsychiatric event could be attributed to the activity of the underlying immunological disease. Despite immunosuppressive and symptomatic treatment, only a partial improvement in cognition was achieved. The psychopathological features of the Cotard delusion remained unchanged 4 months after onset. However, we observed rapid remission of affective psychosis and significant improvement in cognition following electroconvulsive therapy. Subsequent follow-up examinations showed a sustained remission. This case describes a protracted form of the Cotard delusion, the diagnostic challenges that arise in the context of SLE, and treatment dilemmas that necessitate collaboration between neurology, psychiatry, and rheumatology.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Zonation, ligand and dose dependence of S1PR1 signalling in blood and lymphatic vasculature.

Circulating levels of sphingosine 1-phosphate (S1P), an HDL-associated ligand for endothelial cell (EC) protective S1P receptor-1 (S1PR1), are reduced in disease states associated with endothelial dysfunction. Yet as S1PR1 has high affinity for S1P and can be activated by ligand-independent mechanisms and EC-autonomous S1P production, it is unclear if relative reductions in circulating S1P impact endothelial function. It is also unclear how EC S1PR1 insufficiency, whether induced by ligand deficiency or by S1PR1-directed immunosuppressive therapy, affects different vascular subsets. We here fine-map the zonation of S1PR1 signalling in the murine blood and lymphatic vasculature, superimpose cell type-specific and relative deficiencies in S1P production to define ligand source- and dose-dependence, and correlate receptor engagement to essential functions. In naïve blood vessels, despite broad expression, EC S1PR1 engagement was restricted to resistance-size arteries, lung capillaries and high-endothelial venules (HEV). Similar zonation was observed for albumin extravasation in EC S1PR1 deficient mice, and brain extravasation was reproduced with arterial EC-selective S1pr1 deletion. In lymphatic EC, S1PR1 engagement was high in collecting vessels and lymph nodes and low in terminal capillaries that drain tissue fluids. While EC S1P production sustained S1PR1 signaling in lymphatics and HEV, hematopoietic cells provided ∼90% of plasma S1P and sustained signaling in resistance arteries and lung capillaries. S1PR1 signaling and endothelial function were both surprisingly sensitive to reductions in plasma S1P with apparent saturation around 50% of normal levels. S1PR1 engagement did not depend on sex or age, but modestly increased in arteries in hypertension and diabetes. Sphingosine kinase (Sphk)-2 deficiency also increased S1PR1 engagement selectively in arteries, which could be attributed to Sphk1-dependent S1P release from perivascular macrophages. This study highlights vessel subtype-specific S1PR1 functions and mechanisms of engagement and supports the relevance of S1P as circulating biomarker for endothelial function.

Assessment of B-type natriuretic peptide as an early predictor of mortality in acutely poisoned patients with cardiotoxicities.

Cardiotoxicity is a major toxic effect induced by several types of drugs. An electrocardiogram is done routinely in cardiovascular drug exposures. Cardiac troponin I (cTnI) is the usual biomarker for diagnosing myocardial injury. B-type natriuretic peptide (BNP) is a well-established predictor of disease state in suspected heart failure. The study aimed to assess BNP's role as an early predictor of mortality compared with cTnI and ECG changes in acutely poisoned patients with cardiotoxicities. This study enrolled 70 patients with acute cardiotoxicity by drugs and toxins known to cause cardiac injury admitted to Tanta University Poison Control Center (TUPCC). Collected data included socio-demographic data, toxicological history, vital signs, ECG changes, Poison Severity Score (PSS), BNP, and cTnI serum levels. Patients were classified as survivors and non-survivors. Significantly more delay time was recorded in non-survivors. Moreover, vital signs were significantly abnormal in non-survivors. There was no statistical significance regarding the initial ECG abnormalities between survivors and non-survivors. BNP and cTnI levels were significantly higher among non-survivors. For mortality prediction, BNP had good predictive power (AUC=0.841) with 100% sensitivity and 79.7% specificity while cTnI had an acceptable predictive power (AUC=0.786), with 83.3% sensitivity and 78.1% specificity with insignificant difference between both biomarkers. BNP and cTnI levels can predict mortality in acute cardiotoxicity compared to ECG which has no statistically significant prediction. BNP has a higher discriminatory power than cTnI for the prediction of mortality.

The International Consensus for Early TB framework (ICE-TB): Implications from a low-incidence setting.

<sec><title>BACKGROUND</title>In recent years, there has been increasing recognition of the public health significance of the spectrum of TB disease presentation, and the existing classification systems of asymptomatic infection and symptomatic TB have been limited in terms of explanatory power. Accordingly, in 2022-2023, a new International Consensus framework for Early TB (ICE-TB) was developed, categorising the spectrum of TB infection and disease into five states based on the presence or absence of macroscopic pathology, host infectiousness, and symptoms and signs.</sec><sec><title>METHODS</title>We used the ICE-TB framework to re-analyse existing notification data for 2022 within a low-incidence setting to explore the potential utility and future challenges for its public health application.</sec><sec><title>RESULTS</title>Existing notification data were sufficient to allow substantial reclassification of currently recognised active disease states, but did not systematically capture Mycobacterium tuberculosis infection or subclinical TB. Fifty percent of existing TB notifications would be classified as 'Clinical, infectious', with the potential need to consider further subclassification.</sec><sec><title>CONCLUSION</title>Our exploration highlighted limitations in existing classification systems and diagnostic approaches and should encourage researchers and programmatic implementers to emphasise person-centred and programmatic needs in the development of new tools for TB management.</sec>.

Efficient fabrication of 3D bioprinted functional sensory neurons using an inducible Neurogenin-2 human pluripotent stem cell line.

Three-dimensional (3D) tissue models have gained recognition for their improved ability to mimic the native cell microenvironment compared to traditional two-dimensional (2D) models. This progress has been driven by advances in tissue-engineering technologies such as 3D bioprinting, a promising method for fabricating biomimetic living tissues. While bioprinting has succeeded in generating various tissues to date, creating neural tissue models remains challenging. In this context, we present an accelerated approach to fabricate 3D sensory neuron (SN) structures using a transgenic human pluripotent stem cell (hPSC)-line that contains an inducible neurogenin-2 (NGN2) expression cassette. The NGN2 hPSC line was first differentiated to neural crest cell (NCC) progenitors, then incorporated into a cytocompatible GelMA-based bioink for 3D bioprinting. Upregulated NGN2 expression in the bioprinted NCCs resulted in induced SN (iSN) populations that exhibited specific cell markers, with 3D analysis revealing widespread neurite outgrowth through the scaffold volume. Calcium imaging demonstrated functional activity of iSNs, including membrane excitability properties and voltage-gated sodium channel (NaV) activity. This efficient approach to generate 3D bioprinted iSN structures streamlines the development of neural tissue models, useful for the study of neurodevelopment and disease states and offering translational potential.

Aberrant blood-brain barrier dynamics in cerebral small vessel disease - a review of associations, pathomechanisms and therapeutic potentials

This review explores the pivotal role of the blood-brain barrier (BBB) in maintaining central nervous system (CNS) homeostasis and its dynamic involvement in the pathogenesis of cerebral small vessel disease (CSVD), i.e., the major precursor of age-related neurodegenerative diseases such as vascular dementia and Alzheimer’s disease. It underscores the BBB as a critical physiological boundary that regulates the exchange between the bloodstream and the cerebral milieu through a complex and dynamic interface composed of endothelial cells, astrocyte endfeet, and pericytes. The integrity of this barrier is paramount for neural function, shielding the brain from toxicants and pathogens while facilitating the transport of essential nutrients. Nevertheless, BBB dysfunction is recognized as a lead in the pathogenesis of neurodegeneration including CSVD, emphasizing the need for focused research on maintaining or restoring BBB function. This review highlights recent advancements in our understanding of BBB dynamics in both health and disease states, its involvement in CSVD pathomechanisms, and the challenges and future directions of translational research and emerging technologies. The review advocates for a multidisciplinary approach to uncover the complexity of BBB dysfunction in CSVD, as well as insights into potential therapeutic targets aimed at preserving BBB integrity, thereby minimizing its impact given the notable world’s aging demographics.

Polymer scaffolds delineate healthy from diseased states at sites distal from the pancreas in two models of type 1 diabetes.

Type 1 diabetes (T1D) prevention is currently limited by the lack of diagnostic tools able to identify disease before autoimmune destruction of the pancreatic β cells. Autoantibody tests are used to predict risk and, in combination with glucose dysregulation indicative of β cell loss, to determine administration of immunotherapies. Our objective was to remotely identify immune changes associated with the disease, and we have employed a subcutaneously implanted microporous poly(e-caprolactone) (PCL) scaffold to function as an immunological niche (IN) in two models of T1D. Biopsy and analysis of the IN enables disease monitoring using transcriptomic changes at a distal site from autoimmune destruction of the pancreas, thereby gaining cellular level information about disease without the need for a biopsy of the native organ. Using this approach, we identified gene signatures that stratify healthy and diseased mice in both an adoptive transfer model and a spontaneous onset model of T1D. The gene signatures identified herein demonstrate the ability of the IN to identify immune activation associated with diabetes across models.

B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics.

Throughout an organism's life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimer's Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimer's Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

Mucosal and Transmural Healing and Long-term Outcomes in Crohn's Disease.

Healing in Crohn's disease is complex and difficult to measure due to incongruencies between clinical symptoms and disease states. Mucosal healing (MH) and transmural healing (TH) are increasingly used to measure clinical improvement in Crohn's disease, but definitions of MH and TH can vary across studies, and their relationship to long-term outcomes is not clear. To address this knowledge gap, we performed a systematic literature review (SLR) to examine studies measuring MH and TH in Crohn's disease. Database records from 2012 to 2022 were searched for real-world evidence and interventional studies that reported the association of MH or TH with clinical, economic, or quality of life outcomes of adult patients with Crohn's disease. A total of 46 studies were identified in the systematic literature review, representing a combined patient population of 5530. Outcomes of patients with MH were reported by 39 studies; of these, 14 used validated scales for endoscopic assessment. Thirteen studies reported outcomes of patients with TH. Among studies that examined the outcomes of patients with and without MH or TH, patients with healing generally experienced improved clinical outcomes and reduced healthcare resource utilization, including fewer hospitalizations and surgeries and improved rates of clinical remission. This was especially true for patients with TH. Mucosal and transmural healing are associated with positive long-term outcomes for adult patients with Crohn's disease. The adoption of standardized measures and less invasive assessment tools will maximize the benefits of patient monitoring.

An overview of benefits and risks of chronic melanocortin-1 receptor activation.

The melanocortin-1 receptor (MC1R) is a G protein-coupled receptor that plays a pivotal role in human skin pigmentation, melanin synthesis, redox homeostasis and inflammation. Loss-of-function MC1R variants suppress G protein-coupled receptor coupling or cell surface expression leading to a decrease in adenyl cyclase activation and intracellular levels of cyclic adenosine monophosphate. Chronic activation of MC1R can occur in certain medical conditions such as Addison's disease and physiologic states such as pregnancy melasma. MC1R activation is more commonly caused by environmental exposure to ultraviolet (UV) radiation. Approved pharmacologic melanocortin agonists that activate MC1R signalling in a targeted manner or as a bystander effect have recently become available for erythropoietic protoporphyria, sexual desire disorders, monogenic obesity and syndromic obesity. Further, small peptide analogues of α-melanocortin-stimulating hormone, human MC1R selective agonists, are photoprotective, decreasing the adverse impact of UV radiation (a primary risk factor for skin cancer) and are being investigated as potential chemoprevention strategies. MC1R activation through induction of UV-protective skin pigmentation increased DNA repair, and control of aberrant cell growth may reduce the risk of melanoma but importantly does not prevent melanoma particularly in individuals with risk factors and regular skin examination remains critical in high-risk individuals.

Prevalence and progression of chronic kidney disease among adults undergoing creatinine testing in South African public healthcare facilities: a study leveraging data from South Africa’s National Health Laboratory Service (NHLS)

BackgroundChronic kidney disease (CKD) has emerged as a substantial global health challenge, with a marked rise in associated mortality. However, it often goes undetected until advanced stages, particularly in low-income...

Spheroid trilineage differentiation model of primary mesenchymal stem/stromal cells under hypoxia and serum-free culture conditions

Due to their unique properties, human mesenchymal stem/stromal cells (MSCs) possess tremendous potential in regenerative medicine, particularly in cell-based therapies where the multipotency and immunomodulatory characteristics of MSCs can be leveraged to address a variety of disease states. Although MSC-based cell therapeutics have emerged as one of the most promising medical treatments, the clinical translation is hampered by the variability of MSC-based cellular products caused by tissue source-specific differences and the lack of physiological cell culture approaches that closely mimic the human cellular microenvironment. In this study, a model for trilineage differentiation of primary adipose-, bone marrow-, and umbilical cord-derived MSCs into adipocytes, chondrocytes and osteoblasts was established and characterized. Differentiation was performed in spheroid culture, using hypoxic conditions and serum-free and antibiotics-free medium. This platform was characterized for spheroid diameter and trilineage differentiation capacity reflecting functionality of differentiated cells, as indicated by lineage-specific extracellular matrix (ECM) accumulation and expression of distinct secreted markers. The presented model shows spheroid growth during the course of differentiation and successfully supports trilineage differentiation for MSCs from almost all tissue sources except for osteogenesis of umbilical cord-derived MSCs. These findings indicate that this platform provides a suitable and favorable environment for trilineage differentiation of MSCs from various tissue sources. Therefore, it poses a promising model to generate highly relevant biological data urgently required for clinical translation and therefore might be used in the future to generate in vitro microtissues, building blocks for tissue engineering or as disease models.

Copper Overload Increased Rat Striatal Levels of Both Dopamine and Its Main Metabolite Homovanillic Acid in Extracellular Fluid

Copper is a trace element whose electronic configuration provides it with essential structural and catalytic functions. However, in excess, both its high protein affinity and redox-catalyzing properties can lead to hazardous consequences. In addition to promoting oxidative stress, copper is gaining interest for its effects on neurotransmission through modulation of GABAergic and glutamatergic receptors and interaction with the dopamine reuptake transporter. The aim of the present study was to investigate the effects of copper overexposure on the levels of dopamine, noradrenaline, and serotonin, or their main metabolites in rat’s striatum extracellular fluid. Copper was injected intraperitoneally using our previously developed model, which ensured striatal overconcentration (2 mg CuCl2/kg for 30 days). Subsequently, extracellular fluid was collected by microdialysis on days 0, 15, and 30. Dopamine (DA), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxyindoleacetic acid (5-HIAA), and noradrenaline (NA) levels were then determined by HPLC coupled with electrochemical detection. We observed a significant increase in the basal levels of DA and HVA after 15 days of treatment (310% and 351%), which was maintained after 30 days (358% and 402%), with no significant changes in the concentrations of 5-HIAA, DOPAC, and NA. Copper overload led to a marked increase in synaptic DA concentration, which could contribute to the psychoneurological alterations and the increased oxidative toxicity observed in Wilson’s disease and other copper dysregulation states.

The association between fat distribution and α1-acid glycoprotein levels among adult females in the United States

BackgroundVisceral fat accumulation and obesity-induced chronic inflammation have been proposed as early markers for multiple disease states, especially in women. Nevertheless, the potential impact of fat distribution on α1-acid glycoprotein(AGP), a marker of inflammation, remains unclear. This research was conducted to investigate the relationships among obesity, fat distribution, and AGP levels.MethodsA cross-sectional observational study was performed using blood samples from adult females recruited through the National Health and Nutrition Examination Survey from 2015 to 2018. Serum levels of AGP were measured using the Tina-quant α-1-Acid Glycoprotein Gen.2 assay. Based on the fat distribution data obtained from dual-energy X-ray absorptiometry assessments, body mass index (BMI), total percent fat (TPF), android percent fat (APF), gynoid percent fat (GPF), android fat/gynoid fat ratio (AGR), visceral percent fat (VPF), subcutaneous percent fat (SPF), visceral fat/subcutaneous fat ratio (VSR) were used as dependent variables. To investigate the link between fat distribution and AGP, multivariate linear regression analysis was utilized. Furthermore, a sensitivity analysis was also performed.ResultsThe present study included 2,295 participants. After adjusting for covariates, BMI, TPF, APF, GPF, VPF, and SPF were found to be positively correlated with AGP levels (BMI: β = 23.65 95%CI:20.90–26.40; TPF: β = 25.91 95%CI:23.02–28.80; APF: β = 25.21 95%CI:22.49–27.93; GPF: β = 19.65 95%CI:16.96–22.34; VPF: β = 12.49 95%CI:9.08–15.90; SPF: β = 5.69, 95%CI:2.89–8.49; AGR: β = 21.14 95%CI:18.16–24.12; VSR: β = 9.35 95%CI:6.11–12.59, all P < 0.0001). All the above indicators exhibited a positive dose–response relationship with AGP. In terms of fat distribution, both AGR and VSR showed positive associations with AGP (P for trend < 0.0001). In particular, when compared to individuals in tertile 1 of AGR, participants in tertiles 2 and 3 had 13.42 mg/dL (95% CI 10.66–16.18) and 21.14 mg/dL (95% CI 18.16–24.12) higher AGP levels, respectively. Participants in the highest tertile of VSR were more likely to exhibit a 9.35 mg/dL increase in AGP compared to those in the lowest tertile (95% CI 6.11–12.59).ConclusionsOverall, this study revealed a positive dose-dependent relationship between fat proportion/distribution and AGP levels in women. These findings suggest that physicians can associate abnormal serum AGP and obesity with allow timely interventions.

Metabolic Insights into Neuropsychiatric Illnesses and Ketogenic Therapies: A Transcriptomic View

The disruption of brain energy metabolism, leading to alterations in synaptic signaling, neural circuitry, and neuroplasticity, has been implicated in severe mental illnesses such as schizophrenia, bipolar disorder, and major depressive disorder. The therapeutic potential of ketogenic interventions in these disorders suggests a link between metabolic disturbances and disease pathology; however, the precise mechanisms underlying these metabolic disturbances, and the therapeutic effects of metabolic ketogenic therapy, remain poorly understood. In this study, we conducted an in silico analysis of transcriptomic data to investigate perturbations in metabolic pathways in the brain across severe mental illnesses via gene expression profiling. We also examined dysregulation of the same pathways in rodent or cell culture models of ketosis, comparing these expression profiles to those observed in the disease states. Our analysis revealed significant perturbations across all metabolic pathways, with the greatest perturbations in glycolysis, the tricarboxylic acid (TCA) cycle, and the electron transport chain (ETC) across all three disorders. Additionally, we observed some discordant gene expression patterns between disease states and ketogenic intervention studies, suggesting a potential role for ketone bodies in modulating pathogenic metabolic changes. Our findings highlight the importance of understanding metabolic dysregulation in severe mental illnesses and the potential therapeutic benefits of ketogenic interventions in restoring metabolic homeostasis. This study provides insights into the complex relationship between metabolism and neuropsychiatric disorders and lays the foundation for further experimental investigations aimed at appreciating the implications of the present transcriptomic findings as well as developing targeted therapeutic strategies.

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient’s disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.g., ixazomib, lenalidomide, dexamethasone (IRd) vs lenalidomide, dexamethasone (Rd). Using TOURMALINE trial data, we trained and internally validated our model on newly diagnosed myeloma patients (N = 703) and externally validated it on relapsed and refractory myeloma patients (N = 720). Our model achieved superior performance to a risk model based on the multiple myeloma international staging system (ISS) (p < 0.001, Bonferroni corrected) and comparable performance to survival models trained separately on each task, but unable to forecast biomarkers. Our approach outperformed state-of-the-art deep learning models, tailored towards forecasting, on predicting key disease biomarkers (p < 0.001, Bonferroni corrected). Finally, leveraging our model’s capacity to estimate individual-level treatment effects, we found that patients with IgA kappa myeloma appear to benefit the most from IRd. Our study suggests that a holistic assessment of a patient’s myeloma course is possible, potentially serving as the foundation for a personalized decision support system.

Neuropathologic Features and Underlying Medical Disease States of Spontaneous Subdural Hematomas in Adults: A Hospital Autopsy Case Series From a Single Tertiary Center.

Spontaneous (nontraumatic) subdural hematomas have been reported yet have not been well studied. To identify the neuropathologic features of acute spontaneous SDHs (ASSDHs) and their associated medical conditions. A retrospective study of 235 autopsy cases of SDH was conducted. Review of demographics, underlying medical conditions, and coagulation profile as well as gross and histopathologic examination of the brain and other organs were performed. Among the 32 cases of ASSDH, 5 cases (15.6%) had severe hemorrhage and 4 (12.5%) demonstrated brain herniation. Twenty-two cases (68.8%) had concurrent but nonconnecting subarachnoid hemorrhage or intraparenchymal hemorrhage. The most common underlying medical condition was thrombocytopenia (n = 21; 65.6%), followed by immunosuppression (n = 15; 46.9), bloodstream infections or sepsis (n = 12; 37.5%), hypertension (n = 13; 40.6%), and coronary artery disease (n = 12; 37.5%). Many patients with thrombocytopenia or immunosuppression had underlying malignancies, with leukemia being the most common type (n = 11; 34.4%). The use of circulatory devices or hemodialysis was noted in a significant portion of ASSDH cases. In terms of coagulation factors, most of our ASSDH patients had normal prothrombin time and activated partial thromboplastin time, but abnormal platelet count and D-dimer levels. ASSDHs can be severe and are often associated with subarachnoid hemorrhage and/or intraparenchymal hemorrhage. The causes of ASSDH are limited to certain underlying medical conditions that ultimately lead to bleeding tendency. Autopsies are helpful in determining the etiology. Given their association with abnormal platelet count, correcting platelet deficiencies is a potential preventive measure for ASSDHs.

Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease.

Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.