The Intersection of the Pathogenic Processes Underlying Psoriasis and the Comorbid Condition of Obesity.

In the past decade, our understanding of psoriasis pathogenesis has made significant steps forward, leading to the development of multiple game-changing therapies. While psoriasis primarily affects the skin, it is increasingly recognized as a systemic disease that can have effects beyond the skin. Obesity is associated with more severe forms of psoriasis and can potentially worsen the systemic inflammation and metabolic dysfunction seen in psoriatic patients. The exact mechanisms underlying the link between these two conditions are not fully understood, but it is believed that chronic inflammation and immune dysregulation play a role. In this review, we examine the existing body of knowledge regarding the intersection of pathogenic processes responsible for psoriasis and obesity. The ability of biological therapies to reduce systemic and obesity-related inflammation in patients with psoriasis will be also discussed.

Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.

BACKGROUND: Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability. OBJECTIVE: This study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support. METHODS: We introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR's performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions. RESULTS: On a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions. CONCLUSIONS: Our experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

RAS-activated PI3K/AKT signaling sustains cellular senescence via P53/P21 axis in experimental models of psoriasis.

BACKGROUND: Psoriasis is a chronic immune-mediated skin disease in which upper epidermal keratinocytes exhibit a senescent-like phenotype. In psoriatic skin, a variety of inflammatory cytokines can activate intracellular pathways including phosphatidylinositol 3-kinase (PI3K)/AKT signaling and RAS effectors. AKT and RAS participate to cellular senescence, but currently their role in senescence responses occurring in psoriasis have not yet been investigated. OBJECTIVE: The role of AKT molecular axis and RAS activation was evaluated in the context of cellular senescence in psoriasis disease. METHODS: RAS/AKT involvement in senescence was analyzed in psoriatic keratinocytes cultures subjected to multiple passages to promote senescence in vitro, as well as in skin lesions of patients affected by psoriasis. The impact of pharmacological inhibition of PI3K/AKT pathway on senescence and inflammation responses was tested in senescent psoriatic keratinocytes and in a psoriasiform dermatitis murine model induced by RAS overexpression in the upper epidermis of mice. RESULTS: We found AKT hyperactivation associated to the upregulation of senescence markers, in senescent psoriatic keratinocyte cultures, as well as in skin lesions of psoriatic patients. AKT-induced senescence was sustained by constitutive RAS activation, and down-stream responses were mediated by P53/P21 axis. PI3K/AKT inhibition contrasted senescence processes induced by cytokines in psoriatic keratinocytes. Additionally, RAS-induced psoriasis-like dermatitis in mice was accompanied by AKT upregulation, increase of senescence marker expression and by skin inflammation. In this model, both senescence and inflammation were significantly reduced by selective AKT inhibition. CONCLUSION: Therefore, targeting RAS-AKT pathway could be a promising novel strategy to counteract multiple psoriasis symptoms.