Весовые соотношения тимуса и надпочечников у антенатально погибших плодов

ТИМУСНАДПОЧЕЧНИКИтимомегалиядети первого года жизнидети раннего возрастаМЛАДЕНЕЦ, СМЕРТНОСТЬЭНДОКРИННОЙ СИСТЕМЫ БОЛЕЗНИСМЕРТНОСТЬ ДЕТСКАЯсиндром внезапной детской смерт

Interaction of Pattern Recognition Receptors with Mycobacterium Tuberculosis.

Tuberculosis (TB) is considered a major worldwide health problem with 10 million new cases diagnosed each year. Our understanding of TB immunology has become greater and more refined since the identification of Mycobacterium tuberculosis (MTB) as an etiologic agent and the recognition of new signaling pathways modulating infection. Understanding the mechanisms through which the cells of the immune system recognize MTB can be an important step in designing novel therapeutic approaches, as well as improving the limited success of current vaccination strategies. A great challenge in chronic disease is to understand the complexities, mechanisms, and consequences of host interactions with pathogens. Innate immune responses along with the involvement of distinct inflammatory mediators and cells play an important role in the host defense against the MTB. Several classes of pattern recognition receptors (PRRs) are involved in the recognition of MTB including Toll-Like Receptors (TLRs), C-type lectin receptors (CLRs) and Nod-like receptors (NLRs) linked to inflammasome activation. Among the TLR family, TLR1, TLR2, TLR4, and TLR9 and their down-stream signaling proteins play critical roles in the initiation of the immune response in the pathogenesis of TB. The inflammasome pathway is associated with the coordinated release of cytokines such as IL-1β and IL-18 which also play a role in the pathogenesis of TB. Understanding the cross-talk between these signaling pathways will impact on the design of novel therapeutic strategies and in the development of vaccines and immunotherapy regimes. Abnormalities in PRR signaling pathways regulated by TB will affect disease pathogenesis and need to be elucidated. In this review we provide an update on PRR signaling during M. tuberculosis infection and indicate how greater knowledge of these pathways may lead to new therapeutic opportunities

Attenuation of Toll-Like Receptor Expression and Function in Latent Tuberculosis by Coexistent Filarial Infection with Restoration Following Antifilarial Chemotherapy

Mycobacterium tuberculosis (Mtb) and filarial coinfection is highly prevalent, and the presence of filarial infections may regulate the Toll-like receptor (TLR)-dependent immune response needed to control Mtb infection. By analyzing the baseline and mycobacterial antigen–stimulated expression of TLR1, 2, 4, and 9 (in individuals with latent tuberculosis [TB] with or without filarial infection), we were able to demonstrate that filarial infection, coincident with Mtb, significantly diminishes both baseline and Mtb antigen-specific TLR2 and TLR9 expression. In addition, pro-inflammatory cytokine responses to TLR2 and 9 ligands are significantly diminished in filaria/TB-coinfected individuals. Definitive treatment of lymphatic filariasis significantly restores the pro-inflammatory cytokine responses in individuals with latent TB. Coincident filarial infection exerted a profound inhibitory effect on protective mycobacteria-specific TLR-mediated immune responses in latent tuberculosis and suggests a novel mechanism by which concomitant filarial infections predispose to the development of active tuberculosis in humans

Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan–Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.</p

Correction to:Prediction of recurrence risk in endometrial cancer with multimodal deep learning (Nature Medicine, (2024), 10.1038/s41591-024-02993-w)

Correction to: Nature Medicinehttps://doi.org/10.1038/s41591-024-02993-w, published online 24 May 2024 In the version of the article initially published, Viktor H. Koelzer and Tjalling Bosse were not named as equal contributors. This has now been corrected in the HTML and PDF versions of the article.</p

Hereditary C1q Deficiency is Associated with Type 1 Interferon-Pathway Activation and a High Risk of Central Nervous System Inflammation

\ua9 The Author(s) 2024.Hereditary C1q deficiency (C1QDef) is a rare monogenic disorder leading to defective complement pathway activation and systemic lupus erythematosus (SLE)-like manifestations. The link between impairment of the complement cascade and autoimmunity remains incompletely understood. Here, we assessed type 1 interferon pathway activation in patients with C1QDef. Twelve patients with genetically confirmed C1QDef were recruited through an international collaboration. Clinical, biological and radiological data were collected retrospectively. The expression of a standardized panel of interferon stimulated genes (ISGs) in peripheral blood was measured, and the level of interferon alpha (IFNα) protein in cerebrospinal fluid (CSF) determined using SIMOA technology. Central nervous system (encompassing basal ganglia calcification, encephalitis, vasculitis, chronic pachymeningitis), mucocutaneous and renal involvement were present, respectively, in 10, 11 and 2 of 12 patients, and severe infections recorded in 2/12 patients. Elevated ISG expression was observed in all patients tested (n = 10/10), and serum and CSF IFNα elevated in 2/2 patients. Three patients were treated with Janus-kinase inhibitors (JAKi), with variable outcome; one displaying an apparently favourable response in respect of cutaneous and neurological features, and two others experiencing persistent disease despite JAKi therapy. To our knowledge, we report the largest original series of genetically confirmed C1QDef yet described. Additionally, we present a review of all previously described genetically confirmed cases of C1QDef. Overall, individuals with C1QDef demonstrate many characteristics of recognized monogenic interferonopathies: particularly, cutaneous involvement (malar rash, acral vasculitic/papular rash, chilblains), SLE-like disease, basal ganglia calcification, increased expression of ISGs in peripheral blood, and elevated levels of CSF IFNα

Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan-Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.</p

Prediction of Recurrence Risk in Endometrial Cancer with Multimodal Deep Learning

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan-Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC

Membrane-Bound TNF Induces Protective Immune Responses to M. bovis BCG Infection: Regulation of memTNF and TNF Receptors Comparing Two memTNF Molecules

Several activities of the transmembrane form of TNF (memTNF) in immune responses to intracellular bacterial infection have been shown to be different from those exerted by soluble TNF. Evidence is based largely on studies in transgenic mice expressing memTNF, but precise cellular mechanisms are not well defined and the importance of TNF receptor regulation is unknown. In addition, memTNF activities are defined for a particular modification of the extracellular domain of TNF but a direct comparison of different mutant memTNF molecules has not been done in vivo

Regulation of Mycobacterium tuberculosis-Dependent HIV-1 Transcription Reveals a New Role for NFAT5 in the Toll-Like Receptor Pathway

Tuberculosis (TB) disease in HIV co-infected patients contributes to increased mortality by activating innate and adaptive immune signaling cascades that stimulate HIV-1 replication, leading to an increase in viral load. Here, we demonstrate that silencing of the expression of the transcription factor nuclear factor of activated T cells 5 (NFAT5) by RNA interference (RNAi) inhibits Mycobacterium tuberculosis (MTb)-stimulated HIV-1 replication in co-infected macrophages. We show that NFAT5 gene and protein expression are strongly induced by MTb, which is a Toll-like receptor (TLR) ligand, and that an intact NFAT5 binding site in the viral promoter of R5-tropic HIV-1 subtype B and subtype C molecular clones is required for efficent induction of HIV-1 replication by MTb. Furthermore, silencing by RNAi of key components of the TLR pathway in human monocytes, including the downstream signaling molecules MyD88, IRAK1, and TRAF6, significantly inhibits MTb-induced NFAT5 gene expression. Thus, the innate immune response to MTb infection induces NFAT5 gene and protein expression, and NFAT5 plays a crucial role in MTb regulation of HIV-1 replication via a direct interaction with the viral promoter. These findings also demonstrate a general role for NFAT5 in TLR- and MTb-mediated control of gene expression