Introduction L’AVCI est une cause majeure de handicap et de mortalité. L’hyperglycémie de stress fréquente en phase aiguë, pourrait affecter le pronostic. Le SHR combinant glycémie et HbA1c pourrait mieux prédire les complications, la mortalité hospitalière et le pronostic fonctionnel à un mois. Observation Étude de cohorte observationnelle rétrospective (26 janvier 2022–27 décembre 2024) menée à l’Hôpital Universitaire International Cheikh Khalifa (HUICK), Casablanca, incluant les soins intensifs, la neuroréanimation et la neurologie conventionnelle.Les critères d’inclusions sont : patients ≥ 18 ans, AVCI confirmé par TDM/IRM, glycémie à jeun et HbA1c disponibles, admission < 14jours, suivi à 1 mois, consentement obtenu. Les critères d’exclusions sont : AVC hémorragique, AIT, admission > 14jours, données biologiques manquantes ou non réalisées dans les 48h, absence de consentement. Le SHR a été calculé et corrélé aux scores NIHSS, GCS, mRS, aux complications et à la mortalité hospitalière.Dans notre étdude, 150 patients inclus, âge moyen 68,6±10,7 ans, 66,7 % d’hommes. Facteurs de risque principaux : HTA (65,3 %), diabète (30,7 %), tabagisme (23,3 %). NIHSS médian 5 [2–8], GCS moyen 14,6±1,0.Complications fréquentes : pneumopathies 15,3 %, infections urinaires 10,7 %, transformations hémorragiques 11,3 %. Un SHR élevé était associé à une hospitalisation plus longue, une incidence accrue de complications infectieuses et neurologiques (p<0,05), et un pronostic fonctionnel moins favorable à 1 mois (mRS ≥ 3 : 32 % vs. 18 %, p<0,01), indépendamment du statut diabétique. Discussion Un SHR élevé à l’admission d’un AVC ischémique aigu est associé à un pronostic défavorable, indépendamment du diabète, avec plus de complications et un moindre recouvrement fonctionnel. Ces résultats concordent avec la littérature : SHR élevé lié à un mauvais pronostic, à un moindre résultat fonctionnel à 90jours et à une mortalité accrue à court et long terme. Conclusion Limitée par son caractère monocentrique et rétrospectif, notre étude nécessite confirmation par des études prospectives multicentriques pour valider des seuils cliniquement utiles du SHR.
Background Breast cancer is the most common malignancy among women worldwide, with marked disparities in survival outcomes across regions (1). In many low- and middle-income countries, immunohistochemistry remains the cornerstone for tumor biological characterization. This study aimed to evaluate overall survival according to routinely assessed immunohistochemical biomarkers and surrogate molecular subtypes in a large real-world cohort from Morocco. Methods This retrospective monocentric cohort study included 623 patients with invasive breast cancer diagnosed in 2014 at a tertiary referral center in Casablanca. Data on age, estrogen receptor (ER), progesterone receptor (PR), HER2 status, Ki-67 proliferation index, molecular subtype, vital status, and date of death were collected. Overall survival was estimated using the Kaplan–Meier method and compared using the log-rank test. Results At five years, vital status was available for 560 patients. Overall survival at five years was 77.5%. Hormone receptor–positive tumors were associated with significantly improved overall survival compared with hormone receptor–negative tumors (p < 0.0001). High Ki-67 (≥ 14%) was associated with poorer survival (p = 0.0038). Overall survival differed significantly according to triple-negative status, with triple-negative tumors exhibiting the worst outcomes. Conclusions Routinely assessed immunohistochemical biomarkers retain strong prognostic value for overall survival in breast cancer. These real-world data provide important population-specific benchmarks and support the continued use of immunohistochemistry for prognostic stratification in resource-constrained settings.
Introduction 18F-FDG PET/CT plays an increasingly important role in the staging of rectal carcinoma. It provides a comprehensive metabolic assessment that complements conventional imaging techniques such as pelvic MRI and thoraco-abdominal CT. Case report We report the case of a 55-year old male patient diagnosed with biopsy-proven rectal carcinoma. 18F-FDG PET/CT revealed intense hypermetabolism at the primary rectal site and identified several metastatic sites not detected by conventional imaging, including supradiaphragmatic lymph nodes (bilateral mediastinal and left axillary), subdiaphragmatic lymph nodes, a left adrenal lesion, and multiple muscular metastases. These findings led to a change in the therapeutic approach, favoring systemic treatment over localized intervention. Discussion This case illustrates the added value of PET/CT in detecting distant metastases, particularly in unusual locations. Literature supports its use in refining cancer staging and guiding clinical decisions, especially in high-risk patients. Studies show that PET/CT can alter patient management in a significant proportion of cases by revealing occult metastases. Conclusion 18F-FDG PET/CT is a valuable imaging modality in the initial staging of rectal cancer. It allows for more accurate identification of metastatic disease, including uncommon sites, and plays a key role in tailoring individualized treatment strategies.
The increase in the use of medical imaging procedures involving ionizing radiation has resulted in major radiation protection concerns. Patient safety and dose optimization are now at the heart of public health issues. This retrospective study was conducted over 5 months (February-June 2025). Dosimetric data from 180 adult patients were collected (kV, mAs, Dose–Area Product (DAP)). Six examinations from conventional radiology examinations were considered: Thorax posterior anterior (PA), abdomen anterior posterior (AP), pelvis (AP), lumbar spine lateral (lat), lumbar spine AP, and cervical spine lat. To estimate the effective dose and lifetime cancer risk, conversion coefficients assigned by NRPB-SR262 and ICRP were used. The median of the DAP distribution was used to establish the local typical dose for each examination. Exposure parameters varied significantly between anatomical regions. The AP and lateral lumbar spine had the highest mean DAP values (1.93 Gy-cm2, 2.21 Gy-cm2, respectively), whereas the PA thorax had the lowest mean DAP value (0.088 Gy-cm2). All median DAP values were below the international DRLs except for the lumbar spine AP and the cervical spine lat. The estimated effective dose ranged from 0.014 mSv (thoracic AP) to 0.425 mSv (lumbar AP), with corresponding cancer risk estimates ranging from 0.71 to 21.27 cases per million examinations. This is the first study in this region of Morocco to evaluate patient radiation exposure in conventional radiography using dose area product (DAP) as the dosimetric indicator. Overall, most results are encouraging and indicate optimized practices. The local typical doses recommended in this work will enable continued dose monitoring and audit processes.
Genome-scale metabolic models provide a stoichiometric description of biochemical networks, yet most analytical frameworks remain data- or flux-centric, implicitly assuming full observability or well-defined flux states. Under realistic experimental conditions, metabolomics measurements capture only a sparse and condition-dependent subset of metabolites, raising a more fundamental question: which aspects of metabolic mechanism remain identifiable under partial observation, and how should measurements be designed to preserve them? Here, we introduce an operator-centric, metabolite-focused geometric framework for metabolism that treats each biological condition as a mechanistic operator rather than a collection of measured variables or inferred fluxes. Starting from stoichiometry, we construct a Dirac-operator–based formulation whose induced metabolite Laplacian encodes reaction-mediated coupling. Condition-specific gene-level proteomics enter exclusively as modulators of reaction coupling through gene–protein–reaction rules, yielding a family of condition-dependent mechanistic operators that define a spectral geometry on the space of metabolites. Within this framework, partial metabolomic observability is formalized as an operator restriction problem, and identifiability is defined as the stability of low-frequency operator geometry under metabolite masking. This definition is agnostic to steady-state assumptions and avoids imputing unobserved quantities. Building on this criterion, we derive a geometry-aware active measurement strategy that selects metabolite panels which optimally preserve mechanistic structure across conditions. Applying the framework to the human genome-scale metabolic model Human1, we show that proteomics-informed operator geometry is substantially more stable under partial observation than topology-based or unweighted baselines, and that compact, condition-aware metabolite panels can be identified without relying on flux optimization or heuristic centrality measures. In a proof-of-concept real-cohort evaluation, operator-aligned stoichiometric geometry alone captures strong disease-discriminative structure under a sparse 36-metabolite panel, demonstrating that the framework’s core geometric machinery translates to downstream biological relevance independent of proteomics availability. Together, this work reframes metabolic analysis around mechanistic operator geometry, providing a principled approach to identifiability and experimental design under sparse, noisy, and condition-specific molecular measurements.
