The integration of the multitude of ultrasound techniques into a "one-stop" liver clinic model will revolutionize the management of liver diseases. This approach streamlines patient care by providing immediate imaging assessment, facilitating prompt diagnosis, and expediting treatment plans. The traditional ultrasound methods of B-mode imaging and Doppler techniques have been supplemented by the newer techniques of tissue elastography, fat quantification, and contrast-enhanced ultrasound-termed multiparametric ultrasound. The deployment of these techniques to establish in more detail the underlying status of liver disease has been profound. The encompassing ultrasound techniques have allowed the ultrasound practitioner to establish a comprehensive assessment of liver disease, allowing further accurate management, and negating the need for additional, often more expensive, imaging to establish the diagnosis. This paper explores the implementation, benefits, and challenges of ultrasound-based one-stop liver clinics, emphasizing their impact on patient outcomes and healthcare efficiency. A detailed assessment of the techniques and their position in the diagnostic armamentarium is reviewed with a comprehensive overview established. CRITICAL RELEVANCE STATEMENT: Multiparametric liver ultrasound integrating B-mode, Doppler, CEUS, elastography and fat quantification provides a practical, low-cost one-stop pathway for staging chronic liver disease, assessing portal hypertension surrogates and characterizing incidental lesions, thereby speeding up treatment. KEY POINTS: Ultrasound is the first-line imaging investigation for liver disease, with established criteria on B-mode imaging for steatosis and cirrhosis. Multiparametric ultrasound integrates morphology, hemodynamics, fibrosis, steatosis, and lesion assessment. A one-stop liver ultrasound clinic accelerates decisions and reduces additional imaging.
Background: Gastric cancer (GC) is one of the most common malignancies, requires aggressive treatment, as has a high incidence of complications. The high prevalence of cachexia and comorbidity among GC patients has led to the development of the "prehabilitation" concept. We aimed to investigate the prognostic value of cachexia in the "Western" patient population with resectable GC and to evaluate its utility as an indicator for a home-based prehabilitation program. Methods: This cohort study included 147 patients who underwent surgical treatment for GC from 2019 to 2023. A multivariable analysis was conducted to study the impact of cachexia on postoperative outcomes in 122 patients with resectable GC. The prehabilitation group included 25 patients with cachexia who underwent a 2-week-long multimodal prehabilitation program prior to surgery. The functional results, as well as the 30-day incidence of postoperative complications and 90-day mortality, were evaluated. Results: There were 76 (51.7%) patients with cachexia. Multivariate analysis revealed that cachexia was a significant predictor of all postoperative complications (OR = 5.48, 95% CI 1.85-18.39, p = 0.001), severe postoperative complications (OR = 15.87, 95% CI 3.05-131.81, p < 0.001) and surgical site infection (SSI) (OR = 8.03, 95% CI 1.89-49.09, p = 0.038). Patients in the prehabilitation group had a lower incidence of SSI than in the control group (8.3% vs. 23.5%, p = 0.049). Conclusions: Preoperative cachexia is a potentially modifiable predictor of complications after gastric cancer surgery, and its identification may help define high-risk patients for proactive multimodal prehabilitation.
Artificial intelligence (AI) is increasingly used to support organizational and managerial processes in healthcare, although evidence of real-world effects remains heterogeneous. Aim. To identify and critically evaluate the evidence on the impact of AI technologies on organizational processes in healthcare and to assess their potential applicability BRICS healthcare systems. Methods. A systematic review with narrative followed PRISMA 2020. PubMed and eLibrary.ru (incorporating the Russian Science Citation Index) and CyberLeninka were searched between January and February 2026. Primary empirical and implementation studies, reviews, and selected methodological, governance-related, and contextual papers published between 2017 and 2026 were eligible. After multistage screening, 78 publications were included. R esults . The strongest evidence concerned predictive analytics for patient flow management and resource allocation, which reduced patient waiting times by 18–26% and achieving high predictive accuracy. Machine learning algorithms applied to operating room scheduling reduced idle time and improved resource utilization. Ambient and generative AI documentation tools were associated with reduced administrative workload and reduced clinician burnout. Evidence for revenue cycle management, conversational AI, logistics, and supply chain optimization was mainly based on model-development studies, reviews, or limited implementation reports. Several large-scale BRICS-related implementation studies came from Russia, while evidence from other BRICS healthcare systems was less consistently represented. Major barriers included fragmented infrastructure, limited interoperability, workforce gaps, and governance challenges. Conclusion. AI may improve selected organizational processes, but long-term economic effectiveness, scalability, sustainability, and reproducibility require further evaluation in diverse healthcare settings.
Arterial hypertension (AH) remains a leading risk factor for cardiovascular complications and cognitive impairment. Effective control of blood pressure (BP) significantly reduces the risk of stroke and coronary artery disease (CAD). However, in real-world clinical practice, achieving target BP levels is often complicated by comorbidities, poor treatment adherence, and neglect of the circadian BP profile, particularly morning BP surges, which are an independent predictor of cardiovascular events. Among antihypertensive drugs, angiotensin II receptor blockers (ARBs) hold a special place due to their proven cerebroprotective effects. Candesartan, one of the most extensively studied representatives of this class, not only effectively lowers BP but also slows the progression of cognitive impairment and significantly reduces the risk of stroke. The presented clinical case demonstrates the potential for treatment optimization in a patient with long-term uncontrolled AH, CAD, obesity, and established cognitive impairment. Given the presence of morning BP surges and cognitive dysfunction, the patient was prescribed candesartan 16 mg/day in combination with bisoprolol, indapamide, and atorvastatin. For secondary prevention of thrombotic complications, acetylsalicylic acid (ASA) combined with magnesium hydroxide was chosen, which preserves the high bioavailability of the immediate-release ASA formulation while simultaneously providing gastric mucosal protection. After 9 months of therapy, target BP levels were achieved, morning BP surges were eliminated, and improvements in cognitive function and regression of left ventricular hypertrophy were observed. Thus, candesartan, due to its cerebroprotective properties and ultra-long duration of action, is the drug of choice in comorbid patients with AH and cognitive impairment. The combination of ASA with magnesium hydroxide provides an optimal balance of antiplatelet efficacy and gastroprotection.
