Bronchiectasis.

Non-cystic fibrosis bronchiectasis, a condition that remains relatively underrecognized, has garnered increasing research focus in recent years. This scientific interest has catalyzed advancements in diagnostic methodologies, enabling comprehensive clinical and molecular profiling. Such progress facilitates the development of personalized treatment strategies, marking a significant step toward precision medicine for these patients. Bronchiectasis poses significant diagnostic challenges in both clinical settings and research studies. While computed tomography (CT) remains the gold standard for diagnosis, novel alternatives are emerging. These include artificial intelligence-powered algorithms, ultra-low dose chest CT, and magnetic resonance imaging (MRI) techniques, all of which are becoming recognized as feasible diagnostic tools. The precision medicine paradigm calls for refined characterization of bronchiectasis patients by analyzing their inflammatory and molecular profiles. Research into the underlying mechanisms of inflammation and the evaluation of biomarkers such as neutrophil elastase, mucins, and antimicrobial peptides have led to the identification of distinct patient endotypes. These endotypes present variable clinical outcomes, necessitating tailored therapeutic interventions. Among these, eosinophilic bronchiectasis is notable for its prevalence and specific prognostic factors, calling for careful consideration of treatable traits. A deeper understanding of the microbiome's influence on the pathogenesis and progression of bronchiectasis has inspired a holistic approach, which considers the multibiome as an interconnected microbial network rather than treating pathogens as solitary entities. Interactome analysis therefore becomes a vital tool for pinpointing alterations during both stable phases and exacerbations. This array of innovative approaches has revolutionized the personalization of treatments, incorporating therapies such as inhaled mannitol or ARINA-1, brensocatib for anti-inflammatory purposes, and inhaled corticosteroids specifically for patients with eosinophilic bronchiectasis.

Las bronquiectasias no fibrosis quística han atraído una creciente atención en investigación. Este interés científico ha catalizado avances en las metodologías de diagnóstico, permitiendo realizar perfiles clínicos y moleculares integrales. Este progreso facilita el desarrollo de estrategias de tratamiento personalizadas y marca un paso significativo hacia la medicina de precisión.Desde el punto de vista diagnóstico, las bronquiectasias plantean desafíos importantes en entornos clínicos y de investigación. Si bien la TC es el gold standard, están surgiendo nuevas alternativas. Entre ellas, algoritmos de inteligencia artificial, TC de tórax de dosis ultrabajas y técnicas de resonancia magnética.La medicina de precisión aboga por la caracterización de pacientes mediante análisis de perfiles inflamatorios y moleculares. Las investigaciones sobre mecanismos subyacentes de inflamación y la evaluación de biomarcadores como la elastasa de neutrófilos, mucinas y péptidos antimicrobianos, han llevado a la identificación de endotipos de pacientes. Estos endotipos exhiben resultados clínicos variables, requiriendo intervenciones terapéuticas personalizadas. La bronquiectasia eosinofílica destaca por su prevalencia y factores pronósticos específicos, exigiendo consideración de los rasgos tratables.Una comprensión profunda de la influencia del microbioma en la patogénesis y progresión de las bronquiectasias inspira un enfoque holístico. Considera el multibioma como una red microbiana interconectada, no entidades solitarias. El análisis del interactoma se convierte en una herramienta vital para identificar alteraciones durante fases estables y exacerbaciones.Este conjunto de enfoques innovadores revoluciona la personalización de los tratamientos, incorporando terapias como manitol inhalado o ARINA-1, brensocatib con fines antiinflamatorios y corticosteroides inhalados específicos para pacientes con bronquiectasias eosinofílicas.

Characteristics of pleural effusion due to amyloidosis.

The characteristics of patients with pleural amyloidosis (PA) are poorly known. A systematic review was performed of studies reporting clinical findings, pleural fluid (PF) characteristics, and the most effective treatment of PA. Case descriptions and retrospective studies were included. The review included 95 studies with a total sample of 196 patients. The mean age was 63 years, male/female ratio was 1.6:1, and 91.9% of patients were >50 years. The most common symptom was dyspnea (88 patients). PF was generally serious (63%), predominantly lymphocytic, and with the biochemical characteristics of transudates (43.4%) or exudates (42.6%). Pleural effusion was generally bilateral (55%) and <1/3 of the hemithorax (50%), although in 21% pleural effusion (PE) exceeded 2/3. Pleural biopsy was performed in 67 patients (yield: 83.6%; 56/67) and was positive in 54% of exudates and 62.5% of unilateral effusions. Of the 251 treatments prescribed, only 31 were effective (12.4%). The combination of chemotherapy and corticosteroids was effective in 29.6% of cases, whereas talc pleurodesis was effective in 21.4% and indwelling pleural catheter in 75% of patients (only four patients). PA is more frequent in adults from 50 years of age. PF is usually bilateral, serous, and indistinctly a transudate or exudate. A pleural biopsy can aid in diagnosis if effusion is unilateral or an exudate. Treatments are rarely effective and there may be definitive therapeutic options for PE in these patients.

Spirituality, Religiosity and Mental Health: A Clinical-care Approach.

No abstract present.

Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients.

The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6-25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.