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Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods.
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Colorectal cancer (CRC) is a significant global health issue where early detection is crucial for improving treatment outcomes and survival rates. This comprehensive review assesses the utility of stool-based tests in CRC screening, including traditional fecal occult blood tests (FOBT), both chemical (gFOBT) and immunochemical techniques (FIT), as well as multitarget stool DNA (mt-sDNA) as a novel and promising biomarker. The advancements, limitations and the impact of false positives and negatives of these methods are examined. The review analyzed various studies on current screening methods, focusing on laboratory tests and biomarkers. Findings indicate that while FIT and mt-sDNA tests offer enhanced sensitivity and specificity over traditional guaiac-based FOBT, they also come with higher costs and potential for increased false positives. FIT shows better patient adherence due to its ease to use, but incorrect usage and interpretation of FOBT can lead to significant diagnostic errors. In conclusion, despite the improvements in FOBT methods like FIT in CRC detection, careful consideration of each method's benefits and drawbacks is essential. Effective CRC screening programs should combine various methods tailored to specific population needs, aiming for early detection and reduced mortality rates.
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Introduction: Sleep-disordered breathing (SDB) and gas exchange disorders are common in patients with cystic fibrosis (CF). Currently, the impact of the disease on sleep patterns in patients living at high altitude and the relationship of these patterns to lung function are largely unknown. The aim of this study was to determine the frequency of SDB in children with CF aged 6-18 years and the relationship between SDB and lung function (FEV1). Methods: This is an analytical cross-sectional study of children aged 6-18 years diagnosed with CF. Spirometry before and after bronchodilators and polysomnography with capnography were performed. Descriptive analysis of qualitative and continuous variables was performed. Spearman's correlation coefficient was used to determine the correlation between polysomnogram and lung function (FEV1). Results: Twenty-four patients with CF were included. The mean age was 10.5 ± 3.1 years and 62.5% were male. Nine children had bronchiectasis on chest CT. The median absolute baseline FEV1 was 1,880 (1,355-2,325) ml and 98% (83%-110%) of predicted value. No significant difference in FEV1% was observed between subjects with obstructive sleep apnea (OSA) and those without OSA (P = 0.56). The prevalence of OSA was 66.7% in children younger than 13 years and 40% in children older than 13 years. The Spearman correlation coefficient between FEV1 and percentage of total sleep time with saturation less than 90% (T90) was rho -0.52 (p-value = 0.018), and between FEV1 and percentage of total sleep time with saturation less than 85% (T85) was statistically significant with rho -0.45 (p-value = 0.041). A positive correlation was observed between FEV1 and SpO2 during sleep with rho 0.53 and a statistically significant p-value (0.014). Conclusions: A high prevalence of sleep apnea was found in children with CF living at high altitude, with a negative correlation between FEV1 and T90 and T85 oxygenation indices, and a positive correlation between FEV1 and SpO2 during sleep.
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The circadian clock system coordinates metabolic, physiological, and behavioral functions across a 24-h cycle, crucial for adapting to environmental changes. Disruptions in circadian rhythms contribute to major metabolic pathologies like obesity and Type 2 diabetes. Understanding the regulatory mechanisms governing circadian control is vital for identifying therapeutic targets. It is well characterized that chromatin remodeling and 3D structure at genome regulatory elements contributes to circadian transcriptional cycles; yet the impact of rhythmic chromatin topology in metabolic disease is largely unexplored. In this study, we explore how the spatial configuration of the genome adapts to diet, rewiring circadian transcription and contributing to dysfunctional metabolism. We describe daily fluctuations in chromatin contacts between distal regulatory elements of metabolic control genes in livers from lean and obese mice and identify specific lipid-responsive regions recruiting the clock molecular machinery. Interestingly, under high-fat feeding, a distinct interactome for the clock-controlled gene Dbp strategically promotes the expression of distal metabolic genes including Fgf21. Alongside, new chromatin loops between regulatory elements from genes involved in lipid metabolism control contribute to their transcriptional activation. These enhancers are responsive to lipids through CEBPß, counteracting the circadian repressor REVERBa. Our findings highlight the intricate coupling of circadian gene expression to a dynamic nuclear environment under high-fat feeding, supporting a temporally regulated program of gene expression and transcriptional adaptation to diet.
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Cromatina , Relojes Circadianos , Ácidos Grasos , Hígado , Ratones Endogámicos C57BL , Ratones Obesos , Obesidad , Animales , Cromatina/metabolismo , Cromatina/genética , Hígado/metabolismo , Ratones , Relojes Circadianos/genética , Obesidad/metabolismo , Obesidad/genética , Ácidos Grasos/metabolismo , Masculino , Dieta Alta en Grasa/efectos adversos , Ensamble y Desensamble de Cromatina , Ritmo Circadiano/genética , Factores de Transcripción/metabolismo , Factores de Transcripción/genética , Metabolismo de los Lípidos/genética , Factores de Crecimiento de Fibroblastos/metabolismo , Factores de Crecimiento de Fibroblastos/genética , Regulación de la Expresión Génica/efectos de los fármacos , Proteínas de Unión al ADN/genética , Proteínas de Unión al ADN/metabolismoRESUMEN
Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.
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INTRODUCTION: Exhaled nitric oxide fraction (FeNO) is employed for the diagnosis and phenotyping of asthma as an inflammatory biomarker of the airway. Limited evidence exists regarding its behavior in the presence of asthma and obstructive sleep apnea (OSA). Our objective was to determine whether FeNO levels are associated with the severity of OSA or the coexistence of asthma and OSA in residents at high altitudes. MATERIALS AND METHODS: Observational, analytical, cross-sectional study in children aged 5-16 years residing at 2600 m above sea level treated at a Sleep Study Center between 2019 and 2021. We conducted a medical history, polysomnogram, and measurement of FeNO levels. The children were categorized into four groups: OSA, asthma, asthma with OSA, and controls (without asthma or OSA). FeNO levels among the groups were compared using the Kruskal-Wallis test, and correlations were explored using the Spearman correlation coefficient. Analyses considered statistical significance at a two-tailed p-value <0.05. RESULTS: Among the 261 included children, 68 (26.1 %) had OSA, 42 (16.1 %) were diagnosed with asthma, 109 (41.8 %) had both asthma and OSA, and 42 (16.1 %) were controls. Their FeNO medians were 10 ppb, 18.5 ppb, 15 ppb, and 14 ppb, respectively, with no significant differences between the evaluated groups (p = 0.263). We found no correlation between FeNO and apnea-hypopnea index and obstructive apnea index even for the groups of patients with FeNO >20 ppb and FeNO >35 ppb (>75th percentile). In the adjusted model, a significant association was observed between asthma and FeNO levels. CONCLUSIONS: Our findings suggest that FeNO measurements in children would not allow establishing this biomarker as part of the diagnosis of OSA. However, these findings may be related to high altitude.
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Altitud , Asma , Óxido Nítrico , Polisomnografía , Apnea Obstructiva del Sueño , Humanos , Asma/diagnóstico , Asma/complicaciones , Estudios Transversales , Apnea Obstructiva del Sueño/diagnóstico , Apnea Obstructiva del Sueño/metabolismo , Niño , Masculino , Femenino , Óxido Nítrico/análisis , Óxido Nítrico/metabolismo , Preescolar , Biomarcadores/metabolismo , Biomarcadores/análisis , Adolescente , Pruebas Respiratorias , EspiraciónRESUMEN
Chirality is inherent to a broad range of systems, including solid-state and wave physics. The precession (chiral motion) of the magnetic moments in magnetic materials, forming spin waves, has various properties and many applications in magnetism and spintronics. We show that an optical analogue of spin waves can be generated in arrays of plasmonic nanohelices. Such optical waves arise from the interaction between twisted helix eigenmodes carrying spin and orbital angular momenta. We demonstrate that these optical spin waves are reflected at the interface between successive domains of enantiomeric nanohelices, forming a heterochiral lattice regardless of the wave propagation direction within the lattice. Optical spin waves may be applied in techniques involving photon spin, ranging from data processing and storage to quantum optics.
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Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir's clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB's effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.
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Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients.
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Hepatocellular carcinoma (HCC) is the most common primary liver tumor and is associated with high mortality rates. Approximately 80% of cases occur in cirrhotic livers, posing a significant challenge for appropriate therapeutic management. Adequate screening programs in high-risk groups are essential for early-stage detection. The extent of extrahepatic tumor spread and hepatic functional reserve are recognized as two of the most influential prognostic factors. In this retrospective multicenter study, we utilized machine learning (ML) methods to analyze predictors of mortality at the time of diagnosis in a total of 208 patients. The eXtreme gradient boosting (XGB) method achieved the highest values in identifying key prognostic factors for HCC at diagnosis. The etiology of HCC was found to be the variable most strongly associated with a poorer prognosis. The widely used Barcelona Clinic Liver Cancer (BCLC) classification in our setting demonstrated superiority over the TNM classification. Although alpha-fetoprotein (AFP) remains the most commonly used biological marker, elevated levels did not correlate with reduced survival. Our findings suggest the need to explore new prognostic biomarkers for individualized management of these patients.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Aprendizaje Automático , alfa-Fetoproteínas , Humanos , alfa-Fetoproteínas/química , Biomarcadores de Tumor , Carcinoma Hepatocelular/metabolismo , Carcinoma Hepatocelular/patología , Neoplasias Hepáticas/metabolismo , Neoplasias Hepáticas/patología , Estadificación de Neoplasias , Estudios RetrospectivosRESUMEN
BACKGROUND: Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. METHODS: In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. RESULTS: Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). CONCLUSIONS: The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.
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Metabolic Associated Fatty Liver Disease (MASLD) is a condition that is often present in patients with a history of cholecystectomy. This is because both situations share interconnected metabolic pathways. This study aimed to establish a predictive model that allows for the identification of patients at risk of developing hepatic fibrosis following this surgery, with potential implications for surgical decision-making. A retrospective cross-sectional analysis was conducted in four hospitals using a database of 211 patients with MASLD who underwent cholecystectomy. MASLD diagnosis was established through liver biopsy or FibroScan, and non-invasive test scores were included for analysis. Various Machine Learning (ML) methods were employed, with the Adaptive Boosting (Adaboost) system selected to build the predictive model. Platelet level emerged as the most crucial variable in the predictive model, followed by dyslipidemia and type-2 diabetes mellitus. FIB-4 score proved to be the most reliable non-invasive test. The Adaboost algorithm improved the results compared to the other methods, excelling in both accuracy and area under the curve (AUC). Moreover, this system holds promise for implementation in hospitals as a valuable diagnostic support tool. In conclusion, platelet level (<150,000/dL), dyslipidemia, and type-2 diabetes mellitus were identified as primary risk factors for liver fibrosis in MASLD patients following cholecystectomy. FIB-4 score is recommended for decision-making, particularly when the indication for surgery is uncertain. This predictive model offers valuable insights into risk stratification and personalized patient management in post-cholecystectomy MASLD cases.
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BACKGROUND: Bone fragility fractures are associated with high morbidity and mortality. This study analysed the association between the current biochemical parameters of CKD-MBD and bone fragility fractures in the COSMOS project. METHODS: COSMOS is a 3-year, multicentre, open cohort, prospective, observational study carried out in 6797 hemodialysis patients (227 centres from 20 European countries). The association of bone fragility fractures (outcome) with serum calcium, phosphate and PTH (exposure), was assessed using Standard Cox proportional hazards regression and Cox proportional hazards regression for recurrent events. Additional analyses were performed considering all-cause mortality as a competitive event for bone fragility fracture occurrence. Multivariable models were used in all strategies, with the fully adjusted model including a total of 24 variables. RESULTS: During a median follow-up of 24 months 252 (4%) patients experienced at least one bone fragility fracture (incident bone fragility fracture rate 28.5 per 1000 patient-years). In the fractured and non-fractured patients, the percentage of men was 43.7% and 61.4%, mean age 68.1 and 63.8 years and a haemodialysis vintage of 55.9 and 38.3 months respectively. Baseline serum phosphate > 6.1 mg/dL (reference value 4.3-6.1 mg/dL) was significantly associated with a higher bone fragility fracture risk in both regression models (HR: 1.53[95%CI: 1.10-2.13] and HR: 1.44[95%CI: 1.02-2.05]. The significant association persisted after competitive risk analysis (subHR: 1.42[95%CI: 1.02-1.98]) but the finding was not confirmed when serum phosphate was considered as a continuous variable. Baseline serum calcium showed no association with bone fragility fracture risk in any regression model. Baseline serum PTH > 800 pg/mL was significantly associated with a higher bone fragility fracture risk in both regression models, but the association disappeared after a competitive risk analysis. CONCLUSIONS: Hyperphosphatemia was independently and consistently associated with an increased bone fracture risk, suggesting serum phosphate could be a novel risk factor for bone fractures in hemodialysis patients.
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Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.
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Vascular calcification (VC) is a common complication in patients with chronic kidney disease which increases their mortality. Although oxidative stress is involved in the onset and progression of this disorder, the specific role of some of the main redox regulators, such as catalase, the main scavenger of H2O2, remains unclear. In the present study, epigastric arteries of kidney transplant recipients, a rat model of VC, and an in vitro model of VC exhibiting catalase (Cts) overexpression were analysed. Pericalcified areas of human epigastric arteries had increased levels of catalase and cytoplasmic, rather than nuclear runt-related transcription factor 2 (RUNX2). In the rat model, advanced aortic VC concurred with lower levels of the H2O2-scavenger glutathione peroxidase 3 compared to controls. In an early model of calcification using vascular smooth muscle cells (VSMCs), Cts VSMCs showed the expected increase in total levels of RUNX2. However, Cts VMSCs also exhibited a lower percentage of the nucleus stained for RUNX2 in response to calcifying media. In this early model of VC, we did not observe a dysregulation of the mitochondrial redox state; instead, an increase in the general redox state was observed in the cytoplasm. These results highlight the complex role of antioxidant enzymes as catalase by regulation of RUNX2 subcellular location delaying the onset of VC.
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Insuficiencia Renal Crónica , Calcificación Vascular , Humanos , Animales , Ratas , Catalasa , Subunidad alfa 1 del Factor de Unión al Sitio Principal/genética , Peróxido de Hidrógeno , Oxidación-ReducciónRESUMEN
BACKGROUND: In vivo dosimetry (IVD) is gaining interest for treatment delivery verification in HDR-brachytherapy. Time resolved methods, including source tracking, have the ability both to detect treatment errors in real time and to minimize experimental uncertainties. Multiprobe IVD architectures holds promise for simultaneous dose determinations at the targeted tumor and surrounding healthy tissues while enhancing measurement accuracy. However, most of the multiprobe dosimeters developed so far either suffer from compactness issues or rely on complex data post-treatment. PURPOSE: We introduce a novel concept of a compact multiprobe scintillator detector and demonstrate its applicability in HDR-brachytherapy. Our fabricated seven-fiber probing system is sufficiently narrow to be inserted in a brachytherapy needle or in a catheter. METHODS: Our multiprobe detection system results from the parallel implementation of six miniaturized inorganic Gd2 O2 S:Tb scintillator detectors at the end of a bundle of seven fibers, one fiber is kept bare to assess the stem effect. The resulting system, which is narrower than 320 microns, is tested with a MicroSelectron 9.14 Ci Ir-192 HDR afterloader, in a water phantom. The detection signals from all six probes are simultaneously read with a sCMOS camera (at a rate of 0.06 s). The camera is coupled to a chromatic filter to cancel Cerenkov signal induced within the fibers upon exposure. By implementing an aperiodic array of six scintillating cells along the bundle axis, we first determine the range of inter-probe spacings leading to optimal source tracking accuracy (first tracking method). Then, three different source tracking algorithms involving all the scintillating probes are tested and compared. In each of these four methods, dwell positions are assessed from dose measurements and compared to the treatment plan. Dwell time is also determined and compared to the treatment plan. RESULTS: The optimum inter-probe spacing for an accurate source tracking ranges from 15 to 35 mm. The optimum detection algorithm consists of adding the readout signals from all detector probes. In that case, the error to the planned dwell positions is of 0.01 ± 0.14 mm and 0.02 ± 0.29 mm at spacings between the source and detector axes of 5.5 and 40 mm, respectively. Using this approach, the average deviations to the expected dwell time are of - 0.006 ± 0.009 $-0.006\,\pm \,0.009$ s and - 0.008 ± 0.058 $-0.008\, \pm 0.058$ s, at spacings between source and probe axes of 5.5 and 20 mm, respectively. CONCLUSIONS: Our six-probe Gd2 O2 S:Tb dosimeter coupled to a sCMOS camera can perform time-resolved treatment verification in HDR brachytherapy. This detection system of high spatial and temporal resolutions (0.25 mm and 0.06 s, respectively) provides a precise information on the treatment delivery via a dwell time and position verification of unmatched accuracy.
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Braquiterapia , Dosímetros de Radiación , Dosificación Radioterapéutica , Braquiterapia/métodos , Diseño de Equipo , AlgoritmosRESUMEN
The circular economy has a huge potential to make our societies more sustainable and decarbonised, with a reduced impact on the planet's resources. The deployment of innovative solutions in the field of urban biowaste valorisation and reuse is still hindered by numerous bottlenecks, such as technological readiness, funding and financing tools availability, quality and quantity of biowaste and regulatory barriers. The European Green Deal and associated legislative initiatives provide the opportunity to overcome the last ones. To promote innovative solutions for the European circular bioeconomy and help to overcome the barriers for the deployment of a circular bioeconomy, five Horizon 2020 projects working on biowaste valorisation have teamed up. This joint initiative is named ROOTS - circulaR pOlicies for changing the biOwasTe System. The projects HOOP, VALUEWASTE, SCALIBUR, WaysTUP! and CITYLOOPS are piloting new solutions to transform urban biowaste (food waste and green waste) and wastewater into valuable products like feed, fertilisers, bioplastics, biopesticides, proteins and bioethanol. They use different processes and technologies, but they all rely on high levels of recycling/upcycling and propose valorisation solutions relevant to the uptake of a truly circular bioeconomy. As a result of the work performed and experience acquired, a number of bottlenecks have been identified, on the following topics: biowaste prevention, recycling targets and treatment plants, waste and by-products, biopesticides, insects for animal feed, single cell protein, citizen behaviour, investment needs. For each identified bottleneck, this open letter proposes specifically 1) policy recommendations for each level of governance, and 2) information about solutions, good practices and concrete experiences from the participating projects.
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AIM: Nonalcoholic fatty liver disease (NAFLD) is a silent epidemy that has become the most common chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an advanced stage of NAFLD, which is linked to a high risk of cirrhosis and hepatocellular carcinoma. The aim of this study is to develop a predictive model to identify the main risk factors associated with the progression of hepatic fibrosis in patients with NASH. METHODS: A database from a multicenter retrospective cross-sectional study was analyzed. A total of 215 patients with NASH biopsy-proven diagnosed were collected. NAFLD Activity Score and Kleiner scoring system were used to diagnose and staging these patients. Noninvasive tests (NITs) scores were added to identify which one were more reliable for follow-up and to avoid biopsy. For analysis, different Machine Learning methods were implemented, being the eXtreme Gradient Booster (XGB) system the proposed algorithm to develop the predictive model. RESULTS: The most important variable in this predictive model was High-density lipoprotein (HDL) cholesterol, followed by systemic arterial hypertension and triglycerides (TG). NAFLD Fibrosis Score (NFS) was the most reliable NIT. As for the proposed method, XGB obtained higher results than the second method, K-Nearest Neighbors, in terms of accuracy (95.05 vs. 90.42) and Area Under the Curve (0.95 vs. 0.91). CONCLUSIONS: HDL cholesterol, systemic arterial hypertension, and TG were the most important risk factors for liver fibrosis progression in NASH patients. NFS is recommended for monitoring and decision making.
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Neoplasias Hepáticas , Enfermedad del Hígado Graso no Alcohólico , Humanos , Enfermedad del Hígado Graso no Alcohólico/diagnóstico , Enfermedad del Hígado Graso no Alcohólico/epidemiología , Enfermedad del Hígado Graso no Alcohólico/complicaciones , Estudios Retrospectivos , Estudios Transversales , Cirrosis Hepática/etiología , Factores de Riesgo , HDL-Colesterol , Triglicéridos , Neoplasias Hepáticas/patología , Biopsia/efectos adversos , Hígado/patología , FibrosisRESUMEN
We introduce and demonstrate the concept of a multipixel detector integrated at the tip of an individual multicore fiber. A pixel consists here of an aluminum-coated polymer microtip incorporating a scintillating powder. Upon irradiation, the luminescence released by the scintillators is efficiently transferred into the fiber cores owing to the specifically elongated metal-coated tips that ensure efficient luminescence matching to the fiber modes. With each pixel being selectively coupled to one of the cores of the multicore optical fiber, the resulting fiber-integrated x ray detection process is totally free from inter-pixel cross talk. Our approach holds promise for fiber-integrated probes and cameras for remote x and gamma ray analysis and imaging in hard-to-reach environments.
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In recent times, we have been witnessing the development of multiple applications and deployment of services through the indoors location of people as it allows the development of services of interest in areas related mainly to security, guiding people, or offering services depending on their localization. On the other hand, at present, the deployment of Wi-Fi networks is so advanced that a network can be found almost anywhere. In addition, security systems are more demanded and are implemented in many buildings. Thus, in order to provide a non intrusive presence detection system, in this manuscript, the development of a methodology is proposed which is able to detect human presence through the channel state information (CSI) of wireless communication networks based on the 802.11n standard. One of the main contributions of this standard is multiple-input multiple-output (MIMO) with orthogonal frequency division multiplexing (OFDM). This makes it possible to obtain channel state information for each subcarrier. In order to implement this methodology, an analysis and feature extraction in time-domain of CSI is carried out, and it is validated using different classification models trained through a series of samples that were captured in two different environments. The experiments show that the methodology presented in this manuscript obtains an average accuracy above 90%.