Exhaled Nitric Oxide fraction in asthma and obstructive sleep apnea among children at high altitudes. A cross-sectional study.

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.

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach.

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.

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management.

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.

Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence.

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.

Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma.

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.

Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach.

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.

Serum phosphate is associated with increased risk of bone fragility fractures in hemodialysis patients.

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.

Six-probe scintillator dosimeter for treatment verification in HDR-brachytherapy.

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.

Redox Metabolism and Vascular Calcification in Chronic Kidney Disease.

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.

A Machine Learning-Based Method for Detecting Liver Fibrosis.

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.

ROOTS - Circular policies for changing the biowaste system.

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.

A Machine Learning Method to Identify the Risk Factors for Liver Fibrosis Progression in Nonalcoholic Steatohepatitis.

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.

Multipixel x ray detection integrated at the end of a narrow multicore fiber.

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.

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks.

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%.

Optical Link as an Alternative for MRI Receive Coils: Toward a Passive Approach.

OBJECTIVE: The objective of this work was to propose an alternative solution to NMR signal transmission by replacing the coaxial cables of the receiver radiofrequency (RF) coil in the context of MRI so as to improve safety. Starting from the analysis of previous studies and reports on the topic, the difficulty of supplying power wirelessly to an RF coil was identified. To avoid this difficult task, the development of a passive analog optical link was studied. METHODS: In order to quantify the requirements for achieving an analog conversion, the performance of the link was evaluated based on the input NMR signal amplitude and the optical power and compared with that of a galvanic link. Acquisitions were performed on a 7-T preclinical MRI system with a doped saline solution as phantom. A passive and MRI-compatible polarization-state custom-made modulator was tested as well as a commercial Mach-Zehnder interferometer. RESULTS: The conversion was not sensitive enough to keep similar SNRs, but the main source of noise was identified along with parameters for improvement. Optical power emitted by the laser, insertion loss, and full-phase inversion voltage of the modulators were found to be crucial characteristics for the application. These data indicate that custom application devices are required since the frequency, bandwidth, and amplitude of NMR signals are quite different to usual telecommunication signals. CONCLUSION: An electro-optic modulation and a transmission channel were successfully conceived and tested. Images were reconstructed with some significant SNR drawbacks that are expected to be compensated with an appropriate modulator. SIGNIFICANCE: While technical challenges remain, our approach to a two-decades-long problem could solve a major issue of MRI safety by removing the need for supplying on-coil electrical current.

El distrés oxidativo y sus implicaciones moleculares en algunas enfermedades infecciosas: una revisión / Oxidative distress and its molecular implication for some infectious diseases: A review

Introducción: Las especies reactivas de oxígeno, nitrógeno y azufre (ERONS) se generan continuamente en la fisiología de los organismos. Como parte de la respuesta de las células inmunitarias frente a los patógenos podrían aumentar y producir distrés oxidativo, citotoxicidad y daño de los órganos. El reconocimiento de las implicaciones moleculares de las ERONS todavía es un campo de investigación en desarrollo. Objetivo: Describir los aspectos moleculares relacionados con el metabolismo oxidativo y algunos patógenos (virus, parásitos, bacterias y hongos) en relación con las infecciones. Métodos: Se identificaron 520 documentos relacionados con los criterios de búsqueda en las bases de datos LILACS, Science Direct, SciELO, EMBASE, PubMed e Infomed, con los buscadores Google y Google académico. De estos, fueron analizados 78 documentos publicados a partir de 1980 al 2021 en español o inglés y organizados en 7 subtemas. Información, análisis y síntesis: Los agentes infecciosos y el hospedero interactúan produciendo ERONS que pueden superar los sistemas de defensa antioxidantes e influyen en el distrés oxidativo. Los procesos biológicos asociados al estado redox se relacionan con los factores de transcripción Nrf2 y NF-κB. Ambos permiten una respuesta celular entre la susceptibilidad y la resistencia a los agentes infecciosos, por lo que pueden iniciar o acelerar procesos fisiopatológicos en el organismo. En general la respuesta redox en la fisiopatología infecciosa está interconectada con la reprogramación metabólica, las respuestas antimicrobianas e inflamatorias y la disfunción celular o de tejido. Conclusiones: Los eventos moleculares redox pueden participar en diversas enfermedades infecciosas, mediando diferentes respuestas o trastornos asociados.

Introduction: Reactive oxygen/nitrogen/sulfur species (RONSS) are continuously generated in the physiology of organisms. As part of the immune cell response to pathogens, they may increase and lead to oxidative stress, cytotoxicity and organ damage. Recognizing the molecular implications of RONSS is still a developing field of research. Objective: To describe the molecular aspects related to oxidative metabolism and some pathogens (viruses, parasites, bacteria and fungi) in relation to infections. Methods: Based on the search criteria, 520 documents were identified in LILACS, Science Direct, SciELO, EMBASE, PubMed and Infomed databases, using the search engines Google and Google Scholar. Of these, 78 documents published from 1980 to 2021 in Spanish or English and organized into seven subtopics were analyzed. Information, analysis and synthesis: Infectious agents and the host interact to produce RONSS that can overcome antioxidant defense systems influencing on oxidative stress. Biological processes associated with the redox state are related to the transcription factors Nrf2 and NF-κB. Both generate a cellular response between susceptibility and resistance to infectious agents, thus they can initiate or accelerate pathophysiological processes in the organism. In general, the redox response in infectious pathophysiology is interconnected with metabolic reprogramming, antimicrobial and inflammatory responses, and cellular or tissue dysfunction. Conclusions: Molecular redox events may be involved in various infectious diseases, where different associated responses or disorders mediate.

Characterization of a miniaturized scintillator detector for time-resolved treatment monitoring in HDR-brachytherapy.

Purpose.HDR brachytherapy combines steep dose gradients in space and time, thereby requiring detectors of high spatial and temporal resolution to perform accurate treatment monitoring. We demonstrate a miniaturized fiber-integrated scintillator detector (MSD) of unmatched compactness which fulfills these conditions.Methods.The MSD consists of a 0.28 mm large and 0.43 mm long detection cell (Gd2O2S:Tb) coupled to a 110 micron outer diameter silica optical fiber. The fiber probe is tested in a phantom using a MicroSelectron 9.1 Ci Ir-192 HDR afterloader. The detection signal is acquired at a rate of 0.08 s with a standard sCMOS camera coupled to a chromatic filter (to cancel spurious Cerenkov signal). The dwell position and time monitoring are analyzed over prostate treatment sequences with dwell times spanning from 0.1 to 11 s. The dose rate at the probe position is both evaluated from a direct measurement and by reconstruction from the measured dwell position using the AAPM TG-43 formalism.Results.A total number of 1384 dwell positions are analyzed. In average, the measured dwell positions differ by 0.023 ± 0.077 mm from planned values over a 6-54 mm source-probe distance range. The standard deviation of the measured dwell positions is below 0.8 mm. 94% of the 966 dwell positions occurring at a source-probe inter-catheter spacing below 20 mm are successfully identified, with a 100% detection rate for dwell times exceeding 0.5 s. The average deviation to the planned dwell times is of 0.005 ± 0.060 s. The instant dose retrieval from dwell position monitoring leads to a relative mismatch to planned values of 0.14% ± 0.7%.Conclusion.A miniaturized Gd2O2S:Tb detector coupled to a standard sCMOS camera can be used for time-resolved treatment monitoring in HDR Brachytherapy.

Safety of combining dabrafenib plus trametinib in elderly BRAF V600 mutation-positive advanced melanoma patients: real-world data analysis of Spanish patients (ELDERLYMEL).

Efficacy and safety of dabrafenib and trametinib in metastatic melanoma have been demonstrated in two-phase III and one-phase I/II clinical trials. However, patients at least 75 years old (y.o.) were largely underrepresented. Additionally, the safety profile of dabrafenib and trametinib based on age is unknown. ELDERLYMEL is a retrospective noninterventional multicenter study, describing the effectiveness and safety of at least 75 y.o. patients compared with less than 75 y.o. patients with advanced BRAF V600-mutated melanoma treated with dabrafenib plus trametinib or dabrafenib monotherapy. A total of 159 patients were included, 130 less than 75 y.o. and 29 at least 75 y.o. Clinical features were similar between the groups, except in the number of comorbidities, number of metastatic sites, Eastern Cooperative Oncology Group (ECOG) performance status, and BRAF V600-mutation type. Five patients per group received dabrafenib monotherapy. There were no differences in adverse events (AEs) rate or grade between the groups. However, AE profiles were different between the groups, being pyrexia infrequent in patients at least 75 y.o. (13.8% vs. 42.3%; P = 0.005). Dabrafenib and trametinib dose intensities were lower in at least 75 y.o. patients ( P = 0.018 and P = 0.020), but there were no differences in effectiveness between the groups. Finally, in a multivariate analysis, sex (female) was the only variable independently associated with an increased risk of AE grade ≥3. Data from the ELDERLYMEL study demonstrate that dabrafenib plus trametinib is safe and effective in at least 75 y.o. patients with advanced BRAF V600-mutated melanoma without increasing toxicity. Additionally, we describe a different safety profile depending on age and sex.

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs.

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.