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La experiencia, más que visitar un nuevo lugar, comprende un encuentro entre culturas, personas y entornos naturales, que impacta tanto en los viajeros como en los destinos turísticos visitados. El presente estudio propuso determinar cómo la experiencia turística promueve un turismo sostenible en la Reserva Nacional de Lachay (RNL), Lima, Perú. Se desarrolló mediante un enfoque cualitativo, fenomenológico, con casos de estudio identificados mediante la técnica de muestreo bola de nieve; se identificaron cinco profesionales con experticia en el desarrollo de experiencias turísticas, a quienes se les entrevistó mediante una guía de entrevista semiestructurada. Se concluye que la RNL es un espacio turístico natural que ofrece a los turistas la posibilidad de experimentar sensaciones agradables a través de los sentidos, principalmente el sentido auditivo y visual; además, la visita permite reconocer e interiorizar el significado e importancia del ecosistema de lomas, ayudando a comprender la necesidad de su conservación para el beneficio de las generaciones futuras. Cabe precisar que la generación de experiencia turística requiere la confluencia de distintos elementos naturales, materiales y humanos, que repercute en beneficios económicos, sociales y ambientales para los turistas y la comunidad anfitriona.

The tourist experience is not just about visiting a new place; it involves encountering different cultures, meeting new people, and interacting with natural environments, which can profoundly impact travellers and the tourist destinations they visit. In this study, we aimed to determine how the tourist experience can promote sustainable tourism in the Lachay National Reserve (LNR). Using a qualitative, phenomenological approach, we identified five professionals with expertise in developing tourist experiences through snowball sampling. We then conducted semi-structured interviews using an interview guide. Our findings indicate that the LNR is a natural tourist space that allows visitors to experience pleasant sensations through their senses, primarily the auditory and visual senses. Moreover, visiting the reserve will enable visitors to recognise and internalise the importance of the Lomas ecosystem, which helps them understand the need for its conservation to benefit future generations. It is worth noting that generating tourist experiences requires the convergence of various natural, material, and human elements that have economic, social, and environmental benefits for tourists and the host community. [ABSTRACT FROM AUTHOR]

Background: For individuals who wish to reduce their cannabis use without formal help, there are a variety of self-help tools available. Although some are proven to be effective in reducing cannabis use, effect sizes are typically small. More insight into predictors of successful reduction of use among individuals who frequently use cannabis and desire to reduce/quit could help identify factors that contribute to successful cannabis use moderation., Methods: We analyzed data taken from a randomized controlled trial comparing the effectiveness of the digital cannabis intervention ICan to four online modules of educational information on cannabis. For the current study, we included 253 participants. Success was defined as reducing the grams of cannabis used in the past 7 days at baseline by at least 50 % at 6-month follow-up. To train and evaluate the machine learning models we used a nested k-fold cross-validation procedure., Results: The results show that the two models applied had comparable low AUROC values of .61 (Random Forest) and .57 (Logistic Regression). Not identifying oneself as a cannabis user, not using tobacco products, high levels of depressive symptoms, high levels of psychological distress and high initial cannabis use values were the relatively most important predictors for success, although overall the associations were not strong., Conclusions: Our study found only modest prediction accuracy when using machine learning models to predict success among individuals who use cannabis and desire to reduce/quit and show interest in digital self-help tools., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

Objective  The purpose of this study was to evaluate the clinical and functional results of simultaneous reconstruction of the ACL and PCL with bilateral hamstring autografts. We hypothesized that this reconstruction technique results in less morbidity and has similar results to the ones published in the previous literature. Methods  Eighteen patients with bicruciate lesions were selected and treated by arthroscopic surgery with autologous hamstring tendons in a single-stage procedure. The thicker semitendinosus tendon (ST) and the two gracilis tendons (G) were used for a 6-strand PCL reconstruction. The thinner ST was used for a 3-strand ACL reconstruction. The average patient age at surgery was 31 years, and the minimum follow-up was 2 years. Function of the operated knee was evaluated according to the Lysholm scale. Anterior knee laxity was examined with a KT-1000 arthrometer. Posterior laxity was evaluated using stress radiographies. Results  Statistically significant improvements were found for all three measurements ( p  < 0.001). Knee function by the Lysholm score increased from 43.8 ± 4.1 to 89.9 ± 3.8 post-surgery. The average anterior knee laxity improved from 5.2 + -0.8 mm initially to 2.4 + - 0.5 mm post-surgery. The posterior translation of the tibia relative to the femur decreased from 10 ± 3.4 mm to 3 ± 1.6 mm post-surgery. No patient showed loss of motion in extension or knee flexion. Conclusion  The simultaneous bicruciate reconstruction with bilateral hamstring autograft is a valuable option to achieve good functional outcomes and ligamentous stability., Competing Interests: Conflito de Interesses Os autores não têm conflitos de interesse a declarar., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit ( https://creativecommons.org/licenses/by/4.0/ ).)

Introduction: The aim of this study was to analyze the waiting list for kidney transplantation in our hospital according to candidate's panel reactive antibodies (cPRA) and its outcomes., Methods: One thousand six hundred forty patients who were on the waiting list between 2015 and 2019 were included. For the analysis, hazard ratios (HR) for transplant were estimated by Fine and Gray's regression model according to panel reactivity and HR for graft loss and death after transplantation., Results: The mean age was 45.39 ± 18.22 years. Male gender was predominant (61.2%), but the proportion decreased linearly with the increase in cPRA (p < 0.001). The distribution of patients according to panels were: 0% (n = 390), 1% - 49% (n = 517), 50% - 84% (n = 269), and ≥ 85% (n = 226). Transplantation was achieved in 85.5% of the sample within a median time of 8 months (CI 95%: 6.9 - 9.1). The estimated HRs for transplantation during the follow-up were 2.84 (95% CI: 2.51 - 3.34), 2.41(95%CI: 2.07 - 2.80), and 2.45(95%CI: 2.08 - 2.90) in the cPRA range of 0%, 1%-49%, and 50%-84%, respectively, compared to cPRA ≥ 85 (p < 0.001). After transplantation, the HR for graft loss was similar in the different cPRA groups, but the HR for death (0.46 95% CI 0.24-0.89 p = 0.022) was lower in the 0% cPRA group when adjusted for age, gender, and presence of donor specific antibodies (DSA)., Conclusion: Patients with cPRA below 85% are more than twice as likely to receive a kidney transplantation with a shorter waiting time. The risk of graft loss after transplantation was similar in the different cPRA groups, and the adjusted risk of death was lower in nonsensitized recipients.

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.

Purpose: To assess the effectiveness of voice disorder prevention programs and to verify the effectiveness of direct and indirect voice training on the Voice Handicap Index (VHI), Voice-Related Quality of Life (V-RQOL), maximum phonation time (MPT), fundamental frequency and shimmer, for the prevention of voice disorders in regular-school teachers., Methods: This is a literature review with meta-analysis, carried out through an electronic search in PubMed, Cochrane Central, Embase, Web of Science, Scopus and BVS, and Google Scholar, as well as ClinicalTrials.gov databases for gray literature., Selection Criteria: Randomized clinical trials and quasi-experimental studies were selected. The population consisted of regular schoolteachers, adults, with no vocal complaints submitted to a vocal health education and prevention program of voice disorders classified as direct (direct vocal tract training) and indirect (vocal hygiene guidelines that influence voice production). The outcomes vocal self-assessment: fundamental frequency, aerodynamic measurements (maximum phonation time), acoustic measures (shimmer) was analyzed in comparison with other vocal interventions or no vocal intervention., Data Analysis: Studies were independently assessed using the Cochrane Risk of bias and ROBINS-I tools. Effect sizes were calculated only at post-treatment. GRADE criteria were used to assess the quality of evidence., Results: Twenty-six studies were included but, only 12 studies were included in the meta-analysis and investigated for direct and/or indirect intervention compared to no intervention. No studies were judged to be at low risk of bias, and the quality of evidence was considered high only for the Voice Handicap Index. No evidence was found that suggested effectiveness of direct and indirect voice training for any of the outcomes: VHI -1.87 (95% CI -7.03, 3.30), maximum phonation time -1.11 (95% CI -3.40, 1.17), fundamental frequency 0.87 (-1.06, 2.79) and shimmer 0, 28 (-1.50, 2.05)., Conclusion: Results of this study were not considered significant to conclude on the effectiveness of voice training for preventing dysphonia in teachers., Competing Interests: Declaration of Competing Interest There are no conflicts of interest to declare., (Copyright © 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Although urban greening is universally recognized as an essential part of sustainable and climate-responsive cities, a growing literature on green gentrification argues that new green infrastructure, and greenspace in particular, can contribute to gentrification, thus creating social and racial inequalities in access to the benefits of greenspace and further environmental and climate injustice. In response to limited quantitative evidence documenting the temporal relationship between new greenspaces and gentrification across entire cities, let alone across various international contexts, we employ a spatially weighted Bayesian model to test the green gentrification hypothesis across 28 cities in 9 countries in North America and Europe. Here we show a strong positive and relevant relationship for at least one decade between greening in the 1990s-2000s and gentrification that occurred between 2000-2016 in 17 of the 28 cities. Our results also determine whether greening plays a "lead", "integrated", or "subsidiary" role in explaining gentrification., (© 2022. The Author(s).)

Aim: The aim of this study was to compare the peak extensor torque (primary outcome), sensory-motor capacity and Lysholm questionnaire score up to the fourth postoperative month post-primary anterior cruciate ligament reconstruction (ACLR) with bone-patellar-tendon-bone (BPTB) graft using either ipsilateral or contralateral technique in young sportsmen., Methods: We performed a randomized controlled trial from 2018 to 2019. The same preoperative protocol, surgical technique and postoperative protocol were used in both groups. Eighty-eight young adult male patients were randomized, and 44 of each group completed the trial., Results: After the follow up analysis, the contralateral receptor and donor limb demonstrated a statistically significant increase in peak extension torque compared with the ipsilateral operated limb (effect size (ES) = 0.99, power = 0.99, P < 0.0001; ES = 0.46, power = 0.56, P < 0.04). The symmetry between limbs was compared between groups. In the contralateral group, there was a significant decrease in the recipient limb (after surgery), compared with the donor limb (before surgery) (ES = 0.87, power = 0.99, P < 0.0001). In the ipsilateral group, the operated limb demonstrated a significant decrease in peak extension torque compared with the non-operated limb after surgery (ES = 1.88, power = 0.99, P < 0.0001). The comparison between groups in sensory-motor capacity and Lysholm score demonstrated a non-significant group effect postoperatively., Conclusion: The use of contralateral BPTB is more effective in increasing peak extension torque when compared with ipsilateral technique in young sportsmen. Furthermore, patients achieved greater symmetry (side to side) in quadriceps muscle performance after the 4-month follow up with a contralateral graft., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline., Methods: We included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process., Results: From 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach., Conclusions: The combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values., Competing Interests: Erasmus MC received funds from AL. Amsterdam UMC received funds from Stryker for consultations by CM and YR. MUMC received funds from Stryker and Codman for consultations by WZ. CM reports grants from the TWIN Foundation, the CVON/Dutch Heart Foundation, and the European Commission. HM is cofounder and shareholder of Nico.lab. YR and CM own stock in Nico.lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ramos, Os, Hilbert, Olabarriaga, Lugt, Roos, Zwam, Walderveen, Ernst, Zwinderman, Strijkers, Majoie, Wermer and Marquering.)

Background: Distal segment atresia (isthmus) is an extremely rare anatomical variant of obstructive aortic arch anomalies., Case Report: We present the case of a newborn who, at 48 hours of life, presented a clinical picture of heart failure. The initial echocardiogram showed a congenital interrupted aortic arch type A, patent ductus arteriosus, and ventricular septal defect. Prostaglandins were initially indicated. Subsequently, a second echocardiogram showed the absence of ductus arteriosus; the CT angiography study confirmed this finding and revealed blood flow to the descending aorta through small intercostal blood vessels. The possibility of atresia of the distal segment (isthmus) of the aortic arch was considered and confirmed at the time of surgery., Conclusions: Aortic atresia should be considered a diagnostic possibility in the presence of type A interrupted aortic arch since the hemodynamic behavior between them is similar. Surgical medical treatment should be individualized since this condition is frequently an emergency in the neonatal period. However, this is not always the case, as other cases have been reported in schoolchildren and adults., (Copyright: © 2022 Permanyer.)

Several species of the planktonic free-living genus Oithona have been successfully used in the larviculture of marine fish and shrimp. However, few studies have been published that allow us to estimate the potential of Oithona nana culture under controlled conditions. This work evaluated the effect of the microalgae Isochrysis galbana and Chaetoceros calcitrans as single (200,000 cells/mL) and mixed diets (100,000 + 100,000 cells/mL) on population and individual growth, ingestion rate, number of spawnings, fertility, development time by stage, and sex ratio of O. nana . We cultured this copepod at 28 ± 0.5 °C, 35 PSU salinity, 125 lux, and 12:12 photoperiod. Results showed that diet had no effect on the final population level (6273-7966 ind/L) or on individual growth, nor on sex ratio, with less males than females. With C. calcitrans , O. nana had a higher filtration rate (57 ng C/ind/day). On the other hand, a mixed diet induced a higher number of spawns (0.4 events/day) and nauplii per spawn (23 ind). Similarly, a single or mixed diet, containing I. galbana, accelerated the development rate by 6.33-7.00 days. We concluded that O. nana can be cultured with both microalgae, indicating its potential use in an intensive system for production. However, more research is required to improve the productivity of O. nana rearing.

Injury to the medial compartment of the knee is the most common ligament injury to this joint. The medial approach must consider the presence of associated anteromedial instability. Untreated injury of these instabilities can result in failure of the other reconstructed ligaments. As treatment is usually associated with other ligaments, it is relevant that the technique could save grafts and synthetic material. This article aims to describe a technique for the treatment of anteromedial instabilities through semimembranosus tendon tenodesis in a more anterior and distal position, promoting the tensioning of the posteromedial structures., (© 2021 by the Arthroscopy Association of North America. Published by Elsevier.)

Thoracoscopic surgical ablation (SA) for atrial fibrillation (AF) has shown to be an effective treatment to restore sinus rhythm in patients with advanced AF. Identifying patients who will not benefit from this procedure would be valuable to improve personalized AF therapy. Machine learning (ML) techniques may assist in the improvement of clinical prediction models for patient selection. The aim of this study is to investigate how available baseline characteristics predict AF recurrence after SA using ML techniques. One-hundred-sixty clinical baseline variables were collected from 446 AF patients undergoing SA in our tertiary referral center. Multiple ML models were trained on five outcome measurements, including either all or a number of key variables selected by using the least absolute shrinkage and selection operator (LASSO). There was no difference in model performance between different ML techniques or outcome measurements. Variable selection significantly improved model performance (AUC: 0.73, 95% CI: 0.68-0.77). Subgroup analysis showed a higher model performance in younger patients (<55 years, AUC: 0.82 vs. >55 years, AUC 0.66). Recurrences of AF after SA can be predicted best when using a selection of baseline characteristics, particularly in young patients.

Background: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement., Methods: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy., Results: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69-0.73) and (0.71 95%CI 0.67-0.76), respectively, followed by cannabis (0.67 95%CI 0.59-0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success., Discussion: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ramos, Blankers, van Wingen, de Bruijn, Pauws and Goudriaan.)

Objective: To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA)., Methods: Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months., Results: The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives., Conclusions: Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data., Significance: Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital., Design: Retrospective cohort study., Setting: A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020., Participants: SARS-CoV-2 positive patients (age ≥18) admitted to the hospital., Main Outcome Measures: 21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis., Results: 2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81)., Conclusion: Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage., Competing Interests: Competing interests: The COVID-predict consortium declare to have received non-financial support from Castor, who provided access and use of their database free of charge. Pacmed occasionally provided scientific support for methodology and analysis., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The diagnosis of rapid eye movement (REM) sleep behavior disorder (SBD) requires videopolysomnography detection of excessive electromyographic activity during REM sleep, which is time consuming and difficult. An easier, faster, reliable, and reproducible methodology is needed for its diagnosis. The isolated form of RBD represents an early manifestation of the synucleinopathies Parkinson disease and dementia with Lewy bodies. There is a need to find neuroprotective drugs capable of preventing parkinsonism and dementia onset in isolated RBD. Clonazepam and melatonin ameliorate the RBD symptoms, but therapeutic alternatives are needed when these medications fail or show produce side effects., (Copyright © 2021 Elsevier Inc. All rights reserved.)