Hubungan Motif dengan Kepuasan Mendengar Podcast Rintik Sedu (Studi pada Pendengar Channel Podcast Rintik Sedu pada Platform Spotify)

Maraknya podcast sebagai media baru saat ini digandrungi masyarakat terutama generasi digital. Podcast menyajikan banyak program membuat pengguna menjadi aktif dan selektif dalam memilih sesuai dengan kebutuhan dan kepuasan yang berbeda-beda. Berbagai channel podcast berlomba untuk menyajikan konten audio yang memenuhi kebutuhan pendengar. Salah satunya channel podcast Rintik Sedu yang hadir berkonsep cerita sedih untuk membantu mengatasi permasalahan remaja terutama berhubungan dengan kesedihan, kesepian, dan patah hati. Berbeda dengan media lama, media baru menekankan peran khalayak aktif dan selektif selektif menentukan pilihan media sesuai kebutuhan tetapi melihat tindakan yang dapat dilakukan teknologi atau fitur pada media tersebut. Penelitian ini menggunakan rumusan teori uses and gratification 2.0 oleh Sundar & Limperos dalam meneliti gratifikasi lebih baru yang berhubungan dengan kredibilitas teknologi fitur untuk memunjang produktivitas konten yang memuaskan pendengar. Menggunakan The MAIN Model dipilih, sebagai dimensi penelitian dengan 15 indikator turunan sebagai berikut: Modality, terdiri dari multimodal, mindless, bells & whistles, distraction. Agency, terdiri dari bandwagon, novelty, coolness, realism. Interactivity, terdiri dari activity, emotional-needs, social-presence, ownness. Navigability, terdiri dari variety-seeking, multitasking and productivity, play-fun. Penelitian ini menggunakan metode survei pendekatan kuantitatif-eksplanatif, guna mengetahui perbedaan signifikan antara motif dengan kepuasan pendengar dalam mendengarkan channel podcast Rintik Sedu pada platform Spotify. Teknik pengambilan sampel menggunakan non probability sampling secara purposive sampling dengan melibatkan sebanyak 200 responden yang mendengarkan podcast Rintik Sedu pada platform Spotify. Hasil penelitian menunjukan bahwa terdapat 3 kepuasan yang berhasil dipenuhi channel podcast Rintik Sedu kepada khalayak pendengar yaitu kepuasan agensi, kepuasan interaktivitas, dan kepuasan navigasi

A inserção do setor siderúrgico na economia capixaba: uma análise de insumo-produto

The aim of this paper was to analyze the relative importance of the steel industry to the state of Espírito Santo, Brazil. The 1990 and 1999 Brazilian input-output matrices and Brazil’s regional accounts - state of Espírito Santo were used to build input-output matrices (1990 and 1999) to this state. Increases in final demand were simulated to analyze the effects on the production of each sector as well as on employment creation in this state. Backward and forward linkage indexes were also computed to twelve sectors with the same purpose. The results show that the steel industry is very important to the capixaba economy especially due to the high multiplier effects in response to increases in final demand. The results show also that the relative importance of the steel industry has grown during the 1990’s in the capixaba economy. When compared to other sectors, the steel industry has also one the higher linkage indexes, which reinforce the relative importance of this industry to the economy analyzed.Steel industry, Input-output, Linkage index, State of Espírito Santo, Production Economics,

Handling the challenge of antimicrobial resistant superbugs in the clinical setting: nursing staff as a pivotal player

Bacterial resistance to antibiotics (antimicrobial resistance, AMR) is spreading globally among major Gram-positive and Gram-negative bacterial pathogens (including staphylococci, enterococci, Enterobacterales, Pseudomonas aeruginosa and Bacterial resistance to antibiotics (antimicrobial resistance, AMR) is rapidly spreading globally among major Gram-positive and Gram-negative bacterial pathogens (including staphylococci, enterococci, Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii); this phenomenon has a remarkable impact on morbidity, mortality and healthcare-associated costs. Evolution and dissemination of AMR can be counteracted with a combined strategy based on i) antimicrobial stewardship programs aimed at a prudent and appropriate use of antibiotics to improve clinical outcomes and reduce the selective pressure for resistance; and ii) infection prevention and control (IPC) practices, to limit the spread of resistant pathogens within the healthcare settings. In this scenario, the nursing staff plays a pivotal role, since these figures are involved in the enforcement and supervision of IPC bundles (e.g., contact precautions, hand, and environmental hygiene, active surveillance, patient isolation, or cohorting), which are essential to limit the spread of resistant pathogens among different patients

Desmoplastic small round cell tumor:from state of the art to future clinical prospects

Introduction: Desmoplastic small round cell tumor (DSRCT) is an extremely rare and highly aggressive soft tissue sarcoma, presenting mainly in male adolescents and young adults with multiple nodules disseminated within the abdominopelvic cavity. Despite a multimodal approach including aggressive cytoreductive surgery, intensive multi-agent chemotherapy, and postoperative whole abdominopelvic radiotherapy, the prognosis for DSRCT remains dismal. Median progression-free survival ranges between 4 and 21 months, and overall survival between 17 and 60 months, with the 5-year overall survival rate in the range of 10–20%. Area covered: This review discusses the treatment strategies used for DSRCT over the years, the state of the art of current treatments, and future clinical prospects. Expert opinion: The unsatisfactory outcomes for patients with DSRCT warrant investigations into innovative treatment combinations. An international multidisciplinary and multi-stakeholder collaboration, involving both pediatric and adult sarcoma communities, is needed to propel preclinical model generation and drug development, and innovative clinical trial designs to enable the timely testing of treatments involving novel agents guided by biology to boost the chances of survival for patients with this devastating disease.</p

High prevalence of carriage of mcr-1-positive enteric bacteria among healthy children from rural communities in the Chaco region, Bolivia, september to october 2016

Background: The mcr-1 gene is a transferable resistance determinant against colistin, a last-resort anti-microbial for infections caused by multi-resistant Gram-negatives. Aim: To study carriage of antibiotic-resistant bacteria in healthy school children as part of a helminth control and antimicrobial resistance survey in the Bolivian Chaco region. Methods: From September to October 2016 we collected faecal samples from healthy children in eight rural villages. Samples were screened for mcr-1-and mcr-2 genes. Antimicrobial susceptibility testing was performed, and a subset of 18 isolates representative of individuals from different villages was analysed by whole genome sequencing (WGS). Results: We included 337 children (mean age: 9.2 years, range: 7–11; 53% females). The proportion of mcr-1 carriers was high (38.3%) and present in all villages; only four children had previous antibiotic exposure. One or more mcr-1-positive isolates were recovered from 129 positive samples, yielding a total of 173 isolates (171 Escherichia coli, 1 Citrobacter europaeus, 1 Enterobacter hormaechei). No mcr-2 was detected. Co-resistance to other antimicrobials varied in mcr-positive E. coli. All 171 isolates were susceptible to carbapenems and tigecycline; 41 (24.0%) were extended-spectrum β-lactamase producers and most of them (37/41) carried bla CTX - M -type genes. WGS revealed heterogeneity of clonal lineages and mcr-genetic supports. Conclusion: This high prevalence of mcr-1-like carriage, in absence of professional exposure, is unexpected. Its extent at the national level should be investigated with priority. Possible causes should be studied; they may include unrestricted use of colistin in veterinary medicine and animal breeding, and importation of mcr-1-positive bacteria via food and animals

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

The activity of laurel essential oil (crude and fractions) in the control of adult bovine ticks and larvae

Ectoparasites cause direct and indirect losses to farmers, affecting the production of meat and milk and increasing the production costs due to the acquisition of acaricides to maintain cattle health. The financial losses caused by Rhipicephalus (Boophilus) microplus, which is the main livestock ectoparasite in Brazil, reach approximately US$ 3 billion annually. The aim of this study was to evaluate the acaricidal effect of the crude essential oil (EO) and EO’s fractions (FR) obtained from Laurus nobilis L. (Lauraceae) leaves on Rhipicephalus (Boophilus) microplus. Eight fractions were obtained, wherein five major compounds were identified (sabinene, α-terpinyl acetate, 1,8-cineole, linalool, and α-terpineol). The acaricidal activity of these FR was tested by the larval packet test. The EO was tested by the adult immersion test, and, at concentrations of 200, 100 and 50 µL/mL, the oil caused mortality of engorged females, egg mass reduction, and hatching inhibition. The fractions with α-terpineol and sabinene, as the major compounds, were the most active larvicides (LC50=0.13 µL/mL, LC99=0.51 µL/mL; and LC50=0.20 µL/mL, LC99=0.56 µL/mL, respectively). This assessment also indicated that fractionation was important since most of the fractions obtained were more active than the EO. Furthermore, this is the first report of laurel EO and its fractions employed in the control of cattle ticks. Thereby, new prospects for the use of this essential oil or its chromatographic fractions in products applied for cattle tick control can be opened up. However, studies in other stages of development of cattle ticks for the active fractions, and studies under field conditions, the effect on non-target organisms and residual effect on the environment are still needed to evaluate the acaricidal activity of EO and its active fractions

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project