Set Of Media Research Articles

Bladder urine microbiota in children with vesicoureteral reflux: preoperative values

Introduction. Urinary tract infections (UTIs) are prevalent among children, particularly those with anatomical anomalies such as vesicoureteral reflux (VUR). The diagnosis of VUR typically follows an episode of UTI, highlighting the importance of early detection of VUR in case of UTIs to prevent renal complications. The management of a first febrile UTI remains a subject of debate among medical professionals. Pediatricians often recommend diagnosing VUR after recurrent UTIs, while urologists advocate for early imaging and prophylaxis. Antibiotic prophylaxis (ABP) before surgery can reduce the risk of recurrent UTIs; however, it also induces antibiotic resistance and alters the microbiota.Objective. To evaluate the microbiota of bladder urine in children before surgery for VUR.Materials & methods. The study included 40 children (12 boys — group 1, 28 girls — group 2) diagnosed with MTCT after symptomatic urinary tract infection. The control groups were 18 healthy boys (group 3) and 16 healthy girls (group 4). Urine was collected and cultured using an expanded set of nutrient media (10–12) for culturing aerobic and anaerobic taxa of microbiota.Results. The median age of VUR-patients in the study groups was 3.6 years. A statistically significant predominance of girls was observed (p < 0.001). The urine samples from VUR-children were unsterile, and a total of 27 taxa of microorganisms were detected. Among them, 16 taxa were aerobes, and 11 taxa were anaerobes. The dominant taxa differed between the groups: Enterobacterales, Peptococcus spp., and Anaerococcus spp. predominated in VURboys, while coagulase-negative staphylococci, Corynebacterium spp., and Peptococcus spp. dominated in VUR-girls. Most microorganisms were found in associations. The median bacteriuria level was mostly 10² CFU/ml. In total, 29 microbiota taxa with dominant aerobic-anaerobic relationships were identified in healthy children and VUR-patients. There were more significant correlations between different taxa of the urine microbiota in VUR-patients compared to healthy children.Conclusion. Bladder urine from VUR-children contains aerobic and anaerobic bacteria. It is yet to be determined whether bacterial associations with a bacteriuria of 10² CFU/ml increase the risk of UTIs in VUR surgery, but urinary dysbiosis should be investigated as a possible risk factor for UTIs. Further studies are needed to justify the feasibility and suitability of selective ABP in the treatment of VUR.

Be Sure to Use the Same Writing Style: Applying Authorship Verification on Large-Language-Model-Generated Texts

Recently, there have been significant advances and wide-scale use of generative AI in natural language generation. Models such as OpenAI’s GPT3 and Meta’s LLaMA are widely used in chatbots, to summarize documents, and to generate creative content. These advances raise concerns about abuses of these models, especially in social media settings, such as large-scale generation of disinformation, manipulation campaigns that use AI-generated content, and personalized scams. We used stylometry (the analysis of style in natural language text) to analyze the style of AI-generated text. Specifically, we applied an existing authorship verification (AV) model that can predict if two documents are written by the same author on texts generated by GPT2, GPT3, ChatGPT and LLaMA. Our AV model was trained only on human-written text and was effectively used in social media settings to analyze cases of abuse. We generated texts by providing the language models with fanfiction snippets and prompting them to complete the rest of it in the same writing style as the original snippet. We then applied the AV model across the texts generated by the language models and the human written texts to analyze the similarity of the writing styles between these texts. We found that texts generated with GPT2 had the highest similarity to the human texts. Texts generated by GPT3 and ChatGPT were very different from the human snippet, and were similar to each other. LLaMA-generated texts had some similarity to the original snippet but also has similarities with other LLaMA-generated texts and texts from other models. We then conducted a feature analysis to identify the features that drive these similarity scores. This analysis helped us answer questions like which features distinguish the language style of language models and humans, which features are different across different models, and how these linguistic features change over different language model versions. The dataset and the source code used in this analysis have been made public to allow for further analysis of new language models.

Language style (mis)matching: Consuming entertainment media from someone unlike you is linked to positive attitudes

Prior work suggests people often match with conversational partners by using a common rate of style words (e.g., articles, pronouns). Indeed, such language style matching (LSM) has associated positively with downstream social and psychological dynamics like cooperation, liking, and well-being. To what degree is LSM predictive of positive attitudes in entertainment media settings? The current two-study paper addressed this question by collecting participants’ writing style with three diverse prompts, and then having them consume a random selection of TED talks (Study 1) and videotaped podcast narratives (Study 2). The evidence suggested less LSM was associated with positive attitudes (e.g., an interest in watching another video by the speaker, feeling connected to the speaker). Mediation analyses revealed the negative relationship between LSM and positive attitudes was explained by one’s novelty need satisfaction. Implications for LSM research, plus character construction and narrative development in media psychology, are discussed.

PLATFORMS ON TRIAL: MAPPING THE FACEBOOK FILES/PAPERS CONTROVERSY

Big Tech has been involved in several controversies in recent years. Data leaks, whistleblowers, and social experiments have raised alarms about the dubbed toxic and unaccountable power of platforms and, more broadly, about the increasing crisis of accountability in digital societies (Cooper et al., 2022; Khan, 2018; Marres, 2021). At the intersection of digital sociology, media studies, and STS, this research explores how different actors make and unmake connections between social media platforms and societal harms. The study focuses on mapping a specific platform controversy: the Facebook Files/Papers, a leak in 2021 of internal documents from Meta by the former employee Frances Haugen. The disclosures partially exposed what Meta knew about the consequences of its interface designs, data, and algorithms (Hendrix, 2021; Horwitz, 2021). By combining digital and ethnographic methods, I followed the disclosures across different media settings to analyse how they were made public and which actors, issues, and framings gained prominence during the controversy. As I will show, journalists, advocacy groups, and critics promoted a ‘strategic causalism’ to strengthen the connection between Meta platforms and specific social harms. This was contrasted by the ‘strategic ambiguity’ mobilised by Meta spokespersons to undermine such claims and disperse their responsibility to other actors (e.g. users, malicious actors). The analysis highlights a US-centric theatre of accountability that overlooks crucial issues from the actual disclosures, revealing the asymmetries and displacements when platforms are put on trial.

Dilute Species Transport During Generalized Newtonian Fluid Flow in Porous Medium Systems

AbstractDilute species transport in generalized Newtonian fluids (GNFs) is typically described using explanatory empirical approaches assuming a traditional Fickian form, which is an approach that lacks predictive ability for systems and conditions not specifically investigated. Dilute species transport was investigated for a wide range of Cross and Carreau fluids flowing through a set of monodisperse and polydisperse sphere pack porous media. Both microscale and macroscale simulations were performed to demonstrate that GNF fluid flow can be predicted based upon Newtonian characterization of the media and rheological characterization of the fluid. Dilute species transport was shown to have a Fickian limit with dispersivity dependent on the porous media, fluid properties, and the flow rate in a nonlinear fashion. Dimensionless analysis and symbolic regression was used to deduce an explanatory and predictive function to describe dispersivity in terms of relevant system properties, enabling prediction of dilute species transport for GNFs flowing through porous media that does not require any non‐Newtonian experiments or parameter estimation.

Analyzing the influence of physical posture on audience perception in mass media presentations

Non-verbal communication, especially physical posture, affects audience perception. From a cellular and molecular biomechanics angle, different postures may trigger unique intracellular responses. Upright or leaning forward postures might activate neural pathways that enhance neurotransmitter release related to positive perception. In contrast, a slouched posture could disrupt normal cellular signaling, potentially leading to a less favorable audience perception. This study explores the impact of four postures on audience views in a media setting, aiming to offer data on how posture shapes key perceptions and provide valuable insights for Mass-media Presentations (MMP), despite limited prior research on this aspect. A within-subject experimental design was employed, with 34 participants observing media presentations under four posture conditions. Posture was the independent variable, while credibility, trustworthiness, engagement, and authority were the dependent variables. Data were collected using surveys, posture monitoring devices, and eye-tracking data. Statistical analyses, including Analysis of Variance (ANOVA) and paired t-tests, were conducted to determine significant differences between posture conditions. Upright and leaning forward postures were associated with the highest audience ratings for credibility, trustworthiness, engagement, and authority. Slouched posture consistently led to the lowest ratings across all measures. The ANOVA results revealed significant differences in perceptions of engagement (F = 10.21, p = 0.0008) and credibility (F = 8.67, p = 0.0013). Paired t-tests and post-hoc analyses confirmed that upright posture significantly outperformed slouched posture across all metrics, with large effect sizes (Cohen’s d > 1.0). Posture significantly influences audience perceptions in mass media presentations. Upright and leaning forward postures enhance credibility, trustworthiness, engagement, and authority, while slouched posture diminishes these perceptions. These findings provide practical insights for media professionals, suggesting that careful attention to posture can improve the effectiveness of media presentations. Future research could investigate how gestures and facial expressions interact with these cellular and molecular mechanisms to shape audience engagement.

Squeeveillance: Performing Cuteness to Normalise Surveillance Power

Cute videos are everywhere online. Many of these videos increasingly come from footage taken by doorbell cameras. Amazon’s Ring, and related connected camera devices, introduce new sociotechnical relations into domestic environments. First, I outline “squeeveillance” as the affective and performative dimensions of cuteness within surveillance. I explore the Ring surveillant assemblage and why it needs the power of cuteness. Then, I examine squeeveillance as the use of cuteness in the way Ring operates. I use the TV show Ring Nation (2022–present) to discuss the remediation of cute footage from doorbell cameras onto other media, before discussing the ways in which cuteness is performed as a normalisation of surveillance power. The article draws on theories of cuteness in conjunction with surveillance studies of power relations. In presenting squeeveillance as a lens through which to assess the expanding scope of Ring, I offer a discussion of the interconnected role of surveillance in contemporary domestic and media settings and its relation to current forms of power in surveillant assemblages.

Composition and dynamics of bladder, vaginal and bowel microbiota during three trimesters in healthy pregnant women

Introduction. The microbiota dynamics of the core biotopes during pregnancy are hardly studied, although changes in these compartments have an important role in both the functioning of the female organism and foetal development.Objective. To study the dynamics and interactions of changes in bladder, vaginal and bowel microbiota in healthy pregnant women over three trimesters (TRI-1, 2, 3).Materials & Methods. Study design: a single-centre comparative observational longitudinal study. Thirty out of first-time 220 pregnant women were selected for screening at the antenatal clinic from 2021 to 2022. All pregnant women underwent sampling at T-1, 2, 3: mid-stream bladder urine samples, posterior vaginal swabs and faecal masses were collected for culture study. After a special pre-culture preparation, samples were examined on an expanded set of nutrient media (n = 13) using special cultivation (aerobic-anaerobic) conditions. Based on the research results, identification frequencies (IDFs), microbial load values (MLVs) and microbial co-occurrence coefficients between the different biotopes were estimated.Results. Culture study revealed various bacteria in each biotope investigated during all TRIs. In the urine, aerobes and anaerobes were observed from TRI-1 to TRI-3 with different IDFs, but no taxa showed a stable IDFs. In the vagina, IDFs of bacteria were similar to urinary. The bowel microbiota was the most stable biotope remained almost unchanged during pregnancy. In the urine and vagina, mean MLVs of most aerobes and anaerobes did not change significantly throughout pregnancy. In the bowel, MLVs were consistently higher than in the urine and vaginal swabs. According to the co-occurrence analysis bladder-vagina and bladder-bowel biotopes showed significantly more interconnections between microorganisms in all TRIs.Conclusion. The observed microbiota structure during all TRIs is associated with uncomplicated gestation. These results will be valuable for studying changes of microbiota in complicated pregnancies.

Impact of emotional contagion on waste separation intention in social media settings—Evidence based on machine learning and text analysis

Impact of emotional contagion on waste separation intention in social media settings—Evidence based on machine learning and text analysis

DPD (DePression Detection) Net: a deep neural network for multimodal depression detection.

Depression is one of the most prevalent mental conditions which could impair people's productivity and lead to severe consequences. The diagnosis of this disease is complex as it often relies on a physician's subjective interview-based screening. The aim of our work is to propose deep learning models for automatic depression detection by using different data modalities, which could assist in the diagnosis of depression. Current works on automatic depression detection mostly are tested on a single dataset, which might lack robustness, flexibility and scalability. To alleviate this problem, we design a novel Graph Neural Network-enhanced Transformer model named DePressionDetect Net (DPD Net) that leverages textual, audio and visual features and can work under two different application settings: the clinical setting and the social media setting. The model consists of a unimodal encoder module for encoding single modality, a multimodal encoder module for integrating the multimodal information, and a detection module for producing the final prediction. We also propose a model named DePressionDetect-with-EEG Net (DPD-E Net) to incorporate Electroencephalography (EEG) signals and speech data for depression detection. Experiments across four benchmark datasets show that DPD Net and DPD-E Net can outperform the state-of-the-art models on three datasets (i.e., E-DAIC dataset, Twitter depression dataset and MODMA dataset), and achieve competitive performance on the fourth one (i.e., D-vlog dataset). Ablation studies demonstrate the advantages of the proposed modules and the effectiveness of combining diverse modalities for automatic depression detection.

FHG-PR: a hybridized fuzzy-AHP and game theory model for assessing privacy risk on social media platforms

Social media platforms have become integral to our daily lives, enabling global connectivity and interaction. Current research provides general solutions for mitigating privacy risks, but it does not focus on particular platforms. However, the voluntary sharing of personal information on these platforms presents considerable privacy risks, highlighting the need for effective risk assessment mechanisms. This study presents a hybrid approach that uses the fuzzy Analytical Hierarchy Process (AHP) and game theory to estimate privacy risks on Meta and X. We used a fuzzy AHP to rank determinant variables based on surveys of social media users and professionals. These weighted criteria were then used in a Multi-Criteria Decision-Making (MCDM) framework using cooperative game theory to discover ways to lower privacy hazards. The model accurately analyzed the privacy threats on Meta and X, recommending alternate techniques for preventing breaches in data privacy. The cooperative game theory method allows stakeholders to collaborate on creating privacy-preserving measures. The findings emphasize the model’s ability to address platform-specific privacy threats, as well as its flexibility in other social media settings. The model’s potential for real-world application is demonstrated by its ability to provide practical risk reduction measures, which improve privacy protection for social media users.

Three-Year Monitoring of Microorganisms' Composition and Concentration in Atmospheric Aerosols of Novosibirsk City and Suburbs.

The atmospheric environment is formed under the influence of local and distant sources as a result of horizontal and vertical transport. In the present work, microbiological analysis of 604 samples of atmospheric aerosol collected in the period from September 2020 to September 2023 at four sites differing in anthropogenic load, located in Novosibirsk and the region, was carried out. Day and night aerosol samples were collected during 12 h every two weeks by filtration using Sartorius reinforced Teflon membranes, then sown on a set of nutrient media. The taxonomic affiliation of the isolated microbial isolates was determined based on phenotypic characteristics and analysis of 16S rRNA gene nucleotide sequences. Changes in the composition and concentration of culturable microorganisms depending on the season, time of day, and site of aerosol sampling were observed. In winter, lower fungi and bacteria of the genera Bacillus, Staphylococcus, Micrococcus dominated with an average concentration from zero to 12.5 CFU/m3 of aerosol. In the warm period, the concentration and diversity of cocci, spore-forming and non-spore-forming bacteria, actinomycetes, and fungi (up to 1970 CFU/m3), among which pathogenic microorganisms were found, increased sharply in aerosols. The use of 16S metabarcoding techniques has greatly expanded the range of aerosols' microbial diversity detectable.

Becoming Spectral: Toward a Media History of Ghosting

This article contextualizes contemporary forms of digital ghosting by examining how two of its historical precursors—Victorian calling culture and answering machines—have been represented in North American women’s magazines. To do so, we develop mediated avoidance as an analytical heuristic. This concept captures the material, relational and social dimensions of a set of understudied media practices that seek to strategically engage with the gaps that are inherent in all communication, to defer, deflect, or disrupt mediated connections. Representations of mediated avoidance from respective eras were found to reflect different anxieties over the management of the public/private divide. Calling culture relied on unpaid labor to facilitate the transmission of printed messages between bourgeoise women and was constrained by an array of social protocols that regulated interactions along conceptions of propriety. The disconnective features of answering machines, meanwhile, were represented as giving women the upper hand in courtship, as well as providing means for increased productivity and self-care, foreshadowing contemporary justifications of digital disconnection. Concerns over contemporary ghosting are discussed as produced by a spillage of media practices. Ghosting is considered acceptable in feminine-coded spheres like courtship. But it is viewed as inappropriate—sometimes even as signaling a broader social crisis—when it bleeds into other contexts, like when an employee ghosts their employer.

Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study.

Manually analyzing public health-related content from social media provides valuable insights into the beliefs, attitudes, and behaviors of individuals, shedding light on trends and patterns that can inform public understanding, policy decisions, targeted interventions, and communication strategies. Unfortunately, the time and effort needed from well-trained human subject matter experts makes extensive manual social media listening unfeasible. Generative large language models (LLMs) can potentially summarize and interpret large amounts of text, but it is unclear to what extent LLMs can glean subtle health-related meanings in large sets of social media posts and reasonably report health-related themes. We aimed to assess the feasibility of using LLMs for topic model selection or inductive thematic analysis of large contents of social media posts by attempting to answer the following question: Can LLMs conduct topic model selection and inductive thematic analysis as effectively as humans did in a prior manual study, or at least reasonably, as judged by subject matter experts? We asked the same research question and used the same set of social media content for both the LLM selection of relevant topics and the LLM analysis of themes as was conducted manually in a published study about vaccine rhetoric. We used the results from that study as background for this LLM experiment by comparing the results from the prior manual human analyses with the analyses from 3 LLMs: GPT4-32K, Claude-instant-100K, and Claude-2-100K. We also assessed if multiple LLMs had equivalent ability and assessed the consistency of repeated analysis from each LLM. The LLMs generally gave high rankings to the topics chosen previously by humans as most relevant. We reject a null hypothesis (P<.001, overall comparison) and conclude that these LLMs are more likely to include the human-rated top 5 content areas in their top rankings than would occur by chance. Regarding theme identification, LLMs identified several themes similar to those identified by humans, with very low hallucination rates. Variability occurred between LLMs and between test runs of an individual LLM. Despite not consistently matching the human-generated themes, subject matter experts found themes generated by the LLMs were still reasonable and relevant. LLMs can effectively and efficiently process large social media-based health-related data sets. LLMs can extract themes from such data that human subject matter experts deem reasonable. However, we were unable to show that the LLMs we tested can replicate the depth of analysis from human subject matter experts by consistently extracting the same themes from the same data. There is vast potential, once better validated, for automated LLM-based real-time social listening for common and rare health conditions, informing public health understanding of the public's interests and concerns and determining the public's ideas to address them.

Territorial imagination: social media, visual images, and affect in Iranian Kurdistan

Thousands of Iranian Kurds took to the streets in October 2014 in solidarity with the Syrian Kurds’ resistance against the so-called Islamic State (IS), and again in September 2017 to support Iraqi Kurds’ independence referendum. The demonstrations were momentous, given their size, scope, and defiance of the Iranian state’s security measures aimed at suppressing manifestations of Kurdish identity on the streets and on social media. This article examines the use of social media during the demonstrations. Specifically, it investigates how Iranian Kurds utilized social media and visual images to situate the demonstrations within the larger Kurdish resistance in the region and frame the Kurdish movement spatially. Deploying digital methods and poststructural discourse analysis, the article examines a small set of social media posts and draws on semi-structured interviews to deepen and contextualize the analysis. Findings illustrate the role of affect and emotion in enabling bodies to connect, resist the state’s securitized territoriality, and produce a particular territorial imagination – one that foregrounds Kurdistan as a meaningful non-state geopolitical construct. The article emphasizes the mutually constitutive relations between the affective-emotional and the discursive-symbolic by foregrounding users’ agency in connecting with social media and visual images. Highlighting the production of minoritized territorial imaginations, the paper contributes to the literature on social media, visuality, and space.

Relating the ultrasonic and aerosol filtration properties of filters

Non-contact methods are useful to improve the quality control of particle filtration media. The purpose of this paper is to investigate the correlation between the filtration efficiency of a porous sheet and its ultrasonic properties obtained using a non-contact technique. An air-coupled ultrasonic technique is used to obtain rapid measurements without affecting the integrity of the material. High frequencies (from 0.1 to 2.5 MHz) are used to improve technique sensitivity, and transmitted waves are measured to probe the internal properties of the material. Measurements of transmission coefficient spectra (amplitude and phase) and the corresponding ultrasound velocity and attenuation coefficient at different frequencies are obtained for a set of filtration media with well-characterized properties. Results show that the ultrasonic properties of filtration media vary as a function of basis weight, and therefore filtration efficiency, for a given charge state. However, the effect of electrostatic charge on ultrasonic propagation is almost negligible, as expected. We conclude that ultrasonic transmission may provide a valuable tool for the continuous online monitoring of material quality during fabrication and as a method to tease apart mechanical and electrostatic contributions to particle filtration.

Urine microbiota in non-muscle-invasive bladder cancer and approaches to its detection

Introduction. There is evidence of a connection between infectious and inflammatory lesions of the lower urinary tract and bladder cancer (BCa). However, there is virtually no data on the urine microbiota of middle-aged and elderly men with suspected BCa. This knowledge is extremely important from the point of view of studying the role of the infectious-inflammatory hypothesis in the genesis of BCa.Objective. To conduct a comparative assessment of the microbiota of bladder urine obtained during natural urination and bladder catheterization through standard and extended cultural studies in men with a presumptive diagnosis of non-muscle-invasive bladder cancer (NMIBC).Materials &amp; methods. The prospective comparative study with consecutive patient recruitment included 23 men older than 45 years with suspected NMIBC based on clinical, laboratory and sonographic data, as well as with no history of infectious and inflammatory diseases of the kidneys and urinary tract, including sexually transmitted infections and recurrent infections of other organs and systems. A midstream urine samples and catheter-drained urine immediately before urethrocystoscopy were subjected to bacteriological analysis. Urine culture study was carried out using a standard set of nutrient media under aerobic cultivation conditions and an expanded set of nutrient media under aerobic and anaerobic cultivation conditions.Results. An anaerobic spectrum of microorganisms was predominantly detected in the urine of patients with suspected NMIBC. Moreover, Peptococcus spp. was more often found among anaerobic taxa. (70%), Eubacterium spp., Propionibacterium spp. (45% each, respectively), among representatives of the aerobes — Corynebacterium spp. (60%), S. lentus (up to 45%), S. haemolyticus (35%) and E. faecalis (30%). Data from a comparative analysis of the detection frequencies of microorganisms depending on the method of collecting material and the set of media for cultivation showed that more isolates are isolated in the midstream urine samples than in the study of catheter urine (2.9- and 1.9-fold with the standard and extended sets of media, respectively). In addition, when performing an extended bacteriological study compared to the standard method, the detection of microorganisms is 3.5-fold higher when examining an average portion of urine and 5.1-fold higher when examining catheter urine. The average number of microorganisms per one patient is also higher when using the extended method (midstream portion — 4.7 ± 1.9; catheterised urine — 2.3 ± 1.0) in comparison with the standard one (midstream portion — 1.3 ± 0.9; catheterised urine — 0.3 ± 0.5).Conclusion. Middle-aged and older men with NMIBC are characterized by asymptomatic bacteriuria in the form of a wide range of aerobic and anaerobic microorganisms. The urine pattern obtained with a urethral catheter contains a significantly narrower range of bacteria compared to the sample obtained through natural urination. It is advisable to carry out an extended cultural urine examination to study the characteristics of the microbiota / microbiome of the urine of the bladder immediately before the urethrocystoscopy procedure by collecting urine with a urethral catheter.

Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response

ABSTRACT Artificial intelligence (AI) and big data have the potential to promote advancement across various industries. Sport management and marketing have also significantly transformed due to rapid technological advances such as those in AI and big data analytics. Especially sports companies, however, are still underutilizing the potential of AI. At the same time, considering the existing sport marketing research, the effectiveness and optimization of dynamic marketing stimuli in dynamic sport media settings remains unclear. This study aims to assess the differences between two AI models’ predictive capabilities with and without access to consumers’ biometric data when forecasting the influence of game features on consumers’ responses. Academic theoretical models indicate that individual biometric features have a considerable influence on consumers’ responses; nevertheless, it remains impractical for companies to access these data concerning message effectiveness and ROI evaluation. Therefore, the study attempts to enhance the feasibility of message optimization for companies by trialing a real-time prediction derived from game features alone, exemplifying how much predictive capability is lost by non-available consumer data. Two supervised machine learning models (one initial, primarily theoretical based model; one adapted model due to available data) were trained to reanalyze large-scale eye tracking and game-related data, resulting in high predictive accuracy and appropriate applicability of the models. Both models were able to predict consumers’ responses with over 90% accuracy (initial model: 96%; adapted model: 94%). This study exemplifies AI usage in sport marketing and management, enabling companies to implement more effective marketing messages and strategies for their sponsorship based on real-time evaluation.

O-131 A novel approach to predicting the potential of a blastocyst using untargeted spectral analysis of spent culture medium and machine learning

Abstract Study question Is it possible to effectively predict negative outcomes of intrauterine embryo transfer using modified untargeted Raman spectroscopy of the spent culture medium? Summary answer The proposed spectroscopic approach, combined with machine learning methods, allows identifying a group of embryos that are almost unable to implant and develop ongoing pregnancy. What is known already Embryo deselection is usually based on the karyotype analysis of the embryo and originates from the assumption that an abnormal PGT result is associated with negative transfer outcomes. Embryo selection methods aimed at identifying the most capable for implantation embryo are based on morphological, morphometric, and metabolic approaches. However, due to the dependence of the phenomenon (pregnancy) on several factors, predicting the outcome of the transfer solely through variables derived from the embryo does not correspond to modern views on implantation. At the same time, predicting negative transfer outcomes through the information originating from the embryo appears quite rational. Study design, size, duration 424 samples of spent culture medium used, collected from microdrops with individually cultured blastocyst from the internal institutional biosamples repository. Medium samples were collected from July 1, 2021, to June 1, 2023. The class attribute for each sample was the absence or presence of ongoing pregnancy at the 12-14 week of gestational age. The positive class (ongoing pregnancy) proportion in the presented dataset is 33%, and the negative (no ongoing pregnancy) class proportion is 67%. Participants/materials, setting, methods The study material was spent culture medium from blastocysts with known transfer outcomes by the end of first trimester. All media samples were stored at -86 °С until the moment of spectroscopy. Before the study, samples were thawed at room temperature for 15 minutes. Untargeted Raman spectroscopy was done on a signal amplification substrate using a green laser (532 nm). The architecture selection and machine learning model training were performed using the Python library mcfly. Main results and the role of chance After building the model the following results for validation and test sets were obtained: So it is possible to identify a group of embryos that are almost incapable of producing ongoing pregnancy. This group constitutes about 40% of the total number of negative outcomes. The error of determining an embryo as being incapable for implantation and normal pregnancy development is about 6%, what is relatively low number. Limitations, reasons for caution The presented data are not multicenter; they were obtained from samples from one laboratory and using a limited set (only one manufacturer) of culture media. This may lead to reduced or nonexistent relevance of the model for data from other sources and conditions. Wider implications of the findings It is necessary to check the proposed method on an additional set of culture media from other manufactures and laboratories. This may lead to refining the parameters of the built neural network. The assumption of significant clinical value of the developed model needs to be checked through prospective testing. Trial registration number fpc-2023-ivf-01

Comparative Analysis of the Mass Spectra of Mycobacterium abscessus Complex Strains Isolated on Various Nutrient Media.

Mycobacterium abscessus complex (MABSc) causes chronic infection in patients with concomitant structural changes in the respiratory tract, which is especially important for patients with cystic fibrosis. To isolate an MABSc culture from clinical material, a variety of nutrient media are used. For species determination of microorganisms isolated on these media, additional identification methods are used, for example, polymerase chain reaction, sequencing, or mass spectrometry. The latter method is relatively easy to implement but requires improvement, due to the identification inaccuracy of nontuberculosis mycobacterias in general. Consequently, a set of nutrient media may be important for subsequent identification by mass spectrometry. The study was conducted on 64 strains of MABSc representatives: 56 strains were obtained from patients with cystic fibrosis and 8 strains from patients with pulmonary pathology unrelated to cystic fibrosis. The obtained MABSc strains were transplanted to the universal chromogenic medium and the selective medium for the Burkholderia cepacia complex (BCC) isolation. Species identification was carried out by mass spectrometry based on matrix-activated laser time-of-flight desorption/ionization (MALDI-ToF MS). Microbial identification is based on a comparison of the obtained mass spectra with reference spectra from the database. Microorganisms were identified based on the coincidence degree (Score value). Sample preparation for microbial identification by mass spectrometry was carried out by an extended direct application method. Fragments of the rpoB and hsp65 genes with lengths of 752 bp and 441 bp, respectively, were used as molecular markers for subspecific identification of MABSc strains. A comparison of the peaks obtained after mass spectrometry of MABSc strains isolated on the studied nutrient media showed significant differences between these indicators selective medium for the BCC isolation with the supplement of iron polymaltose hydroxide (III) and universal chromogenic medium (P < 0.001) and selective medium for the BCC isolation with universal chromogenic medium (P < 0.001). Twenty-five strains of MABSc representatives were sequenced: results of subspecies determination in strains isolated on the universal chromogenic medium coincided with the results sequencing in 13 (86.6%) strains out of 15. MALDI-ToF mass spectrometry allows microbial identification in a short time and with minimal cost, but it does not yet allow the proper identification of the subspecies of certain microbial groups, such as MABSc. Cultivation methods need optimization and new approaches to the extraction process of the bacterial protein fraction.