Background The role of pharmacists in managing urinary tract infections (UTIs) is crucial, yet there is no instrument to assess their attitudes and practices in this area. The study aimed to develop and initially validate a questionnaire to evaluate pharmacists' attitudes and practices concerning patient counselling for UTIs, with the ultimate goal of supporting improvements in pharmacy practice and enhancing the quality of patient care. Methods The questionnaire was developed and initially validated (content and face) through a multi-phase mixed-methods approach consisting of: 1) initial item generation applying a comprehensive literature review, 2) first expert panel discussion, 3) content and cultural validation by pharmacists (focus group discussion), 4) second expert panel discussion, and 5) pretesting by the target population. The necessity, relevance, and clarity were assessed by calculating the Content Validity Ratio (CVR), Item-Level Content Validity Index (I-CVI), and Scale-Level Content Validity Index (S-CVI/Ave). Qualitative data was analyzed using an ethnographic content analysis. Results The initial questionnaire consisted of 33 items, divided into two domains: pharmaceutical practice and attitudes. After phases 2–4, all items were rated with satisfactory CVR and I-CVI values (over 0.99 and 0.83, respectively). The final phase of content validation resulted in the questionnaire final version of 25 items with S-CVI/Ave = 0.98 for relevance and S-CVI/Ave = 1 for clarity. Internal consistency analysis demonstrated high reliability for the attitudes toward antibiotics subscale (Cronbach's α = 0.850) and acceptable reliability for the attitudes toward herbal products subscale (Cronbach's α = 0.735). Conclusions The developed questionnaire is concise, easy to use and has satisfactory content and face validity. The developed questionnaire can be used to assess pharmacists' practices and attitudes in counselling patients with UTI symptoms, contributing to the identification of areas for improvement in pharmacy practice and patient safety.

The 3ω technique is a prominent thermal conductivity measurement methodology for thin films, substrates, nanowires, and thermal boundary conductance. The extraction of the thermal conductivity typically relies on measuring the thermal response across a wide range of frequencies and determining the slope within acceptable limiting conditions, which can be a time-consuming process prone to error from the amplification of noise when taking the derivative of discrete temperature data to determine thermal conductivity. Here, we develop and demonstrate a frequency-modulated 3ω method (FM-3ω) with which we directly measure the derivative of the 3ω signal by varying the center frequency ω, eliminating the need to postprocess the data, thereby reducing the time to take such measurements from hours to minutes. Our modulation approach is a frequency modulation method in which the frequency ω of the excitation current is sinusoidally varied over time. We show that our new method produces results with similar accuracy to the traditional method on bulk sapphire and borofloat 33 samples, and we further explore the limitations of modulation depth and center frequency on the results. We find that thermal conductivity measurements from the FM-3ω method agree well with thermal conductivities extracted through linear fits to temperature data over similar frequency windows of the traditional method. Our method provides a new strategy using frequency modulation and tandem demodulation to directly measure the derivative of temperature, thus contributing to the advancement of thermal transport sciences by increasing the ease and pace of measuring the thermal conductivity of thin films and multilayer structures.

Perfluorohexyloctane (F6H8) is a semifluorinated alkane recently approved for ophthalmic treatment of dry eye disease. Although considered locally safe for topical use, its structural similarity to persistent per- and polyfluoroalkyl substances (PFAS) raises concerns about systemic accumulation and long-term toxicity. To investigate potential hepatic effects, we examined the metabolic impact of F6H8 exposure in human HepaRG hepatocytes across a broad concentration range representing short- and long-term exposure scenarios. Combined targeted and untargeted metabolic profiling by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS) was performed on intracellular extracts and extracellular media. F6H8 induced pronounced, concentration-dependent metabolic alterations, many of which exhibited non-monotonic responses. Low concentrations primarily affected amino acid, fatty acid, and lipid metabolism, while central carbon metabolism was disrupted only at the highest exposures. Notably, a putative biotransformation product, perfluorohexyloctanoic acid, was detected, suggesting metabolic persistence and conversion to a PFAS-like structure. This metabolite showed strong associations with cellular metabolic profiles and elicited metabolic changes that only partially overlapped with those induced by the parent compound, indicating distinct biological activity following biotransformation. These findings indicate that F6H8 elicits broad metabolic reprogramming and may not be metabolically inert as previously assumed. Given its clinical use and structural similarity to persistent fluorochemicals, the results highlight the need for comprehensive, long-term safety assessment of F6H8 and related semifluorinated alkanes.

ABSTRACT Background Early childhood and education centres (ECECs) are key settings in the promotion of healthy levels of outdoor play and napping among young children. Aim This study aimed to examine the associations between environmental factors and preschoolers' outdoor play and napping in ECECs across an international sample. Methods Data from 187 ECECs in 27 countries (22 low‐ and middle–income countries) that participated in the third pilot phase (January 2021–April 2025) of the SUNRISE International Study were analysed. The director of each ECEC completed a questionnaire which asked if children participating in the SUNRISE Study were unable to participate in outdoor play and nap time due to a range of environmental barriers. Results Forty‐six percent (n = 86) of ECECs reported at least one environmental factor that prevented preschoolers' outdoor play, and 20% (n = 37) reported at least one factor that disrupted naptime. Hot and cold temperatures, rain and other factors were observed as barriers to outdoor play across regions and country income levels. Indoor noise, extreme temperatures, brightness and lack of space were reported as disrupting preschoolers' naptime across regions and country income levels. For rural ECECs, hot temperatures and lack of space were barriers for outdoor play and napping, respectively. Conclusions Context‐specific strategies are required to create climate‐resilient outdoor play spaces and more restful napping environments to optimise early childhood development within ECECs.

Background Dilatation of the common bile duct (CBD) after cholecystectomy is frequently observed during follow-up imaging; however, its extent and clinical implications remain incompletely defined. Distinguishing physiological postoperative ductal enlargement from pathological dilatation is essential to avoid unnecessary diagnostic evaluation. This study aimed to compare CBD diameter in post-cholecystectomy patients with non-operated controls and to assess its association with time since surgery, age, and body mass index (BMI). Materials and methods This retrospective observational study included 165 adult patients who underwent abdominal ultrasound examination, comprising 91 post-cholecystectomy patients and 74 controls with an intact gallbladder. The CBD diameter was measured in the suprahilar segment. Group differences were evaluated using independent t-tests and chi-square tests. Logistic and linear regression analyses were used to assess predictors of CBD dilatation and continuous diameter change. All multivariable models were adjusted for age, sex, and BMI. Results CBD diameter was significantly greater in post-cholecystectomy patients compared with controls (6.61 mm vs. 4.56 mm; p < 0.001). Dilatation ≥7 mm occurred in 38.5% of post-cholecystectomy patients versus 5.4% of controls (p < 0.001), and prior cholecystectomy remained a strong independent predictor of dilatation after adjustment (aOR = 14.583; 95% CI: 4.449-47.807). Using a fixed ≥7 mm cutoff, increasing age was associated with lower odds of categorical CBD dilatation, whereas sex and BMI were not significant predictors. Linear regression analyses demonstrated a significant positive association between CBD diameter and both time elapsed since surgery and age, indicating gradual ductal enlargement over time. Marked dilatation (>10 mm) was uncommon and did not reach statistical significance in relation to cholecystectomy. Conclusion Cholecystectomy is associated with measurable and progressive enlargement of the CBD. While CBD diameter increases gradually with advancing age and postoperative duration, categorical dilation thresholds are more strongly influenced by surgical status than by age alone. Recognition of this expected postoperative anatomical pattern may help clinicians avoid unnecessary imaging and interventions in asymptomatic patients.

BACKGROUND One Class Modelling (CM) is popular among chemometricians, but not well known among omics scientists in general. One issue is that typical CM approaches, including SIMCA, often result in unsatisfactory results due to e.g. large variation, centring and scaling issues, sparsity, outliers, and non-linearities in typical omics data. These effects can cause an inflated decision boundary (of the target class), thereby returning many false positives (of non-target cases). Tree-based techniques are by nature resistant to these challenges. In this study we explore tree-Based SIMCA variants in omics scenarios and compare to existing strategies. RESULTS We present a non-linear form of SIMCA by making use of sample proximities obtained through Unsupervised Random Forest and Isolation Forest (termed URF-SIMCA and IF-SIMCA). We compare accuracy of the algorithms with (traditional) SIMCA, one-class support vector machines, and isolation forest. This comparison was based on five (previously published) clinical omics datasets and the wine-dataset. URF-SIMCA showed superior behaviour. Using the pseudo-sampling principles, an interpretation could be made on the important features for the separation between the target and non-target classes. Using the wine-dataset, we empirically show that these directly relate to information obtained through two-class algorithms. Moreover, feature trajectories in the score- and orthogonal distance spaces further enable interpretability of the model. SIGNIFICANCE URF-SIMCA offers an easy to use extension of SIMCA, which deflates the variance of the target class, allowing for better separation. The increased modelling performance comes at the cost of feature interpretation, but this can be tackled using the pseudo-sampling principle.

Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disorder that typically affects young adults and is primarily characterized by demyelinating lesions in the central nervous system (CNS). According to the Revised McDonald Criteria, the clinical diagnosis of MS can be established based on a combination of clinical observations, the presence of focal lesions in at least two distinct CNS areas on magnetic resonance imaging (MRI) and the detection of specific oligoclonal bands in the cerebrospinal fluid. Conventional MRI remains a cornerstone of MS diagnosis and disease monitoring, providing high-resolution assessments of lesion burden and brain atrophy. In addition, advanced MRI methods are increasingly applied in research settings to probe myelin integrity, iron deposition, and biochemical changes, with the potential to complement established diagnostic workflows in the future. Despite remarkable advances in the management of MS over the past two decades, complex differential diagnoses and the lack of effective imaging tools for therapy monitoring remain major obstacles, thus channeling the development of innovative molecular imaging probes that can be harnessed in clinical practice. Indeed, positron emission tomography (PET) has a significant potential to advance the contemporary diagnosis and management of MS. Given the solid body of evidence implicating myelin dysfunction in the pathophysiology of MS, myelin-targeted imaging probes have been developed, and are currently under clinical evaluation for MS diagnosis and therapy monitoring. In parallel, ligands for the 18 kDa translocator protein (TSPO) and the cannabinoid receptor type 2 (CB2R) have been employed to capture neuroinflammatory processes by visualizing microglial activation, while other tracers allow the assessment of synaptic integrity across various disease stages of MS. Further, PET probes have been employed to delineate the role of activated microglia and facilitate the assessment of synaptic dysfunction across all disease stages of MS. This review discusses the challenges and opportunities of translational molecular imaging by highlighting key molecular concepts that are currently leveraged for diagnostic imaging, patient stratification, therapy monitoring and drug development in MS. Moreover, we shed light on potential future developments that hold promise to advance our understanding of MS pathophysiology, with the ultimate goal to provide the best possible patient care for every individual MS patient.

Type 1 conventional dendritic cells (cDC1s) acquire and cross-present tumor antigens to prime CD8⁺ T cells. Whether this selects for specific neoantigens is unclear. DNGR-1 (CLEC9A), a cDC1 receptor for F-actin exposed on dead cells, promotes cross-presentation of cell-associated antigens. Here we show that DNGR-1-deficient mice develop chemically induced tumors more rapidly and at higher incidence, and these are more frequently rejected on transplantation into wild-type recipients. Whole-exome sequencing reveals enrichment of predicted neoantigens derived from mutated F-actin-binding proteins. Consistent with this observation, tethering model antigens to F-actin enhances DNGR-1-dependent cross-presentation. These results suggest that DNGR-1-mediated recognition of F-actin exposed by dead cancer cells favors priming of CD8⁺ T cells specific for cytoskeletal neoantigens, which can then drive immune escape of cancer cells lacking or reverting those mutations. Thus, neoantigen cross-presentation by cDC1 can determine the immune visibility of the tumor mutational landscape and sculpt cancer evolution by immunoediting. Here the authors show DNGR-1 expressed by cDC1s promotes CD8⁺ T cell priming to cytoskeletal neoantigens from dying tumor cells, thereby shaping cancer immune visibility and tumor evolution through immunoediting.

Abstract In this study, the possibility of using slag derived from hydrogen-plasma reduction of red mud (H2RMS) as a low-cost adsorbent for phosphate removal from aqueous solutions was investigated. Batch adsorption experiments were conducted to evaluate the effects of contact time, solution pH, sorbent dosage, and initial phosphate concentration under controlled laboratory conditions. Phosphate concentrations were determined spectrophotometrically using the ammonium molybdate method. These results demonstrated that phosphate adsorption onto H2RMS is strongly pH-dependent, with maximum removal efficiency achieved under acidic conditions (pH ≈ 2). Adsorption equilibrium was achieved after approximately 18 h of contact time. Increasing the sorbent dosage enhanced phosphate removal efficiency, although improvements became marginal beyond a dosage of 10 g/L. At optimal conditions, phosphate removal efficiency of approximately 90% was achieved. These findings indicate that H2RMS shows significant potential as an effective adsorbent for phosphate removal, offering a possible pathway for the valorization of metallurgical waste residues.

This study explores the transformative role of artificial intelligence (AI) in reshaping digital diplomacy, public relations, and security dynamics across the Middle East and North Africa (MENA) region. By integrating AI-driven analytics with social media monitoring, the research emphasizes how machine learning and algorithmic tools redefine information dissemination mechanisms, influence political narratives, and enhance cybersecurity frameworks. The study employs a mixed-methods approach, combining qualitative analysis of digital communication patterns with quantitative data on user perceptions of online security, surveillance, and self-censorship. The findings reveal that AI-enabled technologies-such as automated content moderation, sentiment analysis, and predictive modeling-serve as double-edged instruments: while they empower governments and institutions to counter disinformation, manage crises, and engage global audiences, they also raise concerns about algorithmic bias, digital surveillance, and privacy violations. In the MENA context, AI facilitates both strategic narrative control and participatory engagement, reflecting the tension between innovation and constraint in authoritarian environments. The research highlights that over 68% of surveyed users expressed fear of surveillance, and over 70% practiced self-censorship, illustrating the pervasive impact of AI monitoring on civic discourse. Ultimately, the study concludes that the future of digital diplomacy in MENA depends on adopting AI-driven but ethically governed communication strategies-balancing security imperatives with transparency, inclusivity, and digital rights. This work contributes to the emerging scholarship on AI in international communication, proposing a framework for responsible AI integration that protects user autonomy while strengthening national and regional stability.