Abstract: Population aging is the most significant demographic transformation of the 21st century, reshaping health systems, economies, and societies. The biological processes of immunosenescence and inflammaging weaken host defenses, reduce vaccine effectiveness, and increase vulnerability to infectious and chronic diseases. These changes underscore the urgent need for preventive strategies that extend beyond childhood immunization. Vaccination is a cornerstone of healthy aging, capable of preventing infections and has been associated with reductions in systemic inflammation, frailty, and loss of functional independence in later life. Furthermore, new insights into vaccine-mediated immunomodulation, including trained immunity, adjuvanted formulations, and epigenetic reprogramming, highlight the evolving role of vaccines as modulators of immune fitness across the lifespan. This first part of our review examines the intersection of aging and immunity, as well as the potential of vaccines to address these challenges. Part 2 will expand on specific vaccines, proposed vaccination schedules, and global perspectives for lifelong immunization.

Abstract: Objective: To construct an interdisciplinary hypothesis framework connecting the Traditional Chinese Medicine (TCM) "Fire" theory with modern metabolic science, providing a biological and material basis explanation for understanding "physiological fire" and "pathological fire." Methods and Main Viewpoints: By systematically integrating classic TCM theory with findings from modern metabolic science, gut microbiota, and inflammation research, this paper proposes "Food Fire" as the core hypothesis. This hypothesis posits that the energy metabolic flux initiated by food intake is the key carrier of TCM "Fire." An orderly, controlled metabolic state corresponds to "physiological fire" (Shao Huo), which is the "vital qi" sustaining life. In contrast, metabolic overload caused by long-term excessive or imbalanced diets, triggering mitochondrial stress, chronic inflammation, and gut microbiota dysbiosis, corresponds to "pathological fire" (Zhuang Huo), manifesting as TCM syndromes like "Shanghuo" (upward flaming of fire) and "consumption of qi and injury to fluids." Results and Conclusion: Analysis using Type 2 Diabetes Mellitus (corresponding to TCM "Xiao Ke" or wasting-thirst syndrome) demonstrates that the "Food Fire" hypothesis effectively integrates TCM pathological mechanisms like "Zhuang Huo consuming qi" with metabolic disorders resulting from nutritional excess (e.g., insulin resistance, chronic inflammation) at both phenomenological and mechanistic levels. This study provides a dynamic and operable research framework for the modern interpretation of core TCM theories, potentially promoting substantive dialogue and collaboration between TCM and modern medicine in the field of metabolic diseases. This paper presents a conceptual hypothesis awaiting subsequent empirical validation.

Abstract: This second part of the expert panel review explores the broader impact of vaccines across the lifespan, emphasizing their role beyond immediate infection control. Vaccines not only extend life expectancy but also influence long-term health trajectories, particularly during the critical first 1000 days of life, where they prevent severe infections, disability, and mortality. They contribute to cancer prevention through immunization against human papillomavirus and hepatitis B, reduce cardiovascular events and complications in individuals with chronic diseases, and protect against late-life functional decline. Furthermore, vaccines exert lasting effects on healthy aging by modulating inflammation and preserving independence, while proposed immunization schemes illustrate the need for a comprehensive, life-course approach. Together with Part 1, which focused on immunosenescence and immune modulation, this closing installment underscores vaccination as a cornerstone of sustainable health policy, reinforcing its pivotal role in extending both lifespan and healthspan for future generations.

Abstract: Infrasound, physically defined as sound at frequencies below 20 Hertz, can travel long distances with minimal attenuation and permeate biological tissues due to its marked particle displacement and deep penetration. Generated by both natural phenomena and human-made systems, infrasound has drawn increasing scientific and public attention regarding its potential physiological and psychological effects. Experimental studies demonstrate that infrasound can modulate mechanosensitive structures at the cellular level, particularly pressure-sensitive ion channels such as PIEZO1 and TRPV4, leading to intracellular calcium influx, oxidative stress, altered intercellular communication, and in some settings, apoptosis. These responses vary according to sound pressure levels, frequencies, exposure duration and tissue type. In the cardiovascular system, higher sound pressures have been associated with mitochondrial injury and fibrosis, whereas low sound pressures may exert context-dependent protective effects. In animal models, prolonged or intense exposure to infrasound has been shown to induce neuroinflammatory responses and memory impairment. Short-term studies in humans at moderate intensities have reported minimal physiological changes, with psychological and contextual factors influencing symptom perception. Occupational environments such as factories and agricultural settings may contain elevated levels of infrasound, underscoring the importance of systematic measurements and exposure assessments. At the same time, controlled infrasound stimulation has shown potential as an adjunct modality in bone repair and tissue regeneration, highlighting its dual capacity as both a biological stressor and a possible therapeutic tool. Overall, existing data indicate that infrasound may be harmful at chronic exposure depending on intensity and frequency, yet beneficial when precisely regulated. Future research should standardize exposure metrics, refine measurement technologies, and clarify dose–response relationships to better define the health risks and therapeutic applications of infrasound.

Abstract: Background: Osteoarthritis, the most common musculoskeletal disorder primarily af-fects people in their mid-40s and older. As the disease progresses, degenerative chang-es occur in the synovial membrane, subchondral bone, and cartilage. Ultimately, the entire joint and its surrounding tissues become structurally and functionally impaired. Several sets of biochemical markers have been proposed to establish a timely diagnosis and anticipate progression. However, only a few of these are routinely used for disease monitoring. Methods: We conducted a prospective, single-center cohort study of 72 patients with knee osteoarthritis. Diagnosis was established on clinical data and radiological find-ings. We examined four cartilage metabolic regulatory factors: the Wnt/β-catenin sig-naling inhibitors serum DKK-1 and sclerostin, RANKL, and OPG, correlating these with disease activity and pain scores (WOMAC, VAS, and KOFUS), radiographic stage, inflammatory molecules and indices, and bone mineral density. Results: DKK-1 levels were higher in the intensive pain group (VAS >5) and were posi-tively correlated with the KOFUS flare-up score throughout the study. This correlation was stronger in individuals with a BMI < 30. Serum DKK-1 levels were higher in pa-tients with lower bone mineral density. No significant modifications in SOST, RANKL, or OPG levels were found. Conclusion: DKK-1 is an indicator of pain and low-grade flare-ups in patients with knee osteoarthritis (OA), being elevated in the early stages of the disease, associated with increasing pain intensity and impaired bone turnover. Early warnings of aug-mentation would be useful for more efficiently exploiting potential disease-modifying treatments.

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Artificial intelligence (AI), particularly generative AI, is rapidly reshaping medical education worldwide. While AI-enabled tools offer significant opportunities for personalized learning, feedback automation, and clinical reasoning support, they simultaneously challenge foundational principles of assessment integrity and professional conduct. Traditional assessment models—largely predicated on individual authorship, knowledge recall, and observable performance are increasingly strained by AI systems capable of generating sophisticated responses, analyses, and clinical narratives. This disruption has prompted urgent reconsideration of what constitutes academic honesty, valid assessment, and professional identity formation in contemporary medical training. This article critically examines the intersection of AI, assessment integrity, and professionalism in medical education from a global perspective, with particular attention to the experiences and emerging lessons from the Gulf Cooperation Council (GCC). The GCC provides a distinctive context characterized by rapid digital transformation, centralized accreditation and licensing systems, high-stakes assessments, and strong sociocultural norms governing professional behavior. These features make the region an instructive case for understanding how medical education systems respond to AI-driven challenges at scale. Drawing on international literature, policy documents, and regional practices, this paper argues that AI should be understood not merely as a technological tool but as a normative disruptor that compels a re-examination of assessment validity, ethical responsibility, and professional identity. The article proposes a shift from reactive prohibition toward principled integration of AI within assessment and professionalism frameworks. It concludes by outlining future-oriented recommendations for educators, institutions, and regulators aimed at preserving trust, fairness, and professional standards in an AI-augmented educational landscape.

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Background: Bone-seeking radiopharmaceuticals based on bisphosphonates enable targeted therapy of skeletal metastases. They are ideal carriers for therapeutic radionuclides such as Terbium-161 (¹⁶¹Tb), a β-emitter that additionally releases short-range conversion and Auger electrons, which may enhance radiation dose delivery to small lesions. This study aimed to investigate the potential of the specific DOTA conjugated bisphosphonate - BPAMD (4-{[(bis(phosphonomethyl))carbamoyl]methyl}-7,10-bis(carboxymethyl)-1,4,7,10 tetraazacyclododec-1-yl)acetic acid) radiolabeled with ¹⁶¹Tb as a next-generation of bone-targeted radiopharmaceutical. Methods: BPAMDwas radiolabeled with ¹⁶¹Tb and ¹⁷⁷Lu under mild conditions (pH 4.5, 95°C, 30min); subsequently the radiochemical purity was assessed by radio-TLC and radioelectrophoresis. Physicochemical properties (charge, lipophilicity, protein binding), in vitro stability (saline and human serum, 48h), and hydroxyapatite binding were evaluated for ¹⁶¹Tb-BPAMD. Biodistribution was studied in healthy Wistar rats (n=3 per time point) at 2h, 24h, and 7 days post-injection. Complementary DFT calculations explored the coordination chemistry of Tb³⁺ and Lu³⁺ with BPAMD. Results: Both complexes achieved >98% radiochemical yield. ¹⁶¹Tb-BPAMD exhibited negative charge, high hydrophilicity (logP = –3.92±0.13), low protein binding (19.07±1.01%), excellent radiochemical stability under simulated physiological conditions (>97% at 48h), and strong hydroxyapatite affinity (>98% with ≥10 mg HAP). Biodistribution showed high, stable bone uptake (8.06% ID/g at 2h; 6.70% ID/g at 24h; 5.31% ID/g at 7d) with rapid blood clearance (<0.001% ID/g at 24h), and low non-target retention. To contextualize its performance, ¹⁶¹Tb-BPAMD was compared with ¹⁷⁷Lu-BPAMD, which demonstrated similarly strong skeletal retention (8.74% ID/g at 2h; 8.08% ID/g at 24h; 5.25% ID/g at 7d) but comparatively higher non-target organ uptake. Complementary DFT calculations indicate that both Tb³⁺ and Lu³⁺ favor octa-coordinated BPAMD complexes. Conclusions: ¹⁶¹Tb-BPAMD exhibits excellent radiochemical and pharmacokinetic properties, with enhanced biodistribution selectivity over ¹⁷⁷Lu-BPAMD. Combined with the radiobiological advantages of ¹⁶¹Tb, it represents a promising theranostic candidate for targeted therapy of bone metastases.

Abstract: Knee osteoarthritis (KOA) is a progressive degenerative joint disorder characterized by cartilage loss, subchondral bone remodeling, and inflammation, ultimately leading to pain and impaired mobility. Early and accurate assessment of KOA severity is essential for personalized clinical management. Although the Kellgren–Lawrence (KL) grading system remains the radiographic gold standard, it is limited by subjectivity and inter-observer variability. This study evaluates a radiomics-based machine learning framework using Teachable Machine for automated KOA grading from knee X-ray images. Radiographs were categorized into KL Grades 0–4, and radiomic features capturing intensity and texture patterns were used to train models under varying epoch and batch-size configurations. The results demonstrate that model performance varied significantly across grades, with consistently higher accuracy for extreme grades (G0 and G4) and lower performance for early and intermediate grades (G1–G3), reflecting the subtle nature of mild KOA changes. Optimal performance was observed at moderate training durations (80–90 epochs) and larger batch sizes, while extended training (100 epochs) led to overfitting and reduced generalizability. Despite these challenges, the radiomics-based approach shows potential for objective and reproducible KOA severity assessment. This work highlights the feasibility of accessible ML platforms for supporting clinical decision-making and improving KOA diagnostic workflows.

Abstract: Background/ Objectives Patients with poor sleep are at high risk for developing diabetes (T2DM). Since T2DM is linked to increased risk of obstructive sleep apnea (OSA), and Metformin is commonly used to treat T2DM, we examined how Metformin affects sleep stages in patients with concurrent T2DM and OSA-related symptoms of snoring and fatigue. T2DM patients on Metformin, progressively develop increased insulin resistance associated with sleep disturbances, and poor glycemic control. We explored changes in sleep patterns in T2DM patients on Metformin. We explored if Metformin affects sleep Architecture in T2DM patients.MethodsEvaluate PSG (polysomnogram data from T2DM patients on Metformin along with data on age, BMI and sex. Data to be analyzed as mean +SE and linear regression, t test p<0.5 taken as significant. Results Non-obese patients taking Metformin experienced a significant decrease in REM duration compared to patients on alternative therapies (p = 0.036). There was no such change in REM for obese patients taking Metformin. While there was no change in N3 duration with Metformin use, linear regression identified a moderate negative correlation between N3 and age in patients taking non-Metformin therapies (R2 = 0.4555). No significant correlations between sleep stage duration and patient sex, smoking status, or body-mass index (BMI) were identified.Conclusion T2DM patients on Metformin had OSA with reduced deep sleep (N3) and REM (Rapid Eye Movement) sleep. These diabetic patients with OSA being treated with Metformin had decreased REM sleep, regardless of sex, smoking history, and BMI. N3 and REM sleep are needed for timely secretion of growth hormone and memory consolidation. Metformin affects sleep architecture and impacts N3 and REM sleep. This may contribute to the development of insulin resistance. Future studies are needed to explore potential causes for this decrease and how it may affect treatment of T2DM.

Abstract: Background/Objectives: Artificial intelligence (AI) tools for chest X-ray interpretation have gained relevance as support systems in diagnostic workflows, particularly in settings with high demand or limited specialist availability. This study evaluated the diagnostic performance of the qXR software (Qure.ai) for detecting high-risk pulmonary nodules, cardiomegaly, and pleural effusion in adult patients at Hospital Clínica Bíblica in San José, Costa Rica. Methods: Three radiologists independently interpreted 225 chest radiographs, serving as the reference standard. qXR results were compared against this standard for each finding. Sensitivity, specificity, Cohen’s kappa, and area under the curve (AUC) were calculated. Predictive values were not used for interpretation due to the artificial prevalence of the sample. Results: qXR showed higher agreement with radiologist assessments for pulmonary nodules and pleural effusion, achieving moderate to substantial concordance. Performance for cardiomegaly was more variable, with lower agreement across evaluators. Overall diagnostic accuracy was acceptable, although the magnitude differed by condition. Conclusions: These findings underscore the importance of validating AI diagnostic tools within local clinical environments and heterogeneous imaging conditions. qXR demonstrated potential as a complementary aid for detecting pulmonary nodules and pleural effusion, while its performance for cardiomegaly should be interpreted with caution. The study does not provide evidence of real-world clinical impact.

Abstract: Background: Breast cancer is one of the leading causes of female mortality, especially if diagnosed in late stages. While mammography is the cornerstone of screening, its diagnostic accuracy is limited by tumor heterogeneity and subjective interpretation. Objective: Herein, we explored the potential of radiomics and machine learning to improve the diagnostic accuracy of mammograms and personalise patient management in breast cancer. Methods: We manually segmented tumours and lymph nodes to analyse mammograms of the open-source INbreast dataset, which comprised multiple cases of benign and malignant breast masses with and without lymphadenopathy. Ra- diomics features (morphological, texture, wavelet) were extracted using PyRadiomics. Stratified sampling ensured balanced class representation. Then, we trained ML classifiers (XGBoost, CatBoost, LightGBM, etc.) to detect malignancy from the extracted radiomical features. Random Forest classifier was used to prognosticate the molecular subtype of the tumour from radiomical findings. Results: Significant radiomic differences were observed between benign and malignant lesions. Combining features of breast mass and lymph node yielded the highest classification accuracy (up to 99%) in detecting malignancy. The Random Forest model achieved 90.8% accuracy in identifying Luminal A molecular subtypes, with first-order and shape-based features contributing most to model perfor- mance. Conclusion: Radiomics-based ML models significantly improve diagnostic accuracy and enable non-invasive prediction of breast cancer subtypes. This approach supports precision oncology by enhancing screening efficiency and informing personalized treatment strategies.

Abstract: Background: West Nile Virus (WNV) is a mosquito-borne flavivirus responsible for seasonal outbreaks in temperate and tropical regions, including Europe, the Mediterranean, and the Middle East. Its transmission via mosquitoes, particularly Culex species, poses persistent challenges to public health. Despite ongoing efforts, comprehensive prevention and treatment strategies remain limited. Methods: A comprehensive search of peer-reviewed literature, clinical trials, and government surveillance data was conducted using PubMed, Scopus, Web of Science, and supplementary web-based resources. Inclusion criteria focused on molecular studies of WNV, vaccine and antiviral drug development, and regional outbreak reports. Results: WNV transmission is influenced by climatic conditions, vector distribution, and ecological patterns. While human vaccines are currently under development, only veterinary vaccines resulted promising but still partial insights. Remarkably, therapeutic measures are currently limited to supportive care, whereas investigational antiviral drugs are in early-stage trials. Interestingly, Italy demonstrates robust surveillance with regular reporting of outbreaks, whereas data from Iran indicate that despite a widespread serological footprint, especially in southern and southwestern provinces, but the reported clinical impact on humans and animals appears comparatively less severe. Conclusions: Bridging gaps in vaccine availability, therapeutic innovation, and disease monitoring is essential for effective WNV management to prepare for potential severe future outbreaks in Europe and the Middle East. Regional differences between Italy and Iran reveal the need for tailored public health interventions, enhanced surveillance, and sustained investment in research. In our view, collaborative frameworks across Mediterranean and Middle Eastern countries in a “One Health” approach may improve preparedness and response to future WNV outbreaks.

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Background/Objectives: Histological examination constitutes a fundamental methodology for establishing the vitality of a lesion. In cases where the corpse is preserved for an extended duration of time prior to the post-mortem evaluation, particularly if the body has undergone freezing and thawing cycles, postmortem changes may obscure or alter evidence of traumatic injuries. Consequently, the reliability of hematoxylin and eosin (H&E) staining for the reliable detection of intralesional erythrocytes in suspected traumatic fatalities is potentially severely compromised. The primary objective of this study is to rigorously underscore the detrimental influence of freeze-thaw processes on histologic examination and to advocate the indispensable incorporation of immunohistochemical analysis, specifically employing anti-human glycophorin A antibodies, to ascertain the presence of red blood cells. Methods: Skin samples from 10 autopsy cases were subjected to serial freeze-thaw cycles and analyzed using anti-human Glycophorin A (GPA) immunohistochemistry staining to evaluate skin lesion vitality in freeze-thawed tissues compared to fresh controls. Results: Results indicated that while H&E reliability was limited to fresh tissue, anti-GPA staining remained stable across all freeze-thaw cycles. Conclusions: Forensic pathologists must remain acutely cognizant of the potential artifacts produced by freeze-thaw cycles. In this cases anti-GPA staining proved to be a reliable asset to evaluate the vitality of a lesion.

Abstract: Laboratory-based gait analysis using motion capture and force plates remains the gold standard for quantifying ground reaction forces (GRFs) and temporal gait parameters. However, its high cost and limited accessibility restrict routine clinical use. Wearable smart insoles offer a portable alternative, yet require rigorous validation before clinical adoption. This study evaluates the clinical and technical validity of the SuraSole® smart insole, a low-cost pressure sensor–embedded insole, by comparing its GRF and temporal gait measurements with those obtained from a laboratory force plate and 3D motion capture system. Twenty healthy adults completed five walking trials while wearing standardized footwear equipped with SuraSole insoles, with simultaneous force plate and motion capture data collection. Agreement between systems was assessed using Bland–Altman plots and intraclass correlation coefficients (ICCs). SuraSole demonstrated excellent agreement with force plates for GRF across weight acceptance, mid-stance, and push-off (ICCs 0.97–0.99), with mean differences of 15.93 ± 45.90 N, 2.38 ± 23.98 N, and 8.64 ± 40.45 N, respectively. Temporal parameters showed moderate to good reliability (ICCs 0.62–0.81), with limitations likely related to the insole's 20 Hz sampling rate. These findings indicate that SuraSole provides reliable GRF measurement and acceptable portable gait assessment, supporting its potential for use in clinical practice, rehabilitation, and community health monitoring. Future hardware improvements, particularly higher sampling frequency, may enhance temporal accuracy.

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Background/Objectives: Bone age assessment is critical in pediatric endocrinology and forensic medicine. Although recently developed multimodal large language models (LLMs) show potential in medical imaging, their diagnostic performance in bone age determination has not been sufficiently evaluated. This study evaluates the performance of four multimodal LLMs (ChatGPT-5, Gemini 2.5 Pro, Grok-3, and Claude 4 Sonnet) in bone age determination using the Gilsanz-Ratib (GR) atlas. Methods: This retrospective study included 245 pediatric patients (109 male, 136 female) under age 18 who underwent left wrist radiography. Each model estimated bone age using the patient's radiograph and GR atlas as reference (atlas-assisted prompting). Bone age assessments made by an experienced radiologist using the GR atlas were evaluated as the reference standard. Performance was assessed using mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland-Altman analysis. Results: ChatGPT-5 demonstrated statistically superior performance with MAE of 1.46 years and ICC of 0.849, showing highest alignment with the reference standard. Gemini 2.5 Pro showed moderate performance with MAE of 2.24 years; Grok-3 (MAE: 3.14 years) and Claude 4 Sonnet (MAE: 4.29 years) had error rates too high for clinical use. Conclusions: Significant performance differences exist among multimodal LLMs despite atlas-supported prompting. Only ChatGPT-5 qualified as "clinically useful," demonstrating potential as an auxiliary tool or educational support under expert supervision. Other models' reliability remains insufficient.

Abstract: The diagnostic and therapeutic approaches of many human pathologies have been substantially modified by the advent of extraordinary progress in the field of molecular and cellular biology. The future of Nuclear Medicine is therefore an effective use of radioactivity, which allows us to get more and more into the mechanisms that generate and maintain the disease and eliminate them or, if possible, to correct them. We can therefore affirm that lymphoscintigraphy still maintains its solid place in the diagnosis of lymphatic flow disorders in this millennium. A position that, even compared to other methods, allows it to be used in diagnosis, prevention, and the monitoring of therapies, be they surgical, pharmacological, or physical.

Abstract:

Objectives: This study aimed to identify predictors of absence versus presence of alteration in system status to inform targeted interventions. Methods: In a cross-sectional analysis of 2495 patients (70.1% women) from Bogotá’s public health facilities (Colombia Open Data, 2023), associations were examined between sociodemographic factors (gender, age groups, education, ethnicity) and clinical variables (BMI, disability type, COVID-19 vaccination, psychiatric risk, dyspnea scale) with health outcomes. Chi-square tests identified bivariate associations, and multivariable logistic regression predicted absence of alteration (reference: presence of alteration), reporting odds ratios (ORs), 95% confidence intervals (CIs), and model fit indicators (deviance, AIC, McFadden’s R²). Outliers were removed using z-scores; significance was set at p<0.05. Results: Women predominated in obesity (81% vs. 19% of men, p<0.001) and in health statuses without alteration but showed higher disability prevalence (16% vs. 6% in men, p<0.001). Men exhibited more altered statuses (e.g., pulmonary: 53.8% vs. 46.2%, p=0.006) and mental disabilities (70%, p<0.001). Underweight and obesity reduced odds of pulmonary alteration (OR=0.08 each, p<0.02) compared with overweight, whereas obesity decreased odds of absence of neurological alteration (OR=0.04, p=0.011). Absence of disability strongly favored absence of alteration (neurological OR=76.95, p<0.001). Lack of education increased odds of mental alteration (OR=2.67, p=0.006). Models showed moderate to excellent fit (R²=0.25–0.72). Conclusions: Gender, BMI, disability, age, and education are key predictors in NCD-related system alterations. Interventions such as BMI management and education strategies may reduce disparities and support WHO 2025 targets. Longitudinal research is recommended to strengthen causal interpretations.

Abstract: Hop (Humulus lupulus L.) is recognised as a valuable source of bioactive compounds; however, the phytochemical composition and biological potential of wild Romanian hops remain insufficiently characterized. In this study, the bioactive profile of wild hop cones was evaluated using an integrated phytochemical, biological, and in silico ap-proach. The hydroethanolic extract was characterized by a total phenolic content of 25.61 mg GAE/g DW and a total flavonoid content of 3.20 mg RE/g DW, with α-acids predominating (8.77%) and β-acids detected only at trace levels (0.15%). Hydrodistil-lation yielded 0.613 ± 0.11% essential oil, which was rich in sesquiterpene hydrocar-bons (64.61%), mainly α-humulene, β-caryophyllene oxide, selina-3,7-diene, and ger-macrene B. The hydroethanolic extract exhibited strong antioxidant activity (IC₅₀ = 5.03 µg GAE/mL), whereas the essential oil showed a moderate but dose-dependent radi-cal-scavenging capacity (IC₅₀ = 0.44% v/v). In addition, the essential oil displayed pronounced antibacterial and antibiofilm activity against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, with the highest antibiofilm inhibition observed for Pseudomonas aeruginosa (96.44%). Molecular docking analysis suggested that the major volatile constituents may interact with S. aureus Sortase A, providing a plausible mechanistic basis for the observed antibiofilm effects. Overall, these findings indicate that wild Romanian hop cones represent a promising source of antioxidant and anti-microbial bioactive compounds, supporting their potential applications in pharmaceu-tical, food, and cosmetic formulations, as well as in natural product–based drug discovery.

Abstract: The WHOQOL-BREF questionnaire is widely used globally in research that requires the assessment of quality of life. Its official status, broad availability in multiple languages, and the fact that it is free to use are the primary reasons it is selected by researchers and clinicians. However, the quality of particular language versions and the lack of standardization raise concerns about the quality of data collected in different countries and thus their comparability. In this study, 17 country versions published on the WHO website were analyzed. Each translation was compared to the model English questionnaire in terms of visual layout, inclusion of instructions for respondents, timeframe of measurement, and additional elements. Moreover, selected versions were reviewed by native speakers and with use of LLM to assess the accuracy of the question and scale translations. The results of the analysis revealed significant discrepancies between translations in all analyzed fields. Some translation errors were so severe that responses to certain questions could not be meaningfully compared. The study concludes that the WHOQOL-BREF questionnaire translations should be reviewed across all evaluated elements to ensure its consistency and comparability. Additionally, specific language versions developed by various academic institutions should be closely scrutinized by the WHO team. A common practice of adding a note that a particular version is not an official WHO translation will not suffice, as these tools are treated de facto as officially approved by researchers and medical specialists.

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Background/Objectives: Sarcopenia, obesity, and sarcopenic obesity (SO) are common in older adults and may be associated with functional limitations in basic (ADL) and instrumental (IADL) activities of daily living. This study aimed to evaluate the association between body composition phenotypes and ADL/IADL limitations among older adults. Methods: A cross-sectional study included 440 community-dwelling adults aged ≥60 years (281 women, 159 men; mean age 74.7 ± 7.8 years). Sarcopenia was diagnosed according to EWGSOP2 criteria, obesity was defined as percent body fat >42% in women and >30% in men, and SO was classified based on the ESPEN/EASO recommendations. Participants without obesity or sarcopenia were categorized as ‘normal’ phenotype. Functional status was evaluated using the Katz and Lawton scales, with limitations defined as ADL ≤5 and IADL ≤26 points, respectively. Multivariate logistic regression analysis was performed to determine factors associated with ADL and IADL limitations. Results: Over half of the participants (57.1%) had abnormal body composition: 31.6% obesity, 11.4% sarcopenia, and 13.2% SO. SO was associated with a nearly threefold higher risk of ADL limitations (OR = 2.86; p = 0.003) and a 3.7-fold higher risk of IADL limitations (OR = 3.68; p < 0.001) compared to the normal phenotype. Sarcopenia was associated with IADL limitations in the unadjusted model (OR = 2.44; p = 0.010). Independent predictors of ADL and IADL limitations included reduced muscle strength, a higher number of chronic diseases, and a worse nutritional status. Conclusions SO was linked to higher risk of both ADL and IADL limitations, while sarcopenia was associated only with IADL deficits. Obesity severity may be relevant, but its impact on daily functioning in older adults requires further study.