Search Results (249)

Background: Subclinical psychological symptoms—such as low mood, perceived stress, and poor sleep—affect a large portion of the population and can impair quality of life despite remaining below clinical thresholds. The gut–brain axis has emerged as a promising target for interventions that support emotional and psychological resilience. Probiotics and postbiotics are gaining attention for their potential to modulate mood and stress via microbiome-related mechanisms, but human evidence remains limited, particularly in non-clinical populations. Objectives: We aimed to assess the effects of a two-strain combination of live microorganisms alongside a two-strain combination of heat-treated inactivated microorganisms on outcomes associated with anxiety, mood, perceived stress, and quality of life in healthy adults experiencing mild stress. Methods: This study was conducted in two parts. In Part I, a randomized, double-blind, placebo-controlled study, 100 participants were randomized to receive either a blend of live microorganisms (Bifidobacterium longum CECT 7347 and Lactobacillus rhamnosus CECT 8361) or an identical placebo once daily for 12 weeks. In Part II, a pilot feasibility study, a subset of eight placebo non-responders from Part I received the heat-inactivated preparation of the same bacterial strains in a 6-week trial extension phase. For Parts I and II, the primary outcome was the change in the Hamilton Anxiety Rating Scale (HAM-A). Secondary outcomes included measures of mood (Beck Depression Inventory (BDI); Patient Health Questionnaire-9 (PHQ-9)), stress (state and trait anxiety inventory (STAI); Perceived Stress Scale (PSS)), sleep (Pittsburgh Sleep Quality Index (PSQI)), quality of life (36-item Short Form Survey (SF-36)), gastrointestinal symptoms (Gastrointestinal Symptom Rating Scale (GSRS)), salivary cortisol and microbiome modulation. Results: In Part I, there were no significant effects of the live blend on the HAM-A, indicating that the primary endpoint was not met. In addition, no significant effects were seen on the STAI or PSS scores when compared to the placebo. However, participants consuming the live blend trended toward a reduction in total PHQ-9 scores compared to placebo (p = 0.089), whilst preliminary exploratory analyses suggested an improvement in anhedonia (p = 0.045). Furthermore, there was a significant improvement in the vitality domain of the SF-36 compared to placebo (p = 0.017). On microbiome analysis, it was noted that consumption of the live blend was linked to the preservation of butyrate-producing bacteria, particularly members of the Pseudoflavonifractor genus and the Clostridium SGB6179 species. Furthermore, the abundance of B. longum species was found to be inversely associated with the total PSS Scores. In Part II, supplementation with the inactivated preparation resulted in significant within-group improvements for the vitality (p = 0.006) and social functioning (p = 0.010) domains of the SF-36 and improvements in PSS scores compared to baseline (p = 0.050). Conclusions: Supplementation with either the dual-strain live or inactivated formulations was associated with significant improvements in the vitality domain of the SF-36, whilst participants receiving the inactivated formulation demonstrated lower perceived stress and improved social functioning compared to baseline. Overall, the findings from this pilot study suggest that these two biotic consortia are well-tolerated and may be associated with improvements in measures of vitality in individuals with subclinical psychological symptoms. The subtle observations detected for stress and anhedonia suggest that further well-powered trials are needed to better characterize these findings, potentially in populations with greater baseline symptomatology. Full article

Coffee pulp, a by-product of coffee processing and a rich source of bioactive compounds, is a promising raw material for functional beverages. However, the influence of genetic variability among coffee varieties on the functional and sensory properties of pulp infusions remains poorly understood. This study evaluated physicochemical, antioxidant, and sensory properties of infusions from nine Coffea arabica L. varieties. Significant differences among varieties were observed (p < 0.05). The pH ranged from 5.36 to 6.42, and titratable acidity from 0.06 to 0.08 g/100 mL, indicating a mild acidic profile. Antioxidant activity (DPPH) ranged from 460.52 to 1006.03 µmol TE/L, and total phenolic content from 29.47 to 59.27 mg GAE/L. Geisha showed the highest antioxidant activity and phenolic content, while Casiopea exhibited the highest reducing capacity. In contrast, Oro Azteca, Excelencia, and H1 achieved the highest sensory acceptance. Multivariate analysis confirmed clear differentiation among varieties and a separation between bioactive and sensory-related attributes. These findings highlight the role of varietal selection in balancing functional potential and consumer acceptance, supporting the development of functional beverages within a circular economy framework. Full article

Background: The recent U.S. Food and Drug Administration authorization of AirPods Pro as over-the-counter hearing aids (HAs) has increased interest in consumer devices as potential alternatives to traditional amplification; however, their electroacoustic performance relative to clinically fitted HAs remains unclear. The purpose of this study was to compare the electroacoustic characteristics and real-ear measures of AirPods Pro 2nd generation (APP2), AirPods Pro 3rd generation (APP3), and a traditional receiver-in-the-canal HA across mild flat, mild-to-moderate sloping, and moderate flat hearing loss configurations. Methods: Outcome measures included 2cc coupler output curves, saturation sound pressure level for a 90 dB input (SSPL90), real-ear speech mapping, maximum power output (MPO), and real-ear-to-coupler differences. Results: Coupler-based electroacoustic measures showed that APP2 and APP3 produced output comparable to the traditional HA (within 7 dB). SSPL90 outputs were similar for APP2 and APP3, whereas the HA demonstrated profile-dependent increases. In contrast, real-ear measurements demonstrated that both APP2 and APP3 consistently produced less output relative to the HA that was fitted to NAL-NL2 targets, with the largest deviations observed for moderate hearing loss and at higher frequencies (up to 14 dB). Across all configurations, MPO was consistently highest for the HA, with both AirPods devices exhibiting reduced maximum output, especially in speech-critical frequency regions. Real-ear-to-coupler difference findings indicated reduced acoustic coupling for APP3 relative to APP2 and the HA, contributing to reduced in-ear amplification despite comparable coupler outputs. Conclusions: While AirPods Pro may offer benefit for mild hearing loss or moderate high-frequency hearing loss, they do not provide output comparable to prescriptively fitted HAs. These findings underscore the continued importance of clinical verification and prescription-based fitting of hearing assistive technology for achieving appropriate audibility across hearing loss configurations. Full article

Moroccan lben is a traditional spontaneously fermented milk widely consumed across the Maghreb. In this review, we synthesize data on spontaneously fermented milks from Morocco and the wider Maghreb–Middle Eastern region to infer the likely microbiota of Moroccan lben, with particular emphasis on dominant lactic acid bacteria such as Lactococcus lactis, Streptococcus thermophilus, Leuconostoc mesenteroides and lactobacilli sensu lato, alongside yeasts including Kluyveromyces marxianus and Saccharomyces cerevisiae. These communities drive a staged fermentation in which early mesophilic lactic acid bacteria (LAB) rapidly acidify the milk and initiate coagulation, intermediate heterofermentative LAB and yeasts generate key aroma compounds and mild effervescence, and late acid-tolerant lactobacilli contribute to flavor refinement and microbiological stability. We summarize how these bacteria and fungi collectively shape physicochemical, sensory and safety attributes through pH reduction, organic acid and bacteriocin production, proteolysis, and volatile formation, and discuss potential nutritional and health-related effects associated with bioactive peptides and putative probiotic strains. Finally, we identify major research gaps, including the need for high-resolution, culture-dependent and culture-independent studies, systematic safety assessments, and rational design of starter and adjunct cultures that reproduce traditional sensory profiles while improving process control. Full article

Mild fasting hypertriglyceridemia is often accompanied by early insulin resistance and atherogenic dyslipidemia, making it an attractive target for pragmatic dietary prevention. This trial aims to determine whether substituting common cereal-based staples with functional low-glycemic index (low-GI) products improves the triglyceride–glucose (TyG) index in adults with fasting triglycerides >150 mg/dL. The GLOW study is an exploratory, randomized, double-blind, single-center crossover trial. Adults aged ≥18 years with fasting triglycerides >150 mg/dL and body mass index ≤30 kg/m² will be enrolled. Participants will follow a stabilized Mediterranean-style diet and will complete two 28-day intervention periods in random sequence: (i) functional low-GI Altograno^® pasta, pizza base and flatbread; and (ii) conventional standard wholegrain products. Intervention periods will be separated by a 28-day washout. Study foods will be consumed as fixed daily substitutions of usual staple servings (one bread portion and one pasta or pizza portion). The primary endpoint is the between-intervention difference in TyG response over each period, defined as the period-specific change from the corresponding period baseline to the end-of-period assessment. The primary analysis will compare end-of-period TyG between interventions while adjusting for the period-specific baseline value. Secondary endpoints include fasting triglycerides and glucose, atherogenic lipoproteins (non–high-density lipoprotein cholesterol and apolipoprotein B), inflammation (high-sensitivity C-reactive protein), endothelial reactivity assessed with the Endocheck^®/Vicorder^® system, and food acceptability. Safety endpoints include adverse event recording. Treatment effects will be estimated using linear mixed-effects models accounting for treatment, period and sequence, with prespecified carryover sensitivity analyses. A total of 40 participants will be recruited to generate feasibility data and effect size estimates. This protocol will provide crossover evidence on whether pragmatic, product-level low-GI staple substitution improves TyG and related cardiometabolic and vascular biomarkers in adults with suboptimal triglyceridemia, informing larger trials. Trial registration: ClinicalTrials.gov NCT07198789. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

Microfluidics-based preparation methods for cell-laden hydrogel microspheres are well-suited for large-scale comparative analysis of single or few cells. However, in existing studies, the preparation of cell-laden hydrogel microspheres and the cell culture process are typically separated, requiring the fabricated microspheres to be eluted and transferred from the preparation device to cell culture dishes or plates for cultivation. This transfer process can easily compromise sterility, while conventional cell culture methods consume more reagents and cause microsphere stacking, hindering single-cell observation and analysis. To address these issues, this paper presents an integrated microfluidic chip that sequentially enables droplet generation with cell encapsulation, gel droplet solidification, hydrogel microsphere trapping, and microsphere-based cell culture and analysis, facilitating the cultivation and observation of single or small numbers of cells. Integrating cell-laden microsphere preparation and 3D cell culture within a sealed chip structure reduces contamination risks associated with cell transfer, enables automation of multiple cell analysis workflows, and minimizes reagent and sample consumption. Using polydimethylsiloxane (PDMS) with good gas permeability and processability as the chip material, biocompatible fluorinated oil was selected as the oil phase for microsphere preparation. A mild sodium alginate-calcium ion gelation system was employed, where calcium ions were released under acidic conditions after droplet generation to trigger solidification, yielding uniform hydrogel microspheres. Under optimized conditions, the single-cell encapsulation efficiency for test samples of human myeloid leukemia cells (K562) was 33.8% ± 1.8%, with a size uniformity coefficient of variation (CV) reaching 3.85%. Cells encapsulated within hydrogel microspheres were cultured in 286 on-chip independent cell culture chambers, achieving >95% viability after 24 h. Full article

Background: Young children requiring neurosurgical care frequently undergo repeated neuroimaging. Whereas CT involves exposure to ionizing radiation, conventional MRI is time-consuming and often necessitates sedation in non-cooperative children. To address these limitations, ultrafast cranial MRI (UF-MRI) based on T2-HASTE sequences was implemented at our institution in 2019 for selected indications. The aim of this study was to evaluate the real-world implementation of UF-MRI in children younger than six years of age. Methods: We retrospectively analyzed cranial MRI examinations consisting exclusively of ultrafast sequences performed between July 2019 and December 2024 in children younger than six years. Clinical settings, diagnostic adequacy, immediate consequences for patient management, and the impact on MRI and CT utilization were systematically assessed. Results: A total of 404 UF-MRI examinations were performed in 198 inpatients and outpatients (mean age: 2 years 2 months) without the need for dedicated anesthesia team support solely for imaging. Only one examination (0.2%) required same-day repetition after mild oral sedation. In 20 patients (5.0%), UF-MRI was supplemented by conventional MRI under anesthesia, most commonly for preoperative planning. Immediate clinical consequences included no change in management in 54.5% of examinations, early follow-up in 22.8%, shunt valve adjustment in 11.6%, neurosurgical intervention in 7.7%, and other measures in 5.0%. UF-MRI accounted for 24.5% of all cranial MRI examinations in this age group and was associated with a 41% reduction in CT utilization compared with the corresponding period prior to UF-MRI implementation. Conclusions: In routine clinical practice, UF-MRI provides rapid, clinically sufficient neuroimaging in young children without the need for sedation or exposure to ionizing radiation. Its implementation significantly streamlines imaging workflows, optimizes resources utilization, reduces the need for CT, and supports timely clinical decision-making, underscoring its value as a complementary imaging modality in pediatric neuroimaging. Full article

Medical images obtained from advanced imaging devices play a crucial role in supporting disease diagnosis and detection. Nevertheless, acquiring such images is often costly and storage-intensive, and it is time-consuming to diagnose individuals. The use of artificial intelligence (AI)-based automated diagnostic systems provides potential solutions to address the limitations of cost and diagnostic time. In particular, deep learning and explainable AI (XAI) techniques provide a reliable and robust approach to classifying medical images. This paper presents a hybrid model comprising two networks, ResNext101_32x8d and Swin Transformer to differentiate four categories of Alzheimer’s disease: no dementia, very mild dementia, mild dementia, and moderate dementia. The combination of the two networks is applied to imbalanced data, trained on 5120 MRI images, validated on 768 images, and tested on 512 other images. Grad-CAM and LIME techniques with a saliency map are employed to interpret the predictions of the model, providing transparent and clinically interpretable decision support. The proposed combination is realized through a TensorFlow framework, incorporating hyperparameter optimization and various data augmentation methods. The performance evaluation of the proposed model is conducted through several metrics, including the error matrix, precision recall (PR), receiver operating characteristic (ROC), accuracy, and loss curves. Experimental results reveal that the hybrid of ResNext101_32x8d and Swin Transformer achieved a testing accuracy of 98.83% with a corresponding loss rate of 0.1019. Furthermore, for the combination “ResNext101_32x8d + Swin Transformer”, the precision, F1-score, and recall were 99.39%, 99.15%, and 98.91%, respectively, while the area under the ROC curve (AUC) was 1.00, “100%”. The combination of proposed networks with XAI techniques establishes a unique contribution to advance medical AI systems and assist radiologists during Alzheimer’s disease screening of patients. Full article

Numerous road accidents involving vulnerable road users result from their insufficient visibility to drivers. To increase the appeal of warning clothing and motivate consumers to use it, particularly in non-professional settings, an innovative high-visibility vest with an active lighting system (ALS) and phosphorescent elements was developed. The effectiveness of the vest’s visibility-enhancing elements was assessed by examining two factors: the intensity of the light emitted by the phosphorescent tapes and the luminance of the optical fibers in the ALS. Studies have shown that thermal-transfer phosphorescent tapes are approximately 42% more effective in terms of luminescence than sewn-on tapes. The ALS demonstrated high durability, withstanding up to 15 washing cycles at 40 °C in a mild process. The luminance of optical fibers decreases significantly with increasing distance from the light source (LED). The difference between the luminance at the light source and at the end of the 1 m optical fiber was about 6 cd/m², representing approximately 68% of the maximum luminance value. This finding can assist in designing luminous clothing. Tests in real-world conditions in a tunnel have shown that the ALS allows the visibility of vest user to be increased to over 430 m, which is a 67% increase compared to retroreflective tapes. Laboratory performance testing confirmed the high acceptability of the vest model, including its aesthetics, by potential users. Full article

Background: The interplay between neuronutrition, physical activity, and mental health for enhancing brain resilience to stress and overall human health is widely recognized. The use of brain mapping via quantitative-EEG (qEEG) comparative analysis enables researchers to identify deviations or abnormalities and track the changes in neurological patterns when a targeted drug or specific nutrition is administered over time. High-functioning mild-to-borderline intellectual disorders (MBID) and autism spectrum disorder (ASD) constitute leading global public health challenges due to their high prevalence, chronicity, and profound cognitive and functional impact. Objective: The objectives of the present study were twofold: first, to characterize an extremely vulnerable group of children with functioning autism symptoms, disclosing their overall pattern of cognitive abilities and areas of difficulty, and second, to investigate the relevance of the effects of a mushroom (Hericium erinaceus) biomass dietary supplement on improvement on neurocognitive behavior. Methods: This study used qEEG to compare raw data with a normative database to track the changes in neurological brain patterns in 147 children with high-functioning autistic attributes when mushroom H. erinaceus biomass supplement was consumed over 6 and 12 months. Conclusions: H. erinaceus biomass in children with pervasive developmental disorders significantly improved the maturation of the CNS after 6 to 12 months of oral use, decreased the dominant slow-wave activity, and converted slow-wave activity to optimal beta1 frequency. Therefore, despite the lack of randomization, blinding, and risk of bias, due to a limited number of observations, it may be concluded that the H. erinaceus biomass may generate a complex effect on the deficits of the autism spectrum when applied to high-functioning MBID children, representing a safe and effective adjunctive strategy for supporting neurodevelopment in children. Full article

Background: Early diagnosis of Alzheimer’s disease (AD) is essential for slowing disease progression and mitigating cognitive decline. However, conventional diagnostic methods are often invasive, time-consuming, and costly, limiting their utility in large-scale screening. There is an urgent need for scalable, non-invasive, and accessible screening tools. Methods: We propose a novel screening framework combining a pre-trained multimodal large language model with structured MMSE speech tasks. An artificial intelligence-assisted multilingual Mini-Mental State Examination system (AAM-MMSE) was utilized to collect voice data from 1098 participants in Sichuan and Chongqing. CosyVoice2 was used to extract speaker embeddings, speech labels, and acoustic features, which were converted into statistical representations. Fourteen machine learning models were developed for subject classification into three diagnostic categories: Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). SHAP analysis was employed to assess the importance of the extracted speech features. Results: Among the evaluated models, LightGBM and Gradient Boosting classifiers exhibited the highest performance, achieving an average AUC of 0.9501 across classification tasks. SHAP-based analysis revealed that spectral complexity, energy dynamics, and temporal features were the most influential in distinguishing cognitive states, aligning with known speech impairments in early-stage AD. Conclusions: This framework offers a non-invasive, interpretable, and scalable solution for cognitive screening. It is suitable for both clinical and telemedicine applications, demonstrating the potential of speech-based AI models in early AD detection. Full article

With the growing consumer demand for enhanced driving dynamics in vehicles, optimizing powertrain configurations to balance performance, energy efficiency, and cost has become a critical challenge. Traditional internal combustion engine vehicles (ICEVs) suffer from significant energy consumption and cost penalties when improving acceleration performance. This study systematically evaluates the trade-offs between dynamic performance, energy consumption, and direct manufacturing costs across six powertrain configurations: ICEV, 48 V mild hybrid (48 V), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), range-extended electric vehicle (REV), and battery electric vehicle (BEV). By developing a comprehensive parameterized model, we quantify the impacts of acceleration improvement on vehicle mass, energy consumption, and costs. Key findings reveal that electrified powertrains (PHEV, REV, BEV) exhibit superior cost-effectiveness and energy efficiency. For instance, improving 0–100 km/h acceleration time from 9 to 5 s reduces direct manufacturing costs by only 5.72% for BEV versus 13.38% for ICEV, while PHEV achieves a balanced compromise with 3.40% lower fuel consumption and 10.43% cost increase compared to conventional counterparts. Mechanistic analysis attributes these advantages to higher power density of electric motors and simplified energy transmission in electrified systems. This work provides data-driven insights for consumers and automakers to prioritize powertrain technologies under dynamic performance requirements, highlighting PHEV with driving range of 50 km as the optimal choice for harmonizing driving experience, energy economy, and affordability. The results of this study assist automakers in optimizing the technology pathways of vehicle powertrain, within the consumer demand for dynamic performance. This plays a crucial role in advancing the automotive industry’s overall fuel consumption and energy consumption, thereby contributing to sustainable development. Full article

Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, objective tool that measures oculometric features during mental addition tasks under one minute. This study aims to develop artificial intelligence (AI)-derived predictive models using OCAT eye movement and time-based features for the early detection of those at risk for MCI, requiring more thorough assessment. Methods: The OCAT with integrated eye tracking was completed by 250 patients at the Mayo Clinic Arizona Department of Neurology. Raw gaze data analysis yielded time-related and eye movement features. Random Forest and univariate decision trees were the feature selection methods used to identify predictors of Dementia Rating Scale (DRS) outcomes. Logistic regression (LR) and K-nearest neighbors (KNN) supervised models were trained to classify PMCI using three feature sets: time-only, eye-only, and combined. Results: LR models achieved the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall of 0.91, and an AUPRC of 0.95. The eye-only and time-only LR models also performed well (accuracy = 0.93), though with slightly lower F1-scores (0.87 and 0.86, respectively). Overall, models leveraging both time and eye movement features consistently outperformed those using individual feature sets. Conclusions: Machine learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering a promising approach for early MCI identification. With further refinement, OCAT has the potential to serve as a practical and scalable cognitive screening tool, suitable for use in clinics, at the bedside, or in remote and resource-limited settings. Full article

Listeria monocytogenes is a foodborne pathogen of significant concern. While it typically causes mild, self-limiting gastroenteritis, it poses a much higher threat to immunocompromised individuals and pregnant women, where it may lead to miscarriage. Numerous outbreaks have been linked to ready-to-eat foods. Although heat treatment is commonly used for microbial decontamination, it is unsuitable for fresh produce such as fruits and vegetables. Other physical (e.g., UV, gamma irradiation) and chemical (e.g., NaOCl, ozone) methods can compromise sensory qualities or face limited consumer acceptance. Photodynamic Inactivation (PDI) has emerged as a promising alternative, particularly when using natural photosensitizers. Because PDI efficacy depends on photosensitizer diffusion, there is a need to further explore how different and complex fruit surface structures may influence its performance. Three fruit models were therefore selected to represent distinct surface textures and were evaluated in situ: apples (smooth), strawberries (irregular), and kiwis (fuzzy and hairy surface). The influence of contamination order was also evaluated, as this factor is highly relevant to real-world supply-chain scenarios but has been largely overlooked in prior research. Additionally, the study investigated how the order of contamination affected the decontamination outcome. Sodium-magnesium-chlorophyllin (Na-Mg-Chl), an approved food additive (E140), was used as photosensitizer. Fruits were cut into 1 cm² squares and inoculated with L. monocytogenes. A 100 µM Na-Mg-Chl solution was applied either before or after bacterial inoculation. All samples were then illuminated using a 395 nm LED (radiant exposure 15 J/cm²). When L. monocytogenes was applied first, followed by the addition of Na-Mg-Chl, a 5.96 log reduction was observed in apples, a 5.71 log reduction in strawberries, and a 6.02 log reduction in kiwis. Conversely, when Na-Mg-Chl was applied prior to bacterial deposition, apples showed a 5.61 log reduction, strawberries demonstrated a 6.34 log reduction, and kiwis achieved the highest inactivation, at 6.74 log units. These results indicate that PDI consistently achieved substantial bacterial reductions across all fruit types, regardless of surface characteristics or application order. This supports PDI as a powerful method for fruit surface decontamination, reducing public health risks and economic losses while preserving product quality and consumer confidence. Full article