Nanostructured smart packaging: Bridging active functionality with intelligent sensing

Live cribwalls, a common Soil and Water Bioengineering (SWB) intervention, are typically built using timber, in combination with soil and plant materials. Timber, due to prolonged exposure to environmental and biological factors, inevitably undergoes progressive deterioration. This process is a fundamental element of live cribwall design that needs to be considered, as the simultaneous growth of live plants gradually provides soil reinforcement and compensates for the loss of wood resistance. Despite the importance of this dual dynamic, long-term monitoring data and standardized diagnostic tools to assess the deterioration of timber are still lacking. Among various diagnostic tools used to evaluate timber deterioration, drilling resistance (DR) is a semi-destructive technique capable of providing rapid and reliable assessments of wood density and mechanical properties. A 15-year monitoring campaign on 15 chestnut ( Castanea sativa Mill.) cribwalls, aged from 2 to 31 years, produced 794 perforations. Because DR measurements are influenced by operational parameters such as feed and drill speeds, we developed a correction model to standardize data collected over the years with different instrument settings. Results revealed a consistent linear decrease of DR values with structure age, corresponding to a reduction of approximately 2% per year, directly reflecting the progressive loss of density and mechanical strength of the wooden member. Distinct degradation stages were identified among cribwalls of different ages, confirming the gradual deterioration of structural timber over time. Within single structures, additional differences emerged depending on log position and microclimatic exposure. DR techniques proved valid and practical for operational use, offering a quick and reliable tool suitable for monitoring programs and research studies. The standardized approach developed here enhances consistency in the monitoring of cribwalls. It opens new opportunities for integrated research that combines wood degradation monitoring with the assessment of root reinforcement from vegetation growth, leading to a better understanding of the life cycle and functional lifespan of live cribwalls. • Deterioration of chestnut cribwalls assessed by standardized drilling resistance. • A correction model standardizes drilling resistance across feed and drill speeds. • Monitoring includes 15 structures aged 2–31 years with 794 drilling tests. • Drilling resistance decreases linearly by ∼2% per year. • The method provides a practical tool for durability assessment of SWB structures.

Military service members training with heavy weapons systems are exposed to low-level blast (LLB) overpressure, sometimes resulting in acute symptoms similar to mild traumatic brain injury (mTBI). There is a need for a quick and effective screening tool to identify early deficits and changes associated with blast exposure and minimizing potential ensuing effects. Recently, researchers developed the modified Vestibular/Ocular Motor Screening (mVOMS), designed to assess signs and symptoms of mTBI. modified Vestibular/Ocular Motor Screening consists of four physical movement conditions: smooth pursuits, horizontal saccades, horizontal vestibular-ocular reflex, and visual motion sensitivity to capture the provocation of the vestibular system (headache, dizziness, nausea, fogginess). After completing each activity, service members were asked whether the symptoms were present or absent. Modified Vestibular/Ocular Motor Screening offers a practical solution as it can be administered in approximately 2 minutes and is 85% accurate in identifying mTBI. Similarly, military settings, where time is often constrained in addressing potential mTBI can benefit from the efficiency and reliability of mVOMS, making it well-suited for training settings; however, the use of mVOMS to detect acute blast exposure effects in service members has not been explored, which was the primary purpose of this study. A secondary purpose was to explore the relationship between mVOMS and self-reported mTBI symptoms without provocation by physical motion, as well as eye-tracking. This was a prospective cohort study design involving 42 male military personnel training with mortar systems and 15 male, healthy, unexposed controls. Participants reported demographics and over three separate visits, a self-reported symptoms questionnaire, mVOMS, and smooth pursuit eye tracking assessment. Independent samples t-tests and chi-square analyses, along with Pearson's and rank-biserial correlations, compared demographic and outcome variables between exposed and control groups from the three visits, including individual measures of blast exposure magnitude. A series of receiver operating characteristic (ROC) curves with area under the curve (AUC) were used to evaluate the accuracy of mVOMS, mTBI symptoms, and eye tracking to identify exposed from control groups. This study has been approved by the Walter Reed Army Institute of Research Institutional Review Board. Participants' mean age was 25 ± 3.42 years with mean duration of service 4 ± 2.43 years. Service members exposed to blast reported more symptoms after performing the mVOMS physical movement challenges. The discriminatory power of the change from pre-exposure in mVOMS (AUC = 0.72) was comparable with the change in self-reported symptoms. In contrast to the change from pre-exposure, post-exposure single timepoint evaluation revealed mVOMS is superior (AUC = 0.70) compared to self-reported symptoms (AUC = 0.51). Modified Vestibular/Ocular Motor Screening was also positively correlated with blast exposure measures suggesting that increasing blast exposure levels are associated with increasing mVOMS scores. These results demonstrate that mVOMS in individuals exposed to LLB elicits a response that may be more sensitive than self-report alone. Our data suggests that mVOMS may be useful to screen for mTBI symptomology in service members following blast exposure when administered as a single timepoint test, as it is quick, simple to interpret, and requires minimal training to administer.

The fast development of e-commerce sites has driven up the need for smart and quick search tools. Keyword-based search systems have traditionally struggled to give correct results when consumers give visual cues or partial textual inquiries. To improve product search accuracy, this study presents a clever optimized search engine combining deep learning approaches, particularly U-Net-based image segmentation. The system searches a large dataset for visually similar goods after U-Net architecture segments relevant features from input images. The suggested strategy enhances user experience, eliminates uncertainty, and improves search relevance. Compared to conventional approaches, experimental findings reveal better retrieval efficiency and accuracy

Candidate varieties must satisfy the requirements of Distinctness, Uniformity and Stability (DUS) based on morphophysiological traits. Breeders of major perennial forage crops strive to achieve statistically significant distinctness of candidate varieties, especially with respect to registered varieties bred from the same genetic base. This study aimed to compare morphophysiological versus molecular distinctness for 10 potential alfalfa varieties selected from the same genetic base and one recently registered variety selected from a similar genetic base. We also aimed to support the optimization of alfalfa molecular distinctness by comparing (a) genotyping-by-sequencing (GBS) versus alfalfa DArTag panel markers, and (b) four statistical criteria. The 11 populations (one originating from mass selection, nine from progeny-based selection, and one from clonal selection) were evaluated for 10 morphophysiological traits ordinarily used for DUS in Italy and were characterized molecularly using three bulked DNA samples of 200 independent plants each per population. Only four morphophysiological traits exhibited significant (P < 0.01) population variation, mainly because of population similarity for autumn dormancy, growth habit, and plant vigor. Morphophysiological distinctness emerged for 20 of the 55 paired comparisons between the 11 populations, with no population showing distinctness from any other. Various GBS and DArTag marker configurations achieved a complete distinctness of each population from any other (at P < 0.01) by statistical criteria based on a principal components analysis of allele frequencies followed by analysis of variance or discriminant analysis of population principal component scores. These criteria showed greater population discrimination than cluster analysis with bootstrap values. Analysis of molecular variance was ineffective for population distinctness, probably because of insufficient number of bulked DNA samples per population. Mantel's test indicated high correlation for Euclidean distances of the populations between GBS and DArTag markers (r = 0.94) and no correlation of these distances with those based on morphophysiological traits. Variety registration on the ground of distinctness from the already registered variety could be granted to only four of 10 selections according to morphophysiological distinctness, and to all selections according to molecular distinctness. Our results support molecular distinctness as a sensitive, quick, and inexpensive tool for alfalfa variety registration, forensic analyses, and control of certified seed.

An effective figure helps communicate the data and findings clearly. The assembly of multi-panel omics-based figures for scientific publications is a time-consuming and error-prone process that often requires specialized software and skills. There is a lack of a quick and effective tool for assembling complex omics-based figures. We present EasyFigAssembler, a user-friendly web application that significantly simplifies and accelerates the making of multi-panel omics-based figures. The app supports nondestructive panel editing, multi-figure project management, and high-resolution export to TIFF, PDF, PNG, and JPEG formats, providing a complete solution for figure assembly without requiring programming expertise. Its Smart Layout feature automates the initial arrangement of panels into optimal, publication-ready grids. The advanced controls for column and row spanning enable the rapid assembly of complex, non-uniform layouts that are typically time-consuming to create. The workflow is ensured by an integrated Quality Advisor module that provides real-time feedback on effective resolution and color-blind-friendly palettes to warrant journal compliance. Finally, users can manage multiple figures and save their projects effortlessly. The source code of EasyFigAssembler is available at https://github.com/Pharmaco-OmicsLab/EasyFigAssembler. The web interface for users can be accessed at https://easyverse.app/easyfig/.

Oral health is critical for overall health and well-being, especially in older people. However, the frequency of dental visits tends to decrease as individuals age. In contrast, frail older people who are less likely to visit the dentist, increasingly rely on medical care, such as the general medical practitioner (GP). There is a lack of quick and efficient screening tools for GPs to assess the risk of oral health deterioration. This study aims to develop a short screening tool to provide GPs with a quick, easy-to-use tool to predict oral health deterioration in older people, enabling timely referral and intervention when needed. The study consisted of three phases. In phase 1, the research team preliminary selected themes and items based on their predictive value, following a prior systematic review. In phase two the English self-reporting items were translated into Dutch, following cross-cultural adaptation guidelines. In phase 3 a panel of experts was selected and a Delphi method was conducted to develop the SHORT screening tool. The preliminary selection resulted in thirteen self-reporting items. Translation and back-translation proceeded without difficulties. The Delphi panel included 10 healthcare professionals and consisted of three rounds, after which consensus was achieved for the inclusion of three self-reporting items in the tool (100% agreement; median score = 5; IQR = 0). The SHORT screening tool was developed, including a preliminary selection of items, translation, and the Delphi method, which was successful in achieving consensus. Further research is recommended to validate and test the psychometric properties of this newly developed screening tool. - The SHORT tool provides a quick, three-item screening method for general practitioners to identify older people at risk for oral health deterioration, enabling timely referrals. - It addresses the lack of practical oral health assessment tools tailored for non-oral health professionals. - The SHORT tool was developed through expert consensus and is ready for further validation.

Plant diseases have a major impact on food security and agricultural productivity because they lower crop quality and output. For crop management to be effective, plant diseases must be identified early and accurately. Using leaf image analysis, this study suggests an intelligent deep learning-based approach for automatically identifying illnesses in tomato plants (Solanum Lycopersicon). Plants that produce tomatoes are particularly vulnerable to a number of diseases, including leaf Mold, early blight, and late blight, which can negatively affect crop yield. In order to increase feature visibility, a dataset of tomato leaf photos that includes both healthy and diseased samples is gathered and preprocessed utilizing image enhancement and normalization techniques. In order to precisely classify various disease categories and extract deep information from leaf photos, the suggested method uses a Convolutional Neural Network (CNN) in conjunction with transfer learning utilizing the Mobile Net architecture. Significant visual characteristics like Colour shifts, textural alterations, and lesion shapes on infected leaves are immediately picked up by the CNN model. The suggested model's efficacy is shown by experimental study. Strong classification capabilities are demonstrated by the system's 97.8% training accuracy and 96.4% testing accuracy. The algorithm's reliability is further supported by performance assessment metrics, which show an F1-score of 96.0%, recall of 96.1%, and precision of 95%. Stable model convergence is also shown by the training and validation loss values, which drop from 0.45 and 0.50 at the first epoch to 0.11 and 0.15, correspondingly. The suggested MobileNet-CNN models works better than current designs like VGG16 (92.3%), ResNet50 (94.1%), and InceptionV3 (95.2%), according to comparison studies. By offering a quick and accurate tool for early disease identification, the proposed system can assist cultivators and farming specialists, facilitate prompt medical care and enhance total crop production and methods that are environmentally friendly.

The ACTIVATE consensus aimed to develop recommendations for assessing, prescribing and promoting physical activity when healthcare professionals consult patients with non-communicable diseases in clinical practice. We developed the consensus through a comprehensive, multistep approach including 27 experts with professionally diverse backgrounds from 13 different countries, including three patient representatives. Initially, we conducted a survey at the World Congress for Sport Physical Therapy in 2022 to document the need for a consensus, which was followed up by development of a conceptual framework with guiding principles and research questions at a stakeholder meeting. We subsequently conducted three rapid umbrella reviews, covering the existing scientific literature to inform the statements for a Delphi survey. The Delphi survey involved two voting rounds (email survey) and one face-to-face (online) round. Finally, we identified recommendations on how to assess, prescribe and promote physical activity through consensus. We recommend routine physical activity assessments using quick and easy tools, documenting physical activity levels and providing tailored prescriptions for patients not meeting WHO guidelines for physical activity. Furthermore, the ACTIVATE consensus highlights the necessity for regular follow-ups to facilitate patients' integration of physical activity into their daily routines, to enhance overall well-being and quality of life.

To evaluate the predictive value of the simple Total Abnormality Score (TAS) for assessing cranial magnetic resonance images to predict cerebral palsy (CP). In this retrospective cohort study, 137 infants with a gestational age no more than 32 + 0 weeks and/or with a birthweight less than 1500 g, born between 2017 and 2021, were included. Findings from cranial magnetic resonance imaging at term-equivalent age were assessed using the TAS. At approximately 24 months (corrected age), the diagnosis of CP and its severity using the Gross Motor Function Classification System (GMFCS) were determined interdisciplinarily. A binary logistic regression with CP as the outcome parameter and a Spearman's rank correlation with TAS and GMFCS were performed in SPSS. The TAS was higher in infants with CP (median 11, interquartile range 10-15, n=9) than in infants without CP (median 2, interquartile range 2-4, n=128). The odds ratio for CP was 1.31 per one-point increase in TAS. The optimal cut-off value of the TAS for predicting CP was 9.5 (sensitivity 88.9%, specificity 91.4%). There was a correlation between the TAS and GMFCS (Spearman's rho=0.34, p < 0.001). The TAS is a useful, quick tool for risk stratification of CP in this cohort. Future studies should consider replication in a larger sample and combination with additional tools.

The global biosensors market is rapidly growing, driven by increasing demand for quick, affordable, and portable diagnostic tools across sectors, including healthcare, environmental monitoring, food safety, and biomedical research. The key to biosensor function is the biological recognition element (BRE), which determines the specificity, sensitivity, and reliability of the device. Traditional BREs, such as antibodies and enzymes, face significant limitations, including instability, high costs, and variability. Phage display technology offers a strong alternative, providing durable, stable, and highly specific peptides as BREs. However, it requires effective amplification and purification methods to produce high-quality peptide libraries. This study examines key factors affecting the amplification of filamentous M13 bacteriophage, highlighting the negative impact of high multiplicity of infection (MOI) caused by superinfection exclusion and reduced phage adsorption efficiency. Our findings indicate that the bacterial growth phase is the most important determinant of M13 amplification efficiency. Furthermore, post-infection PEG/NaCl precipitation followed by high-speed centrifugation significantly outperforms traditional filtration methods in purifying phages, maximizing recovery and viability. These findings present an optimized, reproducible, and scalable approach to M13 phage amplification, improving the effectiveness of phage display for developing advanced biorecognition elements. Ultimately, this research provides a foundational framework for more efficient biosensing and therapeutic applications, filling critical gaps in the current biosensor development landscape.

The performance of sandwich beams in high-speed railway systems experiences severe challenges because their dynamic stability and safety require material configuration tests under high dynamic loads. The research studies sandwich beam performance, which consists of 2D functionally graded face sheets and honeycomb core structures when exposed to moving loads that mimic high-speed train operations. The higher-order shear deformation theory (HSDT) enables accurate modeling of transverse shear effects in thick and layered sandwich structures without the need for shear correction factors. The researchers used Hamilton’s principle to create the governing equations of motion through a detailed process, which included both moving load kinematics and material gradation in face sheets. The researchers applied the differential quadrature method (DQM) to discretize the coupled partial differential equations, which achieved high precision using a minimal number of grid points, while the Newmark method was used for time integration to maintain numerical stability and operational efficiency. Researchers conduct parametric studies to study how different factors impact dynamic stability limits and vibration behavior of sandwich beams, which include load velocity and material gradation indices and honeycomb core characteristics and geometric parameters. The development of a hybrid machine learning system enables better verification of numerical results, which enhances the generated confidence level. The machine learning models achieve training success through the DQM-Newmark solutions, which function as both verification systems and quick assessment tools. Engineers can employ the combined theoretical-numerical-data-driven method as a robust framework to assess dynamic stability and safety for advanced sandwich beam structures in high-speed railway systems.

As dementia becomes a major health concern, developing a quick and accurate diagnostic tool is crucial for early detection and intervention in resource-limited settings. This paper aims to validate the short 10/66 algorithm against the clinical diagnosis of dementia in a sample of older adults in Lebanon. This study is a secondary analysis of data from the Lebanese validation of the standard 10/66 dementia assessment, evaluating the diagnostic accuracy of the Arabic short 10/66 algorithm against a clinical reference standard. 231 participants aged 65 + were recruited from institutions and their dwellings, including geriatric clinics and community-based primary care centers. Clinical dementia diagnosis followed DSM-IV criteria. The final sample included 88 cases (diagnosed with mild to moderate dementia) and 143 controls. The short algorithm used is derived from the Community Screening Instrument for Dementia, the CERAD 10-word list recall and the Euro-D. The psychometric parameters of sensitivity, specificity, positive predictive value (PPV), Negative Predictive Value (NPV), false positive rate (FPR), and area under the curve (AUC) were estimated for the short 10/66 algorithm and its subcomponents. Compared to clinical diagnosis, the short 10/66 showed high specificity (95.1%), good sensitivity (79.5%), excellent PPV (90.9%), high NPV (88.3%), low FPR (4.9%), and substantial agreement between the two (AUC = 0.97, kappa = 0.765). The short 10/66 is accurate and feasible for the diagnosis of dementia in the older Lebanese population, offering a practical tool with a lower time requirement that is adaptable to different literacy levels. Interpretation of predictive values should consider the enriched sampling design, and performance should be further assessed in lower prevalence community settings.

Pedal Acceleration Time: A Functional Indicator of Success in Revascularisation.

Cardiovascular diseases (CVDs) accounted for the major of morbidity and mortality worldwide, so it is crucial to have quick and accurate diagnostic tools. This work aims to use Artificial intelligence algorithms to enhance a patient’s Cardiac health by conducting an analysis of coronary arteries. This proposed approach involves data preprocessing step, feature selection, model optimization in a timely manner to improve and maximize prediction of disease diagnostic. ML algorithms such as CNN and LSTM are applied on the problem with a view of identifying the more accurate in terms of classifying case of coronary artery diseases. Formatting oriented results in terms of accuracy (

Cervical intraepithelial neoplasia (CIN) is a group of precancerous lesions associated with invasive carcinoma of the cervix that reflects the continuous progression of cervical cancer (CC). Therefore, early detection and standard treatment can effectively prevent the progression of CIN to CC. The objective of this study is to establish machine learning model using clinical data to predict the risk of CIN in women, and to develop a clinical prediction tool, exploring its broader clinical application significance. Female patients who sought consultation for cervical lesions at a hospital in Jiangsu province between 2018 and 2021 were enrolled in this study. The feature variables considered in the analysis included age, ThinPrep cytological test (TCT), human papillomavirus (HPV) genotype, multiple infection assessment, folate receptor-mediated tumor detection (FRD) and cotton-tipped swab test. Several algorithms were utilized for establishing the model, including adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), categorical boosting (CatBoost) and others. The performance of models was rigorously evaluated. The SHapley Additive exPlanation (SHAP) values were used to identify risk factors affecting the risk of CIN. For predicting CIN events, CatBoost and GBDT had the highest area under the receiver operating characteristic curve (AUC) (0.89, 0.87, respectively). AdaBoost had the highest F1 score (F1 score = 0.81), followed by RF, LR and stochastic gradient descent (SGD). SHAP values suggested that the variables affected the risk of CIN in descending order of magnitude were TCT, age, FRD, cotton-tipped swab, multiple infection and HPV, respectively. A novel CatBoost-based risk prediction tool for CIN (CINPred) has been developed and it can be accessed through the website at: https://medinfo.hebeu.edu.cn/shiny/CINPred/. CINPred can be used as a quick screening tool to assess CIN risk, offering significant benefits for the development of personalized treatment plans.

The Urgency Numeric Rating Scale (U-NRS) assesses the severity of bowel urgency, a common symptom that is often underestimated in ulcerative colitis. This study aimed to translate the U-NRS into Portuguese and to provide validation in clinical practice, as well as to assess the prevalence of bowel urgency in a cohort of ulcerative colitis patients and, in those in clinical remission, to evaluate its relationship with endoscopic and histological activity and manometric changes. After translation of the U-NRS into Portuguese, patients with ulcerative colitis seen at the outpatient clinic were asked to complete the scale at T0, after 2 weeks (T1) and after 3 months (T2). Patient-reported outcome-2 ulcerative colitis (PRO-2-UC), EQ-5D, EQ-Visual Analogue Scale (EQ-VAS), inflammatory bowel disease disk (IBD-Disk), and the Global Perceived Effect Scale were also completed. Acceptability, reliability, construct validity, responsiveness, and interpretability were assessed. A subgroup of patients in clinical remission with bowel urgency underwent sigmoidoscopy with rectal biopsies and anorectal manometry and were compared with a control group (in clinical remission without bowel urgency). In total, 126 patients were included, of whom 49.21% (n = 62) had bowel urgency. Test-retest reliability was excellent (intraclass correlation coefficient: 0.768). The U-NRS correlated significantly with the PRO-2-UC, IBD-Disk, EQ-VAS, and EQ-5D (P < 0.001), demonstrating construct validity. The U-NRS values correlated with the first constant sensation volume on anorectal manometry (rs = -0.374, P = 0.038). The U-NRS scale is an easy and quick tool for assessing bowel urgency and can be incorporated into clinical practice.

HIMAP POCUS protocol for early evaluation of undifferentiated shocked patients at the emergency department: A prospective study of diagnostic accuracy and time efficiency.

Abstract Background Loneliness is common among older adults and linked to poor health outcomes. In the UK, around 1.4 million older people experience frequent loneliness, a number expected to rise. Despite its impact, loneliness is often unrecognised in acute hospitals. The UCLA Loneliness Scale Version 3 (UCLA-3) is a brief, validated 3-item tool with 77% sensitivity and 61% specificity, suitable for routine geriatric screening. Objectives · Assess feasibility of UCLA-3 for rapid loneliness screening. · Determine loneliness prevalence in older inpatients. · Explore integration of UCLA-3 into geriatric care pathways. Methods A cross-sectional study of 50 randomly selected patients, aged 65 and over on geriatric wards at MMH, between, 1st to 31st of May 2025 was conducted. The data which included UCLA-3 scores, completion time, demographics, and Clinical Frailty Scale (CFS) status was collected by the resident doctors. Results Of the 50 participants, 41/50 (82%) completed the UCLA-3 in under 5 minutes, 7/50 (14%) between 5–15 minutes and 1/50(2%) took more than 15 minutes. 1/50(2%) patient’s questionnaire completion time was not recorded. · Moderate to severe frailty (CFS scores 6–9) was observed in 24/50 (48%) of the screened patients. · Loneliness (UCLA score 6–9) was identified in 16/50 (32%), while 34/40 (68%) were classified as not lonely (UCLA score 3–5). · Among those identified as lonely, 8/16 (50%) were moderate to severely frail. Conclusion The UCLA-3 is a quick, practical and reliable bedside screening tool for early detection of loneliness, which among frail older adults may be a modifiable risk factor. Recommendations: · Screen all elderly patients for loneliness due to high prevalence. · Incorporate UCLA-3 scoring routinely into Comprehensive Geriatric Assessments and discuss high UCLA-3 (score 6–9) during MDT meetings. · Refer all lonely individuals to NHS loneliness support and involve local charities for additional help.

Translated article] It's possible to know tibial torsion in a X ray of the limbs?