Search Results (5,781)

Background/Objectives: Early identification of terminally ill patients is crucial for enhancing care, patient and care partner satisfaction, and healthcare staff confidence in discussing disease trajectories. Yet, timely recognition remains challenging. To address this, we developed a minimally invasive intervention (MINI) for general hospital wards. We aimed to evaluate the MINI’s feasibility in facilitating an earlier identification of terminally ill patients and improving patient reported outcomes in a hospital setting. Methods: This prospective, two-arm pre-post intervention study at a university hospital evaluated the MINI alongside usual care. Patient-reported outcomes, including quality of life (SF-12), palliative care needs (IPOS), and functional status (ECOG), were collected at baseline and every three months over 12 months. Participants were allocated to a control or intervention group. Results: Of 188 patients identified using the Surprise Question, 58 completed the baseline assessment. While physical functioning (SF-12 PCS) remained comparable, the intervention group experienced clinically meaningful improvements in mental health (SF-12 MCS) at three months, with positive trends at six months. This group also showed a decline in palliative care needs, reduced emotional symptoms, and improved performance status, evidenced by significant differences in non-parametric analyses. These findings underscore the MINI’s potential to significantly improve patient well-being. Conclusions: This pilot study demonstrated the feasibility of the MINI and suggests it may foster meaningful system-wide change in patient-centred care within acute hospital settings, leading to improved patient outcomes and more confident healthcare staff in identifying terminally ill patients. However, given the small sample size, these findings should be interpreted with caution. Future research with larger cohorts and extended intervention periods is warranted to fully elucidate the MINI’s impact and refine strategies for improving care for terminally ill patients. Full article

Background and Objectives: The hypothesis that fascial thickness variability may serve as a biomarker for low back pain (LBP) requires a clear understanding of typical thickness values in both LBP and non-LBP populations—an area still lacking in the literature. This narrative review aims to define reference values and patterns of variability for the superficial fascia, deep fascia, and subcutaneous tissue in individuals with and without LBP. Methods: A literature search was conducted in PubMed and ScienceDirect using keywords such as superficial fascia, deep fascia, thoracolumbar, subcutaneous fat, back pain, lumbar, thorax, and thickness. Inclusion criteria focused on human studies with proper identification of the relevant soft tissue structures. A total of 21 studies, published up to February 2024, met the inclusion criteria and were analyzed. Results: The review revealed notable intra- and inter-study variability in the thickness of the investigated structures. In LBP populations, both deep fascia and subcutaneous tissues were generally equal to or thicker than in controls (non-LBP), whereas consistent data on superficial fascia thickness remain limited. Age, sex, and anatomical location were discussed as potential influencing factors. Conclusions: These findings support the establishment of reference thickness values for subcutaneous and fascial tissues and encourage further investigation into their structural and functional roles in LBP. The observed variability may offer a basis for patient- and site-specific assessment and intervention strategies. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a progressive condition whose heterogeneous endotypes, clinical manifestations, and recovery pathways complicate the identification of reliable predictors of rehabilitation outcomes. Several respiratory and functional assessments are available with no consensus on the most predictive ones. While univariate markers may miss multifactorial interactions essential for prognosis, data-driven unsupervised clustering methods can integrate complex information from different sources. This study aimed to apply unsupervised clustering to identify pre-rehabilitation characteristics predictive of discharge outcomes for COPD patients undergoing pulmonary rehabilitation. Methods: A total of 126 COPD patients undergoing pulmonary rehabilitation were included in the analysis. Three assessments were performed at admission, namely the forced oscillation technique, spirometry, and the six-minute walk test (6MWT). The outcome was the change in 6MWT distance between admission and discharge. Unsupervised clustering methods were applied to admission variables to identify subgroups associated with outcomes. Results: Among the clustering algorithms tested, k-means (with N_cl = 2) provided the optimal solution. The resulting respiratory rehabilitation index (R2I) was significantly associated with the outcome dichotomized via the minimal clinically important difference of 30 m. Patients with R2I = 1, indicating severe functional and respiratory impairments, were associated with higher post-rehabilitation functional improvement (p = 0.032). While few functional parameters of 6MWT were statistically different between the groups identified by outcome, nearly all variables in the analysis exhibited significant distribution differences among the R2I clusters. Conclusions: These findings highlight the heterogeneity of COPD and the potential of unsupervised clustering to identify distinct patient subgroups, enabling more personalized rehabilitation strategies. Full article

Background/Objectives: Total knee arthroplasty (TKA) and total hip arthroplasty (THA) are widely performed procedures often associated with significant blood loss, leading to the need for allogeneic blood transfusion. Transfusions carry inherent risks and increase healthcare costs, making the identification of transfusion predictors crucial. This study aimed to assess preoperative predictors associated with transfusion requirement in patients undergoing THA or TKA. Methods: This single-center, retrospective analysis included 742 patients who underwent primary TKA or THA between 2016 and 2023. Preoperative variables such as hemoglobin, red blood cell count (RBC), INR, APTT, and use of tranexamic acid (TXA) were collected. Univariable and multivariable logistic regression analyses were conducted to identify independent predictors of transfusion. Results: Transfusions were required in 12.0% of patients. Multivariable analysis revealed that lower preoperative HGB and RBC levels, absence of TXA use, higher INR, and undergoing THA (versus TKA) were independently associated with increased transfusion risk. INR was not significant in univariable analysis but reached significance in the adjusted model. The final multivariable model demonstrated good predictive performance, with an area under the ROC curve (AUC) of 0.79. Conclusions: Lower hemoglobin and RBC levels, elevated INR, absence of TXA use, and THA surgery were independent predictors of transfusion. These findings may guide the use of routine preoperative hematologic and coagulation assessments to guide perioperative management and reduce transfusion rates in joint arthroplasty. Full article

Driven by growing public security demands and the advancement of intelligent surveillance systems, person re-identification (ReID) has emerged as a prominent research focus in the field of computer vision. %The primary objective of person ReID is to retrieve individuals with the same identity across different camera views. However, this task presents challenges due to its high sensitivity to variations in visual appearance caused by factors such as body pose and camera parameters. Although deep learning-based methods have achieved marked progress in ReID, the high cost of annotation remains a challenge that cannot be overlooked. To address this, we propose an unsupervised attribute learning framework that eliminates the need for costly manual annotations while maintaining high accuracy. The framework learns the mid-level human attributes (such as clothing type and gender) that are robust to substantial visual appearance variations and can hence boost the accuracy of attributes with a small amount of labeled data. To carry out our framework, we present a part-based convolutional neural network (CNN) architecture, which consists of two components for image and body attribute learning on a global level and upper- and lower-body image and attribute learning at a local level. The proposed architecture is trained to learn attribute-semantic and identity-discriminative feature representations simultaneously. For model learning, we first train our part-based network using a supervised approach on a labeled attribute dataset. Then, we apply an unsupervised clustering method to assign pseudo-labels to unlabeled images in a target dataset using our trained network. To improve feature compatibility, we introduce an attribute consistency scheme for unsupervised domain adaptation on this unlabeled target data. During training on the target dataset, we alternately perform three steps: extracting features with the updated model, assigning pseudo-labels to unlabeled images, and fine-tuning the model. % change Through a unified framework that fuses complementary attribute-label and identity label information, our approach achieves considerable improvements of 10.6\% and 3.91\% mAP on Market-1501→DukeMTMC-ReID and DukeMTMC-ReID→Market-1501 unsupervised domain adaptation tasks, respectively. Full article

Background/Objectives: The progression of antibiotic resistance is increasingly recognized as a dynamic and time-dependent phenomenon, challenging conventional diagnostics that define resistance as a binary trait. Methods: Biomolecules have fingerprints in Fourier-transform infrared spectroscopy (FTIR). The targeting of specific molecular groups, combined with principal component analysis (PCA) and machine learning algorithms (ML), enables the identification of bacteria resistant to antibiotics. Results: In this work, we investigate how effective classification depends on the use of different numbers of principal components, spectral regions, and defined resistance thresholds. Additionally, we explore how the time-dependent behavior of certain spectral regions (different biomolecules) may demonstrate behaviors that, independently, do not capture a complete picture of resistance development. FTIR spectra were obtained from Staphylococcus aureus exposed to azithromycin, trimethoprim/sulfamethoxazole, and oxacillin at sequential time points during resistance induction. Combining spectral windows substantially improved model performance, with accuracy reaching up to 96%, depending on the antibiotic and number of components. Early resistance patterns were detected as soon as 24 h post-exposure, and the inclusion of all three biochemical windows outperformed single-window models. Each spectral region contributed distinctively, reflecting biochemical remodeling associated with specific resistance mechanisms. Conclusions: These results indicate that antibiotic resistance should be viewed as a temporally adaptive trajectory rather than a static state. FTIR-based biochemical profiling, when integrated with ML, enables projection of phenotypic transitions and supports real-time therapeutic decision-making. This strategy represents a shift toward adaptive antimicrobial management, with the potential to personalize interventions based on dynamic resistance monitoring through spectral biomarkers. Full article

Background: China has the largest number of patients with type 2 diabetes (T2D) worldwide, and the chronic complications and economic burden associated with T2D are becoming increasingly severe. Developing accurate and widely applicable risk prediction models is of great significance for the early identification of and intervention in high-risk populations. However, current Chinese models still have many shortcomings in terms of methodological design and clinical application. Objective: This study conducts a systematic review and narrative synthesis of existing risk prediction models for type 2 diabetes in China, aiming to identify issues with existing models and provide references with which Chinese scholars can develop higher-quality risk prediction models. Methods: This study followed the PRISMA guidelines to conduct a systematic search of the literature related to T2D risk prediction models in China published in English journals from October 2019 to October 2024. The databases included PubMed, CNKI and Web of Science. Included studies had to meet criteria such as clear modeling objectives, detailed model development and validation processes, and a focus on non-diabetic populations in China. A total of 20 studies were ultimately selected and comprehensively analyzed based on model type, variable selection, validation methods, and performance metrics. Results: The 20 included studies employed various modeling methods, including statistical and machine learning approaches. The AUC values of the models ranged from 0.728 to 0.977, indicating overall good predictive capability. However, only one study conducted external validation, and 45% (9/20) of the studies binned continuous variables, which may have reduced the models’ generalization ability and predictive performance. Additionally, most models did not include key variables such as lifestyle, socioeconomic factors, and cultural background, resulting in limited data representativeness and adaptability. Conclusions: Chinese T2DM risk prediction models remain in the developmental stage, with issues such as insufficient validation, inconsistent variable handling, and incomplete coverage of key influencing factors. Future research should focus on strengthening multicenter external validation, standardizing modeling processes, and incorporating multidimensional social and behavioral variables to enhance the clinical utility and cross-population applicability of these models. Registration ID: CRD420251072143. Full article

Background: The widespread use of cardiac magnetic resonance imaging (MRI) in clinical practice has enabled the identification of numerous patients with evident damage from previous myocarditis, whether known or unknown. For years, myocardial fibrosis has been a topic of interest due to its established correlation with arrhythmic events in various clinical settings, including ischemic heart disease, dilated cardiomyopathy, and hypertrophic cardiomyopathy. MIBG scintigraphy is a method widely used in patients who are candidates for defibrillator implantation or have experienced heart failure. This examination evaluates the sympathetic innervation of the myocardium. Objective: To assess the real arrhythmogenic risk of non-ischemic scars identified in symptomatic or asymptomatic patients through the use of MIBG. Methods: Patients were retrospectively selected based on the presence of non-ischemic myocardial fibrosis detected by cardiac MRI, consistent with a myocarditis outcome (even in the absence of a clear history of myocarditis). These patients underwent myocardial scintigraphy with MIBG using a tomographic technique. Results: A total of 50 patients (41 males, mean age 51 ± 16 years) who underwent MRI from 2019 to June 2024 were selected. The primary indication for MRI was ventricular ectopic extrasystoles detected on Holter ECG (n = 12, 54%), while five patients underwent MRI following a known acute infectious event (23%, including three cases of COVID-19 infection). All symptomatic patients presented with chest pain in the acute phase, accompanied by elevated hsTNI levels (mean value: 437 pg/mL). The MRI findings showed normal ventricular volumes (LV: 80 mL/m², RV: 81 mL/m²) and normal ejection fractions (56% and 53%, respectively). The mean native T1 mapping value was 1013 ms (normal range: 950–1050). T2 mapping values were altered in the 5 patients who underwent MRI during the acute phase (mean value: 57 ms), without segmentation. Additionally, three patients had non-tamponade pericardial effusion. All patients exhibited LGE (nine subepicardial, seven midwall, six patchy). All patients underwent myocardial scintigraphy with MIBG at least 6 months after the acute event, with only one case yielding a positive result. This patient, a 57-year-old male, had the most severe clinical presentation, including more than 65,000 premature ventricular beats (PVBs) and multiple episodes of paroxysmal supraventricular tachycardia (PSVT) recorded on Holter ECG. MRI findings showed severe left ventricular dysfunction, a slightly dilated LV, and midwall LGE at the septum, coinciding with hypokinetic areas. Conclusions: MIBG scintigraphy could be a useful tool in assessing arrhythmic risk in patients with previous myocarditis. It could help reduce the clinical burden of incidental findings of non-ischemic LGE, which does not appear to be independently associated with an increased risk profile. Full article

Background/Objectives: Research has shown that those who experience childhood sexual abuse (CSA) are at increased risk of subsequent sexual revictimization. Multiple sexual victimizations can lead to higher rates of depression, anxiety, trauma, and suicidality. Prior research has yielded varying definitions of revictimization, including only accounting for revictimization that occurred in adulthood or multiple CSA episodes by the same perpetrator, or it has broadly assessed maltreatment without a specific focus on CSA. This study examined mental health outcomes in survivors of CSA who experienced sexual revictimization in childhood from a different perpetrator, comparing their mental health outcomes (i.e., depression, suicidal ideation, post-traumatic stress disorder (PSTD), hopelessness, guilt, and shame) to those who reported CSA by one perpetrator. Methods: Adult survivors of CSA (n = 627) completed an online survey describing their CSA experience, whether they experienced CSA by one or multiple perpetrators in childhood, and a series of mental health questionnaires. Results: Almost half of the sample reported CSA by more than one perpetrator in childhood (n = 267; 42.58%). Survivors who reported multiple CSA perpetrators reported significantly higher levels of depression, suicidal thoughts, PTSD, hopelessness, shame, and some facets of guilt in adulthood compared to those who reported CSA by a single perpetrator. Conclusions: Experiencing CSA by multiple perpetrators in childhood may lead to more negative mental health outcomes in adulthood. The findings emphasize the importance of early identification and intervention for individuals who experienced CSA. Full article

Background/Objectives: The aim of this study is to describe fertility preservation (FP) techniques performed over the last 10 years at a tertiary hospital in northern Spain in patients under 18 diagnosed with cancer. Methods: A retrospective medical record review was conducted for patients aged 0 to 18 years diagnosed between January 2014 and December 2023 in the Pediatric Oncology Unit at a university hospital. We evaluated patient characteristics, the timing of FP procedures, and potential risk factors for ovarian insufficiency and early azoospermia. Additionally, we assessed the agreement between two gonadotoxicity risk classifications. Results: In our center, FP is more frequently offered to pubertal patients (12 to 16 years old), prior to treatment in those at high risk of subsequent gonadotoxicity (>80%), and after treatment in those at low risk (<20%). Additionally, the increased provision of FP over the last five years of the study suggests improved clinician uptake of this long-term effect of cancer treatment. Our study found weak agreement between available gonadotoxicity risk classifications, complicating the identification of FP candidates. Long-term follow-up of survivors allowed for the detection of ovarian insufficiency (1.2%) and early azoospermia (0.7%), enabling hormone replacement therapy when necessary. Hematopoietic stem cell transplantation (HSCT) emerged as a predictor of early infertility. Conclusions: Our study highlights the prevalence of gonadotoxicity in pediatric cancer patients at our center and the increasing access to FP techniques. The findings emphasize the importance of personalized medicine, tailored FP strategies based on individual risk, and long-term follow-up to assess fertility status. Full article

Addressing limitations in remote sensing irrigation monitoring (insufficient resolution, single-source constraints, poor terrain adaptability), this study developed a high-precision identification framework for Jianping County, China, a semi-arid region. We integrated Sentinel-1 SAR (VV/VH), Sentinel-2 multispectral, and MOD11A1 land surface temperature data. Savitzky–Golay (S-G) filtering reconstructed time-series datasets for NDVI, SAVI, TVDI, and VV/VH backscatter coefficients. Irrigation mapping employed random forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Key results demonstrate the following. (1) RF achieved superior performance with overall accuracies of 91.00% (2022), 88.33% (2023), and 87.78% (2024), and Kappa coefficients of 86.37%, 80.96%, and 80.40%, showing minimal deviation (0.66–3.44%) from statistical data; (2) SAVI and VH exhibited high irrigation sensitivity, with peak differences between irrigated/non-irrigated areas reaching 0.48 units (SAVI, July–August) and 2.78 dB (VH); (3) cropland extraction accuracy showed <3% discrepancy versus governmental statistics. The “Multi-temporal Feature Fusion + S-G Filtering + RF Optimization” framework provides an effective solution for precision irrigation monitoring in complex semi-arid environments. Full article

Background/Objectives: RAB11 (RABA) is a type of RAB GTPase. RAB GTPases are key components of membrane trafficking mechanisms, Rab11 is implicated in a variety of biological developmental processes and responses to biotic and abiotic stresses. Nevertheless, the role of Rab11 in the defense mechanisms of cotton against Verticillium dahliae (V. dahliae) remains to be elucidated. Methods: In the present study, by analyzing the transcriptome data of Gossypium hirsutum (G. hirsutum) infected with V. dahliae, in combination with gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, the research focused on endocytosis. Further, through bioinformatics approaches, the endocytosis-related gene Rab11 was identified. We conducted a genome-wide identification and analysis of Rab11 in G. hirsutum. In addition, by integrating transcription factor (TF) prediction, prediction of protein–protein interactions (PPI) and quantitative real-time polymerase chain reaction (qRT-PCR), the gene expression of Rab11 at different infection periods of V. dahliae (0, 24 and 72 hpi) were analyzed and validated. Results: The analysis of transcriptome data revealed that the endocytosis pathway is implicated in the stress response of cotton to V. dahliae. Additionally, three Rab11 genes were identified as being involved in this stress response. Phylogenetic analysis revealed that the 65 genes in the Rab11 family could be divided into four subgroups, each with similar gene structures and conserved motif patterns. Conclusions: The downregulation of Rab11 in G. hirsutum is closely linked to its defense against V. dahliae. TF prediction coupled with PPI offers a roadmap for dissecting the signaling pathways, functional validation, and network construction of the three GhRab11 genes. Full article

Currently, wind turbines utilize doubly fed induction machines that incorporate a frequency converter in the rotor circuit to manage slip energy. This setup ensures a stable voltage amplitude and frequency that align with the alternating current. It is crucial to accurately determine the parameters of the equivalent circuit from the rotor side of the vector control system of the frequency converter. The objective of this study is to develop a method for the preliminary identification of the doubly fed induction machines parameters by analyzing the rotor current decay curves using Newton’s method. The numerical estimates of the equivalent circuit parameters a doubly fed induction machines with a fixed short-circuited rotor are obtained during the validation of the results on a real plant. It is along with the integral errors of deviation between the experimental rotor current decay curve and the response of the adaptive regression model. The integral errors do not exceed 4% in nearly all sections of the curves. It is considered acceptable in engineering practice. The developed algorithm for the preliminary identification for the parameters of the doubly fed induction machines substitution scheme can be applied with the configuring machines control systems, including a vector control system. Full article

The hippocampus is a critical brain structure involved in episodic memory, spatial orientation, and stress regulation. Its volumetric shrinkage is among the earliest and most reliable indicators of both physiological brain aging and pathological neurodegeneration. Accurate segmentation and measurement of the hippocampal subregions from magnetic resonance imaging (MRI) is therefore essential for neurobiological age estimation and the early identification of at-risk individuals. In this study, we present a fully automated pipeline that leverages nnU-Net, a self-configuring deep learning framework, to segment the hippocampus from high-resolution 3D T1-weighted brain MRI scans. The primary objective of this work is to enable accurate estimation of brain age through quantitative analysis of hippocampal volume. By fusing domain knowledge in neuroanatomy with data-driven learning through a highly expressive and self-optimizing model, this work advances the methodological frontier for neuroimaging-based brain-age estimation. The proposed approach demonstrates that deep learning can serve as a reliable segmentation tool as well as a foundational layer in predictive neuroscience, supporting early detection of accelerated aging and subclinical neurodegenerative processes. Full article

Emerging technologies present significant challenges for society as a whole. Among these, Artificial Intelligence (AI) stands out for its transformative potential, with the capacity to fundamentally reshape human thought, behavior, and lifestyle. This article seeks to explore the business-oriented perspective on how AI should be approached in Higher Education (HE) in order to serve the commercial objectives of companies. The motivation for this inquiry stems from recurrent criticisms directed at HE institutions, particularly their perceived inertia in adopting innovations, resistance to change, and delayed responsiveness to evolving labor market demands. In this context, the study examines what businesses deem essential for universities to provide in the context of AI familiarity and examines how companies envision future collaboration between the business sector and Higher Education institutions in using AI for business applications. Adopting a qualitative research methodology, this study conducted interviews with 16 middle-management representatives from international corporations operating across diverse industries. The data were analyzed using Gioia’s methodology, which facilitated a structured identification of first-order concepts, second-order themes, and aggregate dimensions. This analytical framework enabled a nuanced understanding of business expectations regarding the role of HE institutions in preparing graduates capable of meeting economic and commercial imperatives under the pressure of AI diffusion. Full article