Search Results (45,899)

Background: For high-grade dislocation of the acromioclavicular (AC) joint, surgical treatment is widely recommended. This study aimed to evaluate the clinical and radiological outcomes after arthroscopic-assisted stabilization of acute high-grade AC joint dislocations using a low-profile suture button (LPSB) combined with percutaneous AC cerclage fixation. A secondary objective was to quantify clavicular tunnel widening (cTW) and explore its correlation with clinical and radiological outcomes. Methods: This retrospective study included 45 patients with acute Rockwood type V injuries treated with the LPSB technique and additional AC cerclage. Clinical outcomes were the Constant Score (CS), Subjective Shoulder Value (SSV), Taft Score (TF), AC Joint Instability Score (ACJI), and VAS for pain upon palpation. Radiological assessment included coracoclavicular (CC) distance and percentage deviations compared to the contralateral side, reclassified according to Rockwood, dynamic posterior translation (DPT), cTW measurements, and assessment of ossifications and AC joint osteoarthritis. Results: After 35.3 months, significant improvements were observed in CC distance and percentage deviation. A total of 27.3% were reclassified as Rockwood type III and 2.3% as type V. Initial overreduction persisted in 18.2%. DPT was observed in 34.1% of cases. The mean CS was 89.64, the SSV was 91.1, and the VAS was 0.8. cTW occurred only below the superior button and increased significantly over time, showing a negative correlation with the SSV but no correlation with any radiological outcome parameter. No implant-related revision surgery was reported. Conclusions: Arthroscopic-assisted stabilization of acute high-grade AC joint dislocations using the LPSB technique with AC cerclage fixation provides excellent clinical outcomes and high patient satisfaction, with minimal implant-related complications and no need for revision surgery due to implant issues. Although cTW occurs, its clinical impact appears limited within this procedure. Full article

Chronic kidney disease is a systemic disease of multiple factors and slow progression, and is now becoming a rapidly changing global pathological problem affecting healthcare systems. Anyone who can go through diagnosis before getting to stage 5 Chronic Kidney Disease (CKD) or end stage renal failure has a better shot at the result. This work involves 1659 patient records and dependent variables include demographics, lifestyle, and clinical biochemistry of CKD. Based on the supervised techniques of machine learning which are Random Forest, K Nearest Neighbors (KNN), Logistic Regression, and Naïve Bayes, it was agreed that the performance of the model metrics such as accuracy, precision and recall would need to be used. These models were applied, evaluated by means of more or less simple effectiveness parameters including, for instance, accuracy, precision, or recall. Out of these [best algorithm] achieved [accuracy value] % of predictive accuracy in CKD, and so can be used for diagnosis of CKD in its early stage. This work offers the Framework and results in the development of data-integrated approaches in healthcare and improves the disease control and management. Full article

Periprosthetic joint infection (PJI) remains a major complication in orthopedic surgery, with accurate and timely diagnosis being essential for optimal patient management. Traditional culture-based diagnostics are often limited by suboptimal sensitivity, especially in biofilm-associated and low-virulence infections. In recent years, non-culture-based methodologies have gained prominence. Molecular techniques, such as polymerase chain reaction (PCR) and next-generation sequencing (NGS), offer enhanced detection of microbial DNA, even in culture-negative cases, and enable precise pathogen identification. In parallel, extensive research has focused on biomarkers, including systemic (e.g., C-reactive protein, fibrinogen, D-dimer), synovial (e.g., alpha-defensin, calprotectin, interleukins), and pathogen-derived markers (e.g., D-lactate), the latter reflecting metabolic products secreted by microorganisms during infection. The development of multiplex platforms now allows for the simultaneous measurement of multiple synovial biomarkers, improving diagnostic accuracy and turnaround time. Furthermore, the integration of artificial intelligence (AI) and machine learning algorithms into diagnostic workflows has opened new avenues for combining clinical, molecular, and biochemical data. These models can generate probability scores for PJI diagnosis with high accuracy, supporting clinical decision-making. While these technologies are still being validated for routine use, their convergence marks a significant step toward precision diagnostics in PJI, potentially improving early detection, reducing diagnostic uncertainty, and guiding targeted therapy. Full article

Soft tissue sarcomas (STSs) are a rare and highly heterogeneous group of tumours originating from mesenchymal cells, characterized by significant clinical and biological diversity. Due to this complexity, STSs present considerable challenges in both diagnosis and treatment, requiring an expert, multidisciplinary, and coordinated approach. To address these challenges, a multidisciplinary team of experts from the Spanish Group for Research in Sarcomas (GEIS) has developed a comprehensive, evidence-based consensus guideline that incorporates the latest advancements in biology, imaging techniques, and treatment options. The primary objective of this guideline is to provide clear and practical, up-to-date recommendations addressing the key questions that arise in the management of STSs. This approach aims to support therapeutic decision-making, enhance overall patient management, and ultimately improve clinical outcomes for patients affected by STSs. Key recommendations include the use of MRI as the preferred imaging modality prior to biopsy and ensuring that all patients are referred to specialised sarcoma centres with a multidisciplinary team for diagnosis and treatment. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

Background/Objectives: Childhood eye disorders, including refractive errors, strabismus, and amblyopia, are prevalent yet often underdiagnosed in Saudi Arabia. Limited data on barriers to pediatric eye care hinder efforts to optimize service delivery. This study aimed to identify barriers to accessing pediatric eye care and to develop consensus-based strategies for improvement. Methods: A Delphi technique involving three iterative rounds of questionnaires was conducted with a panel of 22 eye care experts across Saudi Arabia. Consensus was defined as ≥80% agreement among participants. In total, 30 statements were developed from thematic analysis of open-ended responses and a supporting literature review. Panelists rated each statement on a five-point Likert scale, and descriptive statistics were applied. Internal consistency across rounds was assessed using Cronbach’s alpha. Results: Of the 30 proposed statements, 25 (83.3%) reached consensus, with a mean agreement score of 4.45 ± 0.59. Internal consistency was high (Cronbach’s alpha = 0.92). High-priority recommendations included implementing mandatory vision screening, integrating optometrists into primary healthcare, and establishing specialized pediatric eye care centers. Other recommendations emphasized expanding mobile clinics and increasing public awareness. Areas that did not reach consensus included referral inefficiencies, adequacy of the current workforce, and school accommodations for children with visual impairment. Conclusions: This study presents the first national consensus on pediatric eye care in Saudi Arabia and provides actionable recommendations to strengthen services. The findings offer a strategic framework to guide policy, enhance workforce development, and reduce childhood visual impairment through early detection and intervention. Full article

Heart disease is the leading cause of death across the world. However, such an early prediction of heart attacks can save lives if clinical data are used to predict it accurately. For this, we use four machine learning models: Naive Bayes, Decision Tree, Random Forest and K-Nearest Neighbors (KNN) to predict heart attacks from the data of the patients. Models developed achieved an average accuracy of 65.08%; however, this paper explores the performance of these models in real world healthcare applications. Our focus is on improving model performance by improving the quality of the data, the features and hyperparameter tuning. Future directions indicate combining deep learning techniques and larger dataset for more accurate prediction. Full article

Biliary tract cancers include cholangiocarcinoma, gallbladder cancer, and ampullary cancer. Although overall rare, the incidence is increasing globally, particularly the subset of intrahepatic cholangiocarcinoma. Surgery is currently considered to be the only curative treatment approach; however, survival outcomes after surgery remain poor. Moreover, many patients already have advanced-stage, unresectable disease at the time of diagnosis. Herein, we will review the role of adjuvant radiotherapy to improve local control after surgery, the role of neoadjuvant radiotherapy to increase the proportion of patients able to undergo surgery, and the use of definitive/palliative radiotherapy to provide local control/symptom relief for patients who have inoperable disease. Most studies observed a survival benefit associated with radiotherapy, with the strongest evidence for those with high-risk disease features (e.g., positive surgical margins, lymph node involvement). However, due to the low incidence of biliary tract cancers, most existing studies are retrospective; there is very limited randomized data and prospective studies tend to have small sample sizes, underscoring the need for more high-quality research on radiotherapy for biliary tract cancers. As some studies show evidence of a dose-dependent response, further investigation into the delivery of dose-escalated radiotherapy with modern techniques such as proton therapy is warranted. Full article

Background/Objectives: The high prevalence of oral squamous cell carcinoma (OSCC) has driven the development of surgical and oncologic techniques to improve survival. Despite advancements in surgical technique and chemoradiation protocols, survival rates for locally advanced OSCC remain low due to high recurrence and metastasis. This has driven the exploration of neoadjuvant treatment protocols as a potential pathway towards improving organ-preserving resection, de-escalating adjuvant treatment, and improving overall and recurrence-free survival. Methods: This is a narrative review summarizing the current literature and ongoing trials on neoadjuvant treatment for OSCC. PubMed was searched using a snowballing technique to capture all relevant clinical trials. Results: 21 clinical trials were identified. Although neoadjuvant chemotherapy was associated with favorable pathologic outcomes, clinical trials demonstrated variable survival outcomes. In contrast, neoadjuvant immunotherapy for OSCC demonstrated improved pathologic responses and survival outcomes, with a low incidence of grade 3–4 adverse events. Conclusions: Neoadjuvant therapy in OSCC shows promise but does not yet constitute standard of care. Neoadjuvant immunotherapy has encouraging response rates and lower treatment-related toxicities in comparison to neoadjuvant chemotherapy. Although recent clinical trials have presented strong evidence to support the use of neoadjuvant immunotherapy in the treatment of locally advanced OSCC, further randomized trials are required to establish standardized neoadjuvant protocols and biomarkers to assess treatment response. Full article

Background/Objective: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques—Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration—in terms of improving PET/CT image alignment. Methods: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4–8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). Results: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. Conclusions: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments. Full article

Background: The rapid advancement of mobile health [mHealth] applications significantly improved healthcare accessibility. However, the adoption of these applications among the elderly population remains uncertain. This study aims to assess the attitudes of elderly individuals in the Aseer region, Saudi Arabia, toward mobile health applications, focusing on awareness, perceived benefits, usability concerns, and privacy issues. The findings will help guide strategies to enhance digital health adoption among older adults. Methods: A cross-sectional study was conducted among elderly individuals [≥60 years] residing in the Aseer region. A total of 500 participants were recruited using a non-probability convenient sampling technique. Data were collected through structured face-to-face interviews utilizing a validated questionnaire covering demographic characteristics, awareness, usage patterns, perceived benefits, and barriers to mHealth applications. Descriptive and inferential statistical analyses were performed using SPSS version 23.0. Results: The study included 500 elderly individuals in the Aseer region, Saudi Arabia. The majority of participants [70.4%] were aware of mobile health applications, with higher awareness among younger elderly [60–69 years, p = 0.008], individuals with higher income [p = 0.015], and those living with family [p < 0.001]. Mobile health apps were widely used, primarily for contacting healthcare providers [83.8%], managing appointments [79.4%], and medication management [79.2%]. Participants perceived these apps as highly useful, particularly for disease monitoring [mean = 4.34] and healthcare communication [mean = 4.34]. Awareness was negatively correlated with age [r = −0.109, p = 0.015], emphasizing the need for targeted educational interventions to enhance digital health adoption among older populations. Conclusions: Despite growing awareness, mHealth application usage remains limited among the elderly in the Aseer region due to digital literacy challenges and usability concerns. Addressing these barriers through user-friendly designs, targeted education, and privacy assurance measures may enhance adoption. Policymakers and healthcare providers should implement tailored interventions to promote digital health solutions for this population. Full article

Intrinsically stretchable polymer ionic conductors (PICs) hold significant application prospects in fields such as flexible sensors, energy storage devices, and wearable electronic devices, serving as promising solutions to prevent mechanical failure in flexible electronics. However, the development of PICs is hindered by an inherent trade-off between mechanical robust and electrical properties. Cellulose, renowned for its high mechanical strength, tunable chemical groups, abundant resources, excellent biocompatibility, and remarkable recyclability and biodegradability, offers a powerful strategy to decouple and enhance mechanical and electrical properties. This review presents recent advances in cellulose-based polymer ionic conductors (CPICs), which exhibit exceptional design versatility for flexible electrodes and strain sensors. We systematically discuss optimization strategies to improve their mechanical properties, electrical conductivity, and environmental stability while analyzing the key factors such as sensitivity, gauge factor, strain range, response time, and cyclic stability, where strain sensing refers to a technique that converts tiny deformations (i.e., strain) of materials or structures under external forces into measurable physical signals (e.g., electrical signals) for real-time monitoring of their deformation degree or stress state. Full article

CFRP is extensively utilized in the manufacturing of aerospace equipment owing to its distinctive properties, and hole-making processing continues to be the predominant processing method for this material. However, due to the anisotropy of CFRP, in its processing process, processing damage appears easily, such as stratification, fiber tearing, burrs, etc. These damages will seriously affect the performance of CFRP components in the service process. This work employs acoustic emission (AE) and infrared thermography (IT) techniques to analyze the characteristics of AE signals and temperature signals generated during the CFRP drilling process. Fast Fourier transform (FFT) and short-time Fourier transform (STFT) are used to process the collected AE signals. And in combination with the actual damage morphology, the material removal behavior during the drilling process and the AE signal characteristics corresponding to processing defects are studied. The results show that the time-frequency graph and root mean square (RMS) curve of the AE signal can accurately distinguish the different stages of the drilling process. Through the analysis of the frequency domain characteristics of the AE signal, the specific frequency range of the damage mode of the CFRP composite material during drilling is determined. This paper aims to demonstrate the feasibility of real-time monitoring of the drilling process. By analyzing the relationship between the RMS values of acoustic emission signals and hole surface topography under different drilling parameters, it provides a new approach for the research on online monitoring of CFRP drilling damage and improvement of CFRP machining quality. Full article

Background: The human gut microbiome—a diverse ecosystem of trillions of microorganisms—plays an essential role in metabolic, immune, and neurological regulation. However, modern lifestyle factors such as antibiotic overuse, cesarean delivery, reduced breastfeeding, processed and high-sodium diets, alcohol intake, smoking, and exposure to environmental toxins (e.g., glyphosate) significantly reduce microbial diversity. Loss of keystone species like Bifidobacterium infantis (B. infantis) and Lactobacillus reuteri (L. reuteri) contributes to gut dysbiosis, which has been implicated in chronic metabolic, autoimmune, cardiovascular, and neurodegenerative conditions. Materials and Methods: This Qualitative Systematic Review (QualSR) synthesized data from over 547 studies involving human participants and standardized microbiome analysis techniques, including 16S rRNA sequencing and metagenomics. Studies were reviewed for microbial composition, immune and metabolic biomarkers, and clinical outcomes related to microbiome restoration strategies. Results: Multiple cohort studies have consistently reported a 40–60% reduction in microbial diversity among Western populations compared to traditional societies, particularly affecting short-chain fatty acid (SCFA)-producing bacteria. Supplementation with B. infantis is associated with a significant reduction in systemic inflammation—including a 50% decrease in C-reactive protein (CRP) and reduced tumor necrosis factor-alpha (TNF-α) levels—alongside increases in regulatory T cells and anti-inflammatory cytokines interleukin-10 (IL-10) and transforming growth factor-beta 1 (TGF-β1). L. reuteri demonstrates immunomodulatory and neurobehavioral benefits in preclinical models, while both probiotics enhance epithelial barrier integrity in a strain- and context-specific manner. In murine colitis, B. infantis increases ZO-1 expression by ~35%, and L. reuteri improves occludin and claudin-1 localization, suggesting that keystone restoration strengthens barrier function through tight-junction modulation. Conclusions: Together, these findings support keystone species restoration with B. infantis and L. reuteri as a promising adjunctive strategy to reduce systemic inflammation, reinforce gut barrier integrity, and modulate gut–brain axis (GBA) signaling, indicating translational potential in metabolic and neuroimmune disorders. Future research should emphasize personalized microbiome profiling, long-term outcomes, and transgenerational effects of early-life microbial disruption. Full article

Partial discharge (PD) signals are used to evaluate the insulation condition of stators in electrical machines. Their measurements are often heavily corrupted by ambient noise, making denoising essential for effective detection and analysis of PD signals. Wavelet thresholding techniques are widely applied to denoise PD signals. However, existing hard and soft thresholding functions introduce oscillation or deviation into PD signals after wavelet reconstruction, particularly under high-noise conditions. This paper proposes an improved thresholding function for the wavelet threshold denoising method that effectively overcomes the oscillation issue associated with the hard thresholding function and the constant deviation of the soft thresholding function. Additionally, wavelet basis selection based on the correlation coefficient and an adaptive threshold value is integrated with the improved thresholding function to implement the wavelet threshold denoising method. The proposed technique is applied to both simulated and real-world measured PD signals to evaluate its performance across different signal-to-noise ratio (SNR) levels. Compared with traditional soft and hard thresholding functions, simulation results confirm the superiority of the improved thresholding function, especially under high-noise conditions. At an input Gaussian noise level of −10 dB, the proposed method yielded an SNR that was 1.20 dB and 2.66 dB higher than those of the hard and soft thresholding functions, respectively. Full article