Peri-implantitis is a prevalent complication in dental implantology, often resulting from bacterial contamination on implant surfaces. Researchers have explored diode lasers as a promising alternative for bacterial decontamination. In this study, we evaluated the effectiveness of the 810 nm diode laser in eliminating Staphylococcus aureus from titanium implant surfaces. This investigation was designed as a controlled, standardized in vitro laboratory experiment. A total of 95 titanium implants were divided into the following groups: (1) A negative control group (n = 5): New integrity implants; (2) a positive control group (n = 6): Implants contaminated with a S. aureus suspension and left untreated; and (3) four intervention groups (n = 21 for each group): Implants contaminated and then treated with the 810 nm diode laser at varying power settings (1 W, 1.5 W, 2 W, 2.5 W) for 3 minutes. Bacterial load was quantified using colony-forming unit analysis, while surface roughness and morphology were assessed with a surface roughness tester and scanning electron microscopy (SEM) after laser treatment. The results showed that all intervention groups demonstrated significant reductions in bacterial load compared to the positive control group, with bacterial reduction increasing with higher laser power. The 2.5 W settings proved most effective in decontamination, although none of the laser settings completely eradicated the bacteria. Laser treatment did not result in significant changes in implant surface roughness, suggesting its safety for use in surface decontamination without compromising implant integrity. This technology may represent a safer, more effective approach to implant surface decontamination than traditional methods.
Drug classification is a key task in medical decision-making, and this process can be supported through appropriate selection of drugs that fit patient attributes and medical history. Currently, state-of-the-art solutions for this problem tend to make use of attribute-based representation and may overlook the relational structure that could exist in the data points. This paper seeks to examine a machine learning algorithm for multiclass drug classification, where the target variable is the prescribed type of drug, using a graph-based feature extraction method. The proposed method can be used thorough data preprocessing strategies, including handling imbalance and removal of outliers. Evaluation of the method requires a 10-fold cross-validation strategy to achieve a genuine and impartial evaluation. In each training set, a similarity graph is created and graph-based attributes such as degree centrality measures and clustering coefficient are extracted by using three different distance measures to consider graph relationships between samples. In the case of the testing data, graph attributes are computed by supplanting a weighted k-nearest neighbor strategy, wherein there is absolute prevention of information leakage. The graph attributes obtained along with other attributes are used to train different machine learning classifiers. The results show that the performance across all distance measures plays a great role for such classifications.
Comprehensive biochemical analysis is an essential part of clinical diagnostics, pharmaceutical development, and biomedical research. The gold-standard technique for such analysis is spectrophotometry, which has demonstrated excellent reliability, quantitative accuracy, and broad biomolecular sensitivity. Commercial micro-volume spectrophotometers, which are instrumental to applications with limited sample volumes, are often restricted to well-equipped laboratories due to the high cost and portability limitations especially in resource-limited settings. To address this gap, we propose a proof-of-concept, 3D-printed micro-volume spectrophotometer that integrates precision optics, microfluidic sample handling (8-15 & micro;L), combined with spectral reconstruction and machine learning-based concentration estimation algorithm in a compact and affordable format. Preliminary validation of the proposed system based on bovine serum albumin (BSA) protein quantification via Biuret assay demonstrates strong agreement with commercial spectrophotometers (R2 > 0.91). With a production cost of approximately $36 and rapid measurement, the architecture provides a framework that can be extended to other absorbance-based biochemical assays ensuring accessible spectrophotometric systems for point-of-care and resource-limited applications, overcoming traditional limits of cost, sample volume, and portability.
To clarify the specific effects of sports biomechanical regulation on improving technical performance and preventing sports injuries in rugby and soccer players, this study employed a randomized controlled trial design. A total of 48 rugby players (24 males and 24 females) and 40 soccer players (20 males and 20 females) were enrolled and randomly assigned to an experimental group (receiving an 8-week personalized biomechanical intervention) and a control group (undergoing conventional training) by gender stratification. The intervention protocol was developed and dynamically optimized based on high-precision kinematic and kinetic data collected by a **multichannel synchronous sensor system**: 8 infrared high-speed motion capture cameras (sampling frequency: 200 Hz) were used to obtain the 3D motion trajectories of the athletes' lower limb joints; 16-channel wireless surface electromyography (sEMG) sensors (sampling frequency: 1500 Hz) were applied to monitor the activation timing and amplitude of the quadriceps femoris, hamstrings, and lateral ankle muscle groups; and a 3D force platform (1000 Hz) was utilized to synchronously record ground reaction forces and lower limb joint torque data. The core of the intervention focused on optimizing the angular and torque parameters of the hip, knee, and ankle joints. Statistical analyses were performed using repeated-measures analysis of variance (ANOVA) and independent samples ttests. The intraclass correlation coefficient (ICC) was used to assess the reliability of coaches' scores, with ICC values ranging from 0.89 to 0.93. For technical improvement, the average skill scores of male/female rugby players in the experimental group increased from 57.83 +/- 5.31 / 55.33 +/- 2.87 to 68.42 +/- 5.35 / 65.33 +/- 3.67 (all P < 0.001); those of male/female soccer players rose from 41.85 +/- 5.72 / 49.70 +/- 5.13 to 58.75 +/- 5.28 / 74.35 +/- 6.89 (all P < 0.001). Time-frequency analysis based on sEMG sensors revealed that the percentage of myoelectric activity in key muscle groups was significantly higher in the experimental group than in the control group (e.g., a 7% increase in males and 3% in females for the lateral ankle muscle group of rugby players, P < 0.05), indicating optimized muscle activation efficiency. Regarding injury prevention, after the intervention, the hip frontal torque of female rugby players in the experimental group decreased to 8.73 +/- 0.32 N & centerdot;m (control group: 9.14 +/- 0.41 N & centerdot;m, P = 0.03), and the ankle coronal torque of male soccer players decreased by 8.6% (P = 0.02). The injury incidence during the intervention period was significantly lower in the experimental group (3.1%) than in the control group (15.6%, P = 0.01). This study confirms that multi-sensor fusion-based sports biomechanical intervention can simultaneously improve technical performance and reduce injury risk in rugby and soccer players by optimizing lower limb joint mechanical parameters and muscle activation patterns. It provides quantitative sensor data support and a precision intervention basis from an engineering perspective for the formulation of gender-specific specialized training programs.
Wearable devices are widely utilized in the field of health monitoring. Given the real-time requirements of wearable devices for dynamic tracking of muscle fatigue, and addressing the issues of prolonged computation time and failure to extract fatigue-related modal information when applying the variational mode decomposition (VMD) algorithm to surface electromyography (sEMG) signals, this paper proposes the fast VMD (FVMD) algorithm. The objective was to rapidly decompose fatigue-related modal information and extract a complete fatigue curve to alert users. The proposed algorithm is an engineering acceleration of the VMD algorithm. FVMD extends the original signal and applies the Fourier transform to convert the time-domain variational problem into the frequency domain. The optimization problem was constrained to the positive frequency range to simplify calculations while leveraging the unilateral spectrum characteristics to streamline optimization and focus on narrowband modes. The alternating direction method of multipliers framework was employed to decompose the problem into subproblems solvable in closed form, with modal updates inspired by Wiener filtering. The Lagrange multipliers were iteratively updated, and convergence criteria were established to ensure stability. The time-domain signal was reconstructed via the inverse Fourier transform. According to the experimental results, the proposed algorithm exhibits a substantial improvement in processing time compared to the original algorithm and other enhanced algorithms. Compared with other VMD variant algorithms using the same experimental data, the fast recursive VMD (FRVMD) algorithm takes 9.93 s. In contrast, the FVMD method can complete the same task in a shorter time. An evaluation metric was used to select the muscle fatigue modal component most correlated with the original signal and extract the muscle fatigue curve. The FVMD algorithm enhances computational efficiency, overcoming the computational limitations of wearable devices, and provides reliable technical support for real-time muscle fatigue quantification and early warning in scenarios such as sports rehabilitation and occupational health.
Aptamers, including nucleic acid and peptide aptamers, are small biological molecules whose development has consistently represented the forefront of science and technology. With advances in synthetic biology, bioinformatics, and cell biology, alongside the integration of multidisciplinary approaches, researchers have been able to construct aptamers of diverse structures and functions based on peptide self-assembly, thereby continuously driving innovation in this field. The maturation of various synthesis techniques has further facilitated the gradual translation of aptamers into the market. Supported by the establishment of aptamer information libraries, as well as their inherent excellent affinity and specificity, aptamers can now be synthesized, chemically modified, and applied across a broad spectrum of biomedical scenarios. They function not only as therapeutic agents and diagnostic probes, but also as biosensing tools and delivery vehicles for other drugs. These characteristics underscore the significance of aptamer development within the field of molecular recognition. In this paper, we conduct a comprehensive review of various research directions centered on their targeting properties, including their use as therapeutic and diagnostic agents, biosensors, platforms for new drug development, and drug delivery vehicles.
Research into biodegradable polymers as sustainable alternatives to traditional petrochemical plastics has increased significantly in response to the growing environmental impact of plastic pollution. In this review, we offer a comprehensive, multidisciplinary overview of current advances in the creation, degradation processes, and green energy applications of biodegradable polymers. We examined how chemical structure, environmental factors, and microbiological activity influence polymer breakdown, alongside controlled degradation and lifecycle optimization. Consideration was given to incorporating biodegradable materials into next-generation energy devices such as transient batteries, triboelectric nanogenerators, and supercapacitors. A comparative analysis highlighted the material properties, performance trade-offs, and environmental impacts of key polymers like PLA, PCL, cellulose, and chitosan. Emerging trends were explored within regulatory support and circular economy frameworks, including smart polymers, nanocomposites, and AI-driven material design. In the review, we also emphasized key challenges and future research directions necessary for practical implementation, demonstrating the potential of biodegradable polymers to enable scalable, environmentally friendly solutions across energy and material sectors.
BackgroundBone regeneration is a critical area of regenerative medicine that faces significant challenges, such as bone defects and fractures. 3D printing offers a promising solution through customized scaffolds that mimic the natural architecture of bone and support tissue healing. Polylactic acid (PLA) is a biodegradable and biocompatible polymer widely used in biomedical 3D printing. Preclinical animal models are essential to evaluate the performance of PLA-based scaffolds before their clinical use.ObjectiveThis systematic review aimed to assess the current applications of 3D-printed PLA scaffolds for bone regeneration in animal models, focusing on PLA, animal models, biological performance, and in vivo outcomes.MethodsA comprehensive search was conducted across databases, covering studies published between January 2009 and January 2025, following the PRISMA guidelines. Studies were included if they reported 3D-printed PLA scaffold constructs for bone regeneration that were validated in animal models. Data on the animal species, defect types, biomaterials, and outcomes were extracted and analyzed.ResultsThis review included 38 studies that used animal models, such as rodents, rabbits, canines, and sheep, to assess the performance of the 3D-printed PLA scaffolds. Cells and compounds such as hydroxyapatite, drugs, nanoparticles, proteins, and polymers enable active scaffold fabrication that enhances regeneration from 1 to 12 weeks on the defect created in the chosen animal model.Conclusion3D printing based on PLA offers significant potential for advancing bone regeneration, with promising preclinical outcomes in animal models. Further preclinical and clinical studies are required to confirm the safety, effectiveness, and scalability for human applications.
Diabetes is a chronic disorder that is among the most prevalent diseases in many parts of the world, as it is brought about by high levels of sugar in the blood, which may cause severe complications in the heart, blood vessels, kidneys, and nerves. Thus, it is important to monitor blood glucose continuously. The use of traditional finger-prick methods was done away with in this study, and it was substituted with a noninvasive blood glucose meter. The proposed system has an optical sensor known as the MAX30100, an LCD, and an Arduino Mega 2560 microprocessor to provide real-time measurements. The device can determine the glucose content through a combination of digital filtering and mathematical computations implemented within the microcontroller through the correlation of heart rate (HR) and oxygen saturation (SpO(2)). The 120 samples (females and males, fasting, normal, and diabetic) were tested with the system and compared with a commercial reference device (Accu-Chek). There were high accuracy levels of 97.5% agreement, a sensitivity of 97.94%, and a specificity of 95.65%. Strong correlations were found. HR was negatively correlated with SpO(2) (r = -0.936, p < 0.001) and positively correlated with glucose (R-2 = 0.860, p < 0.001). The validity and clinical reliability of the system were validated by statistical methods such as the Clarke error grid, Bland-Altman test, and error test. The suggested approach showed promise as a practical and affordable substitute for regular blood glucose monitoring, and it achieved greater accuracy than that in earlier research.
This paper addresses the main methods of chemical analysis of gunshot residues (GSRs), highlighting both their social and forensic relevance. Forensic authorities have determined that crimes involving firearms, including homicides and suicides, constitute a significant portion of the cases examined. In these cases, GSR plays a central role in the reconstruction of crimes. Classical instrumental techniques such as atomic absorption spectroscopy (AAS) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) remain widely used; however, recent advances have introduced innovative approaches including electrochemical sensors, portable devices, and luminescent metal-organic frameworks (MOFs). The objective of these novel methodologies is to enhance sensitivity, selectivity, and accessibility in forensic analysis. The objective of this article is to provide a critical overview of the historical development, current practices, and recent technological innovations in GSR detection. The text places particular emphasis on the challenges posed by heavy-metal-free "green" ammunition and highlights perspectives where electrochemistry, chemical markers, and artificial intelligence (AI) can enhance the robustness of forensic investigations. The primary findings indicate that the amalgamation of nanomaterials, portable platforms, and chemometric instruments possesses the capacity to transform the domain of GSR analysis, thereby enhancing the reliability of forensic evidence and fortifying its application within the justice system.
