Bioengineering, Volume 11, Issue 5 (May 2024) – 65 articles

Effective oral care is important for maintaining a high quality of life. Therefore, plaque control can prevent the development and recurrence of periodontitis. Brushing with a toothbrush and toothpaste is a common way to remove plaque; however, excessive brushing or brushing with abrasive toothpaste can cause wear and tear on the dental crown. Hence, we aimed to quantitatively compare the plaque-removal efficiency and tooth wear of toothbrushes using the bioelectric effect (BE) with those of electric–mechanical toothbrushes. To generate the BE signal, an electronic circuit was developed and embedded in a toothbrush. Further, typodonts were coated with cultured artificial plaques and placed in a brushing simulator. A toothpaste slurry was applied, and the typodonts were eluted with tap water after brushing. The plaques of the typodonts were captured, and the images were quantified. For the tooth wear experiment, polymethyl methacrylate disk resin blocks were brushed twice a day, and the thickness of the samples was measured. Subsequently, statistical differences between the experimental toothbrushes and typical toothbrushes were analyzed. The BE toothbrush had a higher plaque-removal efficiency and could minimize tooth wear. This study suggests that the application of BE may be a new solution for oral care. Full article

In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes. Full article

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes. Full article

This study explores an approach to design and prepare a multilayer scaffold mimicking interstratified natural tissue. This multilayer construct, composed of chitosan matrices with graded nanohydroxyapatite concentrations, was achieved through an in situ biomineralization process applied to individual layers. Three distinct precursor concentrations were considered, resulting in 10, 20, and 30 wt% nanohydroxyapatite content in each layer. The resulting chitosan/nanohydroxyapatite (Cs/n-HAp) scaffolds, created via freeze-drying, exhibited nanohydroxyapatite nucleation, homogeneous distribution, improved mechanical properties, and good cytocompatibility. The cytocompatibility analysis revealed that the Cs/n-HAp layers presented cell proliferation similar to the control in pure Cs for the samples with 10% n-HAp, indicating good cytocompatibility at this concentration, while no induction of apoptotic death pathways was demonstrated up to a 20 wt% n-Hap concentration. Successful multilayer assembly of Cs and Cs/n-HAp layers highlighted that the proposed approach represents a promising strategy for mimicking multifaceted tissues, such as osteochondral ones. Full article

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices. Full article

A recent author’s fractal fluid-dynamic dispersion theory in porous media has focused on the derivation of the associated nonergodic (or effective) macrodispersion coefficients by a 3-D stochastic Lagrangian approach. As shown by the present study, the Fickian (i.e., the asymptotic constant) component of a properly normalized version of these coefficients exhibits a clearly detectable minimum in correspondence with the same fractal dimension (d ≅ 1.7) that seems to characterize the diffusion-limited aggregation state of cells in advanced stages of cancerous lesion progression. That circumstance suggests that such a critical fractal dimension, which is also reminiscent of the colloidal state of solutions (and may therefore identify the microscale architecture of both living and non-living two-phase systems in state transition conditions) may actually represent a sort of universal nature imprint. Additionally, it suggests that the closed-form analytical solution that was provided for the effective macrodispersion coefficients in fractal porous media may be a reliable candidate as a physically-based descriptor of blood perfusion dynamics in healthy as well as cancerous tissues. In order to evaluate the biological meaningfulness of this specific fluid-dynamic parameter, a preliminary validation is performed by comparison with the results of imaging-based clinical surveys. Moreover, a multifractal extension of the theory is proposed and discussed in view of a perspective interpretative diagnostic utilization. Full article

Cervical cancer is a major health concern worldwide, highlighting the urgent need for better early detection methods to improve outcomes for patients. In this study, we present a novel digital pathology classification approach that combines Low-Rank Adaptation (LoRA) with the Vision Transformer (ViT) model. This method is aimed at making cervix type classification more efficient through a deep learning classifier that does not require as much data. The key innovation is the use of LoRA, which allows for the effective training of the model with smaller datasets, making the most of the ability of ViT to represent visual information. This approach performs better than traditional Convolutional Neural Network (CNN) models, including Residual Networks (ResNets), especially when it comes to performance and the ability to generalize in situations where data are limited. Through thorough experiments and analysis on various dataset sizes, we found that our more streamlined classifier is highly accurate in spotting various cervical anomalies across several cases. This work advances the development of sophisticated computer-aided diagnostic systems, facilitating more rapid and accurate detection of cervical cancer, thereby significantly enhancing patient care outcomes. Full article

Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention. Full article

Background: The morphology and internal composition, particularly the nucleus-to-cross sectional area (NP-to-CSA) ratio of the lumbar intervertebral discs (IVDs), is important information for finite element models (FEMs) of spinal loadings and biomechanical behaviors, and, yet, this has not been well investigated and reported. Methods: Anonymized MRI scans were retrieved from a previously established database, including a total of 400 lumbar IVDs from 123 subjects (58 F and 65 M). Measurements were conducted manually by a spine surgeon and using two computer-assisted segmentation algorithms, i.e., fuzzy C-means (FCM) and region growing (RG). The respective results were compared. The influence of gender and spinal level was also investigated. Results: Ratios derived from manual measurements and the two computer-assisted algorithms (FCM and RG) were 46%, 39%, and 38%, respectively. Ratios derived manually were significantly larger. Conclusions: Computer-assisted methods provide reliable outcomes that are traditionally difficult for the manual measurement of internal composition. FEMs should consider the variability of NP-to-CSA ratios when studying the biomechanical behavior of the spine. Full article

The application of calcium coacervates (CCs) may hold promise for dental hard tissue remineralization. The aim of this study was to evaluate the effect of the infiltration of artificial enamel lesions with a CC and its single components including polyacrylic acid (PAA) compared to that of the self-assembling peptide P₁₁-4 in a pH-cycling (pHC) model. Enamel specimens were prepared from bovine incisors, partly varnished, and stored in demineralizing solution (DS; pH 4.95; 17 d) to create two enamel lesions per sample. The specimens were randomly allocated to six groups (n = 15). While one lesion per specimen served as the no-treatment control (NTC), another lesion (treatment, T) was etched (H₃PO₄, 5 s), air-dried and subsequently infiltrated for 10 min with either a CC (10 mg/mL PAA, 50 mM CaCl₂ (Ca) and 1 M K₂HPO₄ (PO₄)) (groups CC and CC + DS) or its components PAA, Ca or PO₄. As a commercial control, the self-assembling peptide P₁₁-4 (Curodont^TM Repair, Credentis, Switzerland) was tested. The specimens were cut perpendicularly to the lesions, with half serving as the baseline (BL) while the other half was exposed to either a demineralization solution for 20 d (pH 4.95; group CC + DS) or pHC for 28 d (pH 4.95, 3 h; pH 7, 21 h; all five of the other groups). The difference in integrated mineral loss between the lesions at BL and after the DS or pHC, respectively, was analyzed using transversal microradiography (ΔΔZ = ΔZ_pHC − ΔZ_baseline). Compared to the NTC, the mineral gain in the T group was significantly higher in the CC + DS, CC and PAA (p < 0.05, Wilcoxon). In all of the other groups, no significant differences between treated and untreated lesions were detected (p > 0.05). Infiltration with the CC and PAA resulted in a consistent mineral gain throughout the lesion body. The CC as well as its component PAA alone promoted the remineralization of artificial caries lesions in the tested pHC model. Infiltration with PAA further resulted in mineral gain in deeper areas of the lesion body. Full article

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals’ haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system. Full article

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97–97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865–0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs. Full article

Introduction: Chronic wounds caused by diabetes or lower-extremity artery disease are intractable because the wound healing mechanism becomes ineffective due to the poor environment of the wound bed. Biosheets obtained using in-body tissue architecture (iBTA) are collagen-based membranous tissue created within the body and which autologously contain various growth factors and somatic stem cells including SSEA4-posituve cells. When applied to a wound, granulation formation can be promoted and epithelialization may even be achieved. Herein, we report our clinical treatment experience with seven cases of intractable diabetic foot ulcers. Cases: Seven patients, from 46 to 93 years old, had large foot ulcers including in the heel area, which were failing to heal with standard wound treatment. Methods: Two or four Biosheet-forming molds were embedded subcutaneously in the chest or abdomen, and after 3 to 6 weeks, the molds were removed. Biosheets that formed inside the mold were obtained and applied directly to the wound surface. Results: In all cases, there were no problems with the mold’s embedding and removal procedures, and Biosheets were formed without any infection or inflammation during the embedding period. The Biosheets were simply applied to the wounds, and in all cases they adhered within one week, did not fall off, and became integrated with the wound surface. Complete wound closure was achieved within 8 weeks in two cases and within 5 months in two cases. One patient was lost due to infective endocarditis from septic colitis. One case required lower leg amputation due to wound recurrence, and one case achieved wound reduction and wound healing in approximately 9 months. Conclusions: Biosheets obtained via iBTA promoted wound healing and were extremely useful for intractable diabetic foot ulcers involving the heel area. Full article

Spinal cord injury (SCI) represents a severe trauma to the nervous system, leading to significant neurological damage, chronic inflammation, and persistent neuropathic pain. Current treatments, including pharmacotherapy, immobilization, physical therapy, and surgical interventions, often fall short in fully addressing the underlying pathophysiology and resultant disabilities. Emerging research in the field of regenerative medicine has introduced innovative approaches such as autologous orthobiologic therapies, with bone marrow aspirate (BMA) being particularly notable for its regenerative and anti-inflammatory properties. This review focuses on the potential of BMA to modulate inflammatory pathways, enhance tissue regeneration, and restore neurological function disrupted by SCI. We hypothesize that BMA’s bioactive components may stimulate reparative processes at the cellular level, particularly when applied at strategic sites like the sacral hiatus to influence lumbar centers and higher neurological structures. By exploring the mechanisms through which BMA influences spinal repair, this review aims to establish a foundation for its application in clinical settings, potentially offering a transformative approach to SCI management that extends beyond symptomatic relief to promoting functional recovery. Full article

Recognizing the growing interests and benefits of technology-assisted interactive telerehabilitation in various populations, the aim of this review is to systematically review the effects of interactive telerehabilitation with remote monitoring and guidance for improving balance and gait performance in older adults and individuals with neurological conditions. The study protocol for this systematic review was registered with the international prospective register of systematic reviews (PROSPERO) with the unique identifier CRD42024509646. Studies written in English published from January 2014 to February 2024 in Web of Science, Pubmed, Scopus, and Google Scholar were examined. Of the 247 identified, 17 were selected after initial and eligibility screening, and their methodological quality was assessed with the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies. All 17 studies demonstrated balance and gait performance improvement in older adults and in individuals with stroke, Parkinson’s disease, and multiple sclerosis following 4 or more weeks of interactive telerehabilitation via virtual reality, smartphone or tablet apps, or videoconferencing. The findings of this systematic review can inform the future design and implementation of interactive telerehabilitation technology and improve balance and gait training exercise regimens for older adults and individuals with neurological conditions. Full article

The study aimed to investigate overt reading and naming processes in adult people with dyslexia (PDs) in shallow (transparent) language orthography. The results of adult PDs are compared with adult healthy controls HCs. Comparisons are made in three phases: pre-lexical (150–260 ms), lexical (280–700 ms), and post-lexical stage of processing (750–1000 ms) time window. Twelve PDs and HCs performed overt reading and naming tasks under EEG recording. The word reading and naming task consisted of sparse neighborhoods with closed phonemic onset (words/objects sharing the same onset). For the analysis of the mean ERP amplitude for pre-lexical, lexical, and post-lexical time window, a mixed design ANOVA was performed with the right (F4, FC2, FC6, C4, T8, CP2, CP6, P4) and left (F3, FC5, FC1, T7, C3, CP5, CP1, P7, P3) electrode sites, within-subject factors and group (PD vs. HC) as between-subject factor. Behavioral response latency results revealed significantly prolonged reading latency between HCs and PDs, while no difference was detected in naming response latency. ERP differences were found between PDs and HCs in the right hemisphere’s pre-lexical time window (160–200 ms) for word reading aloud. For visual object naming aloud, ERP differences were found between PDs and HCs in the right hemisphere’s post-lexical time window (900–1000 ms). The present study demonstrated different distributions of the electric field at the scalp in specific time windows between two groups in the right hemisphere in both word reading and visual object naming aloud, suggesting alternative processing strategies in adult PDs. These results indirectly support the view that adult PDs in shallow language orthography probably rely on the grapho-phonological route during overt word reading and have difficulties with phoneme and word retrieval during overt visual object naming in adulthood. Full article

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios. Full article

Background: Long-distance running is popular but associated with a high risk of injuries, particularly toe-related injuries. Limited research has focused on preventive measures, prompting exploration into the efficacy of raised toe box running shoes. Purpose: This study aimed to investigate the effect of running shoes with raised toe boxes on preventing toe injuries caused by distance running. Methods: A randomized crossover design involved 25 male marathon runners (height: 1.70 ± 0.02 m, weight: 62.6 + 4.5 kg) wearing both raised toe box (extended by 8 mm along the vertical axis and 3 mm along the sagittal axis) and regular toe box running shoes. Ground reaction force (GRF), in-shoe displacement, and degree of toe deformation (based on the distance change between the toe and the metatarsal head) were collected. Results: Wearing raised toe box shoes resulted in a significant reduction in vertical (p = 0.001) and antero–posterior (p = 0.015) ground reaction forces during the loading phase, with a notable increase in vertical ground reaction force during the toe-off phase (p < 0.001). In-shoe displacement showed significant decreased movement in the forefoot medial (p < 0.001) and rearfoot (medial: p < 0.001, lateral: p < 0.001) and significant increased displacement in the midfoot (medial: p = 0.002, lateral: p < 0.001). Impact severity on the hallux significantly decreased (p < 0.001), while impact on the small toes showed no significant reduction (p = 0.067). Conclusions: Raised toe box running shoes offer an effective means of reducing toe injuries caused by long-distance running. Full article

The survival rate of cardiac arrest (CA) can be improved by utilizing percutaneous left ventricular assist devices (pLVADs) instead of conventional chest compressions. However, existing pLVADs require complex fluoroscopy-guided placement along a guidewire and suffer from limited blood flow due to their cross-sectional area. The recently developed self-expandable Impella CP (ECP) pLVAD addresses these limitations by enabling guidewire-free placement and increasing the pump cross-sectional area. This study evaluates the feasibility of resuscitation using the Impella ECP in a swine CA model. Eleven anesthetized pigs (73.8 ± 1.7 kg) underwent electrically induced CA, were left untreated for 5 min and then received pLVAD insertion and activation. Vasopressors were administered and defibrillations were attempted. Five hours after the return of spontaneous circulation (ROSC), the pLVAD was removed, and animals were monitored for an additional hour. Hemodynamics were assessed and myocardial function was evaluated using echocardiography. Successful guidewire-free pLVAD placement was achieved in all animals. Resuscitation was successful in 75% of cases, with 3.5 ± 2.0 defibrillations and 1.8 ± 0.4 mg norepinephrine used per ROSC. Hemodynamics remained stable post-device removal, with no adverse effects or aortic valve damage observed. The Impella ECP facilitated rapid guidewire-free pLVAD placement in fibrillating hearts, enabling successful resuscitation. These findings support a broader clinical adoption of pLVADs, particularly the Impella ECP, for CA. Full article

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician’s manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences. Full article

Ralstonia eutropha strain H16 is a chemoautotrophic bacterium that oxidizes hydrogen and accumulates poly[(R)-3-hydroxybutyrate] [P(3HB)], a prominent polyhydroxyalkanoate (PHA), within its cell. R. eutropha utilizes fructose or CO₂ as its sole carbon source for this process. A PHA-negative mutant of strain H16, known as R. eutropha strain PHB⁻4, cannot produce PHA. Strain 1F2, derived from strain PHB⁻4, is a leucine analog-resistant mutant. Remarkably, the recombinant 1F2 strain exhibits the capacity to synthesize 3HB-based PHA copolymers containing 3-hydroxyvalerate (3HV) and 3-hydroxy-4-methyvalerate (3H4MV) comonomer units from fructose or CO_2. This ability is conferred by the expression of a broad substrate-specific PHA synthase and tolerance to feedback inhibition of branched amino acids. However, the total amount of comonomer units incorporated into PHA was up to around 5 mol%. In this study, strain 1F2 underwent genetic engineering to augment the comonomer supply incorporated into PHA. This enhancement involved several modifications, including the additional expression of the broad substrate-specific 3-ketothiolase gene (bktB), the heterologous expression of the 2-ketoacid decarboxylase gene (kivd), and the phenylacetaldehyde dehydrogenase gene (padA). Furthermore, the genome of strain 1F2 was altered through the deletion of the 3-hydroxyacyl-CoA dehydrogenase gene (hbdH). The introduction of bktB-kivd-padA resulted in increased 3HV incorporation, reaching 13.9 mol% from fructose and 6.4 mol% from CO₂. Additionally, the hbdH deletion resulted in the production of PHA copolymers containing (S)-3-hydroxy-2-methylpropionate (3H2MP). Interestingly, hbdH deletion increased the weight-average molecular weight of the PHA to over 3.0 × 10⁶ on fructose. Thus, it demonstrates the positive effects of hbdH deletion on the copolymer composition and molecular weight of PHA. Full article

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions’ decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types. Full article

Postmortem human eyes were subjected to optic nerve (ON) traction in adduction and elevated intraocular pressure (IOP) to investigate scleral surface deformations. We incrementally adducted 11 eyes (age 74.1 ± 9.3 years, standard deviation) from 26° to 32° under normal IOP, during imaging of the posterior globe, for analysis by three-dimensional digital image correlation (3D-DIC). In the same eyes, we performed uniaxial tensile testing in multiple regions of the sclera, ON, and ON sheath. Based on individual measurements, we analyzed eye-specific finite element models (FEMs) simulating adduction and IOP loading. Analysis of 3D-DIC showed that the nasal sclera up to 1 mm from the sheath border was significantly compressed during adduction. IOP elevation from 15 to 30 mmHg induced strains less than did adduction. Tensile testing demonstrated ON sheath stiffening above 3.4% strain, which was incorporated in FEMs of adduction tethering that was quantitatively consistent with changes in scleral deformation from 3D-DIC. Simulated IOP elevation to 30 mmHg did not induce scleral surface strains outside the ON sheath. ON tethering in incremental adduction from 26° to 32° compressed the nasal and stretched the temporal sclera adjacent to the ON sheath, more so than IOP elevation. The effect of ON tethering is influenced by strain stiffening of the ON sheath. Full article

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts. Full article

Aim: The purpose of the present study is the three-dimensional (3D) analysis of molar and incisor movements that occur during the correction of the upper midline deviation by using the Mesial-Distalslider appliance. Materials and Methods: A total of 20 consecutive patients (12 women and 8 men; mean age 19.6 ± 11.1 years) were selected from the Orthodontic Department of Heinrich-Heine University of Düsseldorf. To correct the upper midline deviation (>2 mm), the patients were treated with asymmetric mechanics (mesialization on one side and distalization on the contralateral side) with the aid of Mesial-Distalslider. Dental casts were taken for each patient before (T0) and after the treatment (T1). The casts were 3D digitized and the models were superimposed on the palatal anterior region. Three-dimensional molar movements and sagittal incisor movements (proclination and retroclination) were assessed for T0 and T1. Results: At the end of the treatment, the total movements of the molars resulted in 4.5 ± 2.2 mm (antero-posterior direction), −0.4 ± 2.4 mm (transverse direction) and 0.3 ± 0.9 mm (vertical direction) on the mesialization side, and −2.4 ± 1.7 mm (antero-posterior direction), −0.5 ± 1.5 mm (transverse direction) and 0.2 ± 1.4 mm (vertical direction) on the distalization side. Incisor displacement was 0.9 mm ± 1.7 (mesialization side) and 0.6 mm ± 0.7 (distalization side). Conclusion: The Mesial-Distalslider appliance could be considered a valuable tool in orthodontic treatment for upper midline correction. Within the limits of a retrospective study, asymmetric molar movements appeared possible without clinically relevant anchorage loss. Full article

Optical coherence tomography (OCT) is widely used to probe retinal structure and function. This study investigated the outer retina band (ORB) pattern and reflective intensity for the region between bands 2 and 3 (Dip) in three mouse models of inherited retinal degeneration (Rs1KO, TTLL5KO, RPE65KO) and in human AMD patients from the A2A database. OCT images were manually graded, and reflectivity signals were used to calculate the Dip ratio. Qualitative analyses demonstrated the progressive merging band 2 and band 3 in all three mouse models, leading to a reduction in the Dip ratio compared to wildtype (WT) controls. Gene replacement therapy in Rs1KO mice reverted the ORB pattern to one resembling WT and increased the Dip ratio. The degree of anatomical rescue in these mice was highly correlated with level of transgenic RS1 expression and with the restoration of ERG b-wave amplitudes. While the inner retinal cavity was significantly enlarged in dark-adapted Rs1KO mice, the Dip ratio was not altered. A reduction of the Dip ratio was also detected in AMD patients compared with healthy controls and was also positively correlated with AMD severity on the AMD score. We propose that the ORB and Dip ratio can be used as non-invasive early biomarkers for retina health, which can be used to probe therapeutic gene expression and to evaluate the effectiveness of therapy. Full article

Aim: This study aimed to compare the torque loss, fracture load, compressive strength, and failure types of selective-laser-sintered cobalt chromium (SLM-Co-Cr), computer-aided design and computer-aided manufacturing zirconium oxide (CAD-CAM-ZrO), and machined titanium (Ti) implant abutments. Methods: Thirty endosseous dental implants were vertically embedded with machined Ti (control group), CAD-CAM-ZrO, and SLM-Co-Cr abutments. Abutment fabrication involved CAD-CAM milling and SLM technology. The de-torque assessment included preload reverse torque values (RTVs), cyclic loading, and post-RTVs using a customized protocol. Fracture load assessment employed ISO-14801 standards, and statistical analysis was conducted using ANOVA and Tukey Post hoc tests (p < 0.05). Results: In pre-load RTVs, SLM-Co-Cr showed the lowest mean torque loss (24.30 ± 2.13), followed by machined Ti (27.33 ± 2.74) and CAD-CAM-ZrO (22.07 ± 2.20). Post-load RTVs decreased for all groups. Fracture load and compressive strength were highest for SLM-Co-Cr, with significant differences among groups (p < 0.001). Fracture types included abutment failures in SLM-Co-Cr and machined Ti, while CAD-CAM-ZrO exhibited crown separation with deformation. Conclusion: SLM-Co-Cr-fabricated implant abutments exhibited superior stability and resistance to rotational forces, higher fracture loads, and greater compressive strength compared to CAD-CAM-ZrO and machined Ti. Full article

Precise medical image segmentation of regions of interest (ROIs) is crucial for accurate disease diagnosis and progression assessment. However, acquiring high-quality annotated data at the pixel level poses a significant challenge due to the resource-intensive nature of this process. This scarcity of high-quality annotated data results in few-shot scenarios, which are highly prevalent in clinical applications. To address this obstacle, this paper introduces Agent-Guided SAM (AGSAM), an innovative approach that transforms the Segment Anything Model (SAM) into a fully automated segmentation method by automating prompt generation. Capitalizing on the pre-trained feature extraction and decoding capabilities of SAM-Med2D, AGSAM circumvents the need for manual prompt engineering, ensuring adaptability across diverse segmentation methods. Furthermore, the proposed feature augmentation convolution module (FACM) enhances model accuracy by promoting stable feature representations. Experimental evaluations demonstrate AGSAM’s consistent superiority over other methods across various metrics. These findings highlight AGSAM’s efficacy in tackling the challenges associated with limited annotated data while achieving high-quality medical image segmentation. Full article

Excessive fibrosis and resultant poor control of intraocular pressure (IOP) reduce the efficacy of glaucoma surgeries. Historically, corticosteroids and anti-fibrotic agents, such as mitomycin C (MMC) and 5-fluorouracil (5-FU), have been used to mitigate post-surgical fibrosis, but these have unpredictable outcomes. Therefore, there is a need to develop novel treatments which provide increased effectiveness and specificity. This review aims to provide insight into the pathophysiology behind wound healing in glaucoma surgery, as well as the current and promising future wound healing agents that are less toxic and may provide better IOP control. Full article

Background and Aims: Accurate recognition of endoscopic instruments facilitates quantitative evaluation and quality control of endoscopic procedures. However, no relevant research has been reported. In this study, we aimed to develop a computer-assisted system, EndoAdd, for automated endoscopic surgical video analysis based on our dataset of endoscopic instrument images. Methods: Large training and validation datasets containing 45,143 images of 10 different endoscopic instruments and a test dataset of 18,375 images collected from several medical centers were used in this research. Annotated image frames were used to train the state-of-the-art object detection model, YOLO-v5, to identify the instruments. Based on the frame-level prediction results, we further developed a hidden Markov model to perform video analysis and generate heatmaps to summarize the videos. Results: EndoAdd achieved high accuracy (>97%) on the test dataset for all 10 endoscopic instrument types. The mean average accuracy, precision, recall, and F1-score were 99.1%, 92.0%, 88.8%, and 89.3%, respectively. The area under the curve values exceeded 0.94 for all instrument types. Heatmaps of endoscopic procedures were generated for both retrospective and real-time analyses. Conclusions: We successfully developed an automated endoscopic video analysis system, EndoAdd, which supports retrospective assessment and real-time monitoring. It can be used for data analysis and quality control of endoscopic procedures in clinical practice. Full article