Bioengineering, Volume 10, Issue 9 (September 2023) – 117 articles

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively. Full article

(1) Background: The physical fitness (PF) of hearing-impaired students has always been an international research hotspot since hearing-impaired students have difficulty in social interactions such as exercise or fitness programs. Sports interventions are proven to improve the fitness levels of hearing-impaired students; however, few studies evaluating the influence of Cha-cha (a type of Dance sport) training on the PF levels of hearing-impaired students have been conducted. (2) Purpose: This study aimed to intervene in hearing-impaired children through 12 weeks of Cha-cha dance training, evaluating its effects on their PF-related indicators, thus providing a scientific experimental basis for hearing-impaired children to participate in dance exercises effectively. (3) Methods: Thirty students with hearing impairment were randomly divided into two groups, and there was no difference in PF indicators between the two groups. The Cha-cha dance training group (CTG, n = 15) regularly participated in 90-min Cha-cha dance classes five times a week and the intervention lasted a total of 12 weeks, while the control group (CONG, n = 15) lived a normal life (including school physical education classes). Related indicators of PF were measured before and after the intervention, and a two-way repeated-measures analysis of variance was performed. (4) Results: After training, the standing long jump (CONG: 1.556 ± 0.256 vs. CTG: 1.784 ± 0.328, p = 0.0136, ES = 0.8081), sit-and-reach (CONG: 21.467 ± 4.539 vs. CTG: 25.416 ± 5.048, p = 0.0328, ES = 0.8528), sit-ups (CONG: 13.867 ± 4.912 vs. CTG: 27.867 ± 6.833, p < 0.0001, ES = 2.4677) and jump rope (CONG: 52.467 ± 29.691 vs. CTG: 68.600 ± 21.320, p = 0.0067, ES = 0.6547) scores showed significant differences. (5) Conclusions: After 12 weeks of Cha-cha dance training for hearing-impaired students, the PF level of hearing-impaired students in lower-body strength, flexibility, core strength, and cardiorespiratory endurance were effectively improved; however, there was no significant change in body shape, upper-body strength, vital capacity, and speed ability. Full article

Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver–vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver–vehicle interfaces, multimodal interaction, and intelligent vehicles. Full article

Developing new drugs for emerging diseases, such as COVID-19, is crucial for promoting public health. In recent years, the application of artificial intelligence (AI) has significantly advanced drug discovery pipelines. Generative models, such as generative adversarial networks (GANs), exhibit the potential for discovering novel drug molecules by relying on a vast number of training samples. However, for new diseases, only a few samples are typically available, posing a significant challenge to learning a generative model that produces both high-quality and diverse molecules under limited supervision. To address this low-data drug generation issue, we propose a novel molecule generative domain adaptation paradigm (Mol-GenDA), which transfers a pre-trained GAN on a large-scale drug molecule dataset to a new disease domain using only a few references. Specifically, we introduce a molecule adaptor into the GAN generator during the fine tuning, allowing the generator to reuse prior knowledge learned in pre-training to the greatest extent and maintain the quality and diversity of the generated molecules. Comprehensive downstream experiments demonstrate that Mol-GenDA can produce high-quality and diverse drug candidates. In summary, the proposed approach offers a promising solution to expedite drug discovery for new diseases, which could lead to the timely development of effective drugs to combat emerging outbreaks. Full article

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics. Full article

Modern dental implantology is based on a set of more or less related first-order parameters, such as the implant surface and the intrinsic composition of the material. For decades, implant manufacturers have focused on the research and development of the ideal material combined with an optimal surface finish to ensure the success and durability of their product. However, brands do not always communicate transparently about the nature of the products they market. Thus, this study aims to compare the surface finishes and intrinsic composition of three zirconia implants from three major brands. To do so, cross-sections of the apical part of the implants to be analyzed were made with a micro-cutting machine. Samples of each implant of a 4 to 6 mm thickness were obtained. Each was analyzed by a tactile profilometer and scanning electron microscope (SEM). Compositional measurements were performed by X-ray energy-dispersive spectroscopy (EDS). The findings revealed a significant use of aluminum as a chemical substitute by manufacturers. In addition, some manufacturers do not mention the presence of this element in their implants. However, by addressing these issues and striving to improve transparency and safety standards, manufacturers have the opportunity to provide even more reliable products to patients. Full article

To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called sEMGPoseMIM, that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner. In addition, we propose a novel network called sEMGXCM as the sEMG encoder. Comprehensive experiments on seven sparse multichannel sEMG databases are conducted to demonstrate the effectiveness of the training scheme sEMGPoseMIM and the network sEMGXCM, which achieves an average improvement of +1.3% on the sparse multichannel sEMG databases compared to the existing methods. Furthermore, the comparison between training sEMGXCM and other existing networks from scratch shows that sEMGXCM outperforms the others by an average of +1.5%. Full article

Accurate identification of lesions and their use across different medical institutions are the foundation and key to the clinical application of automatic diabetic retinopathy (DR) detection. Existing detection or segmentation methods can achieve acceptable results in DR lesion identification, but they strongly rely on a large number of fine-grained annotations that are not easily accessible and suffer severe performance degradation in the cross-domain application. In this paper, we propose a cross-domain weakly supervised DR lesion identification method using only easily accessible coarse-grained lesion attribute labels. We first propose the novel lesion-patch multiple instance learning method (LpMIL), which leverages the lesion attribute label for patch-level supervision to complete weakly supervised lesion identification. Then, we design a semantic constraint adaptation method (LpSCA) that improves the lesion identification performance of our model in different domains with semantic constraint loss. Finally, we perform secondary annotation on the open-source dataset EyePACS, to obtain the largest fine-grained annotated dataset EyePACS-pixel, and validate the performance of our model on it. Extensive experimental results on the public dataset FGADR and our EyePACS-pixel demonstrate that compared with the existing detection and segmentation methods, the proposed method can identify lesions accurately and comprehensively, and obtain competitive results using only coarse-grained annotations. Full article

The use of biological systems in manufacturing and medical applications has seen a dramatic rise in recent years as scientists and engineers have gained a greater understanding of both the strengths and limitations of biological systems. Biomanufacturing, or the use of biology for the production of biomolecules, chemical precursors, and others, is one particular area on the rise as enzymatic systems have been shown to be highly advantageous in limiting the need for harsh chemical processes and the formation of toxic products. Unfortunately, biological production of some products can be limited due to their toxic nature or reduced reaction efficiency due to competing metabolic pathways. In nature, microbes often secrete enzymes directly into the environment or encapsulate them within membrane vesicles to allow catalysis to occur outside the cell for the purpose of environmental conditioning, nutrient acquisition, or community interactions. Of particular interest to biotechnology applications, researchers have shown that membrane vesicle encapsulation often confers improved stability, solvent tolerance, and other benefits that are highly conducive to industrial manufacturing practices. While still an emerging field, this review will provide an introduction to biocatalysis and bacterial membrane vesicles, highlight the use of vesicles in catalytic processes in nature, describe successes of engineering vesicle/enzyme systems for biocatalysis, and end with a perspective on future directions, using selected examples to illustrate these systems’ potential as an enabling tool for biotechnology and biomanufacturing. Full article

The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalization to different image modalities and medical conditions. To address these issues, a methodological strategy was adopted, combining natural language processing and features extraction from medical images and feeding them into a generative model based on neural networks. The goal was to achieve model generalization across various image modalities and medical conditions. The results showed promising outcomes in the generation of descriptions, with an accuracy of 0.7628 and a BLEU-1 score of 0.5387. However, the quality of the generated descriptions may still be limited, exhibiting semantic errors or lacking relevant details. These limitations could be attributed to the availability and representativeness of the data, as well as the techniques used. Full article

Elderly people usually have poorer surgical tolerance and a higher incidence of complications when undergoing revision surgery after posterior instrumented lumbar fusion (PILF). Full-endoscopic transforaminal surgery is a safe and effective option, but sometimes, it is difficult to revise L5-S1 foraminal stenosis (FS) after PILF. Therefore, we developed full-endoscopic lumbar decompression (FELD) at the arthrodesis level via a modified interlaminar approach under local anesthesia. This study aimed to describe the technical note and clinical efficacy of the technique. Eleven patients with unilateral lower limb radiculopathy after PILF underwent selective nerve root block and then underwent FELD. Magnetic resonance imaging (MRI) and computer tomography (CT) were performed on the second postoperative day. Their clinical outcomes were evaluated with a Visual analog scale (VAS) of low back pain and sciatica pain, Oswestry disability index (ODI), and the MacNab score. Complete decompression was achieved in every case with FELD without serious complications. Postoperative VAS of sciatica pain and ODI at each time point and VAS of low back pain and ODI after three months postoperatively were significantly improved compared with those preoperative (p < 0.05). According to the MacNab criteria, seven patients (63.6%) had excellent results at the two-year follow-up, and four patients (36.4%) had good results. No patients required further revision surgery. FELD, via a modified interlaminar approach, is effective for treating unilateral L5-S1 FS after PILF in elderly people. Full article

Surgery has long been an important treatment for limiting optic nerve damage and minimising visual loss in patients with glaucoma. Numerous improvements, modifications, and innovations in glaucoma surgery over recent decades have improved surgical safety, and have led to earlier and more frequent surgical intervention in glaucoma patients at risk of vision loss. This review summarises the latest advancements in trabeculectomy surgery, glaucoma drainage device (GDD) implantation, and minimally invasive glaucoma surgery (MIGS). A comprehensive search of MEDLINE, EMBASE, and CENTRAL databases, alongside subsequent hand searches—limited to the past 10 years for trabeculectomy and GDDs, and the past 5 years for MIGS—yielded 2283 results, 58 of which were included in the final review (8 trabeculectomy, 27 GDD, and 23 MIGS). Advancements in trabeculectomy are described in terms of adjunctive incisions, Tenon’s layer management, and novel suturing techniques. Advancements in GDD implantation pertain to modifications of surgical techniques and devices, novel methods to deal with postoperative complications and surgical failure, and the invention of new GDDs. Finally, the popularity of MIGS has recently promoted modifications to current surgical techniques and the development of novel MIGS devices. Full article

Background: Surgeries of severe periacetabular bone defects (Paprosky ≥ 2B) are a major challenge in current practice. Although solutions are available for this serious clinical problem, they all have their disadvantages as well as their advantages. An alternative method of reconstructing such extensive defects was the use of a cup with a stem to solve these revision situations. As the instrumentation offered is typically designed for scenarios where a significant bone defect is not present, our unique technique has been developed for implantation in cases where reference points are missing. Our hypothesis was that a targeting device designed based on the CT scan of a patient’s pelvis could facilitate the safe insertion of the guiding wire. Methods: Briefly, our surgical solution consists of a two-step operation. If periacetabular bone loss was found to be more significant during revision surgery, all implants were removed, and two titanium marker screws in the anterior iliac crest were percutaneously inserted. Next, by applying the metal artifact removal (MAR) algorithm, a CT scan of the pelvis was performed. Based on that, the dimensions and positioning of the cup to be inserted were determined, and a patient-specific 3D printed targeting device made of biocompatible material was created to safely insert the guidewire, which is essential to the implantation process. Results: In this study, medical, engineering, and technical tasks related to the design, the surgical technique, and experiences from 17 surgical cases between February 2018 and July 2021 are reported. There were no surgical complications in any cases. The implant had to be removed due to septic reasons (independently from the technique) in a single case, consistent with the septic statistics for this type of surgery. There was not any perforation of the linea terminalis of the pelvis due to the guiding method. The wound healing of patients was uneventful, and the implant was fixed securely. Following rehabilitation, the joints were able to bear weight again. After one to four years of follow-up, the patient satisfaction level was high, and the gait function of the patients improved a lot in all cases. Conclusions: Our results show that CT-based virtual surgical planning and, based on it, the use of a patient-specific 3D printed aiming device is a reliable method for major hip surgeries with significant bone loss. This technique has also made it possible to perform these operations with minimal X-ray exposure. Full article

Background: In minimally invasive spine surgery (MISS), where the surgeon cannot directly see the patient’s internal anatomical structure, the implementation of augmented reality (AR) technology may solve this problem. Methods: We combined AR, artificial intelligence, and optical tracking to enhance the augmented reality minimally invasive spine surgery (AR-MISS) system. The system has three functions: AR radiograph superimposition, AR real-time puncture needle tracking, and AR intraoperative navigation. The three functions of the system were evaluated through beagle animal experiments. Results: The AR radiographs were successfully superimposed on the real intraoperative videos. The anteroposterior (AP) and lateral errors of superimposed AR radiographs were 0.74 ± 0.21 mm and 1.13 ± 0.40 mm, respectively. The puncture needles could be tracked by the AR-MISS system in real time. The AP and lateral errors of the real-time AR needle tracking were 1.26 ± 0.20 mm and 1.22 ± 0.25 mm, respectively. With the help of AR radiographs and AR puncture needles, the puncture procedure could be guided visually by the system in real-time. The anteroposterior and lateral errors of AR-guided puncture were 2.47 ± 0.86 mm and 2.85 ± 1.17 mm, respectively. Conclusions: The results indicate that the AR-MISS system is accurate and applicable. Full article

Background: Alzheimer’s disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. Objective: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. Materials and Methods: The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results. Results: Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group. Conclusions: The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection. Full article

This study focuses on developing and characterizing a novel 3-dimensional cell-laden micro-patterned porous structure from a mechanical engineering perspective. Tissue engineering holds great promise for repairing damaged organs but faces challenges related to cell viability, biocompatibility, and mechanical strength. This research aims to overcome these limitations by utilizing gelatin methacrylate hydrogel as a scaffold material and employing a photolithography technique for precise patterned fabrication. The mechanical properties of the structure are of particular interest in this study. We evaluate its ability to withstand external forces through compression tests, which provide insights into its strength and stability. Additionally, structural integrity is assessed over time to determine its performance in in vitro and potential in vivo environments. We investigate cell viability and proliferation within the micro-patterned porous structure to evaluate the biological aspects. MTT assays and immunofluorescence staining are employed to analyze the metabolic activity and distribution pattern of cells, respectively. These assessments help us understand the effectiveness of the structure in supporting cell growth and tissue regeneration. The findings of this research contribute to the field of tissue engineering and provide valuable insights for mechanical engineers working on developing scaffolds and structures for regenerative medicine. By addressing challenges related to cell viability, biocompatibility, and mechanical strength, we move closer to realizing clinically viable tissue engineering solutions. The novel micro-patterned porous structure holds promise for applications in artificial organ development and lays the foundation for future advancements in large soft tissue construction. Full article

Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression ability. This paper proposes a new method called MWG-UNet (multiple tasking Wasserstein generative adversarial network U-shape network) as a lung field and heart segmentation model, which takes advantages of the attention mechanism to enhance the segmentation accuracy of the generator so as to improve the performance. In particular, the Dice similarity, precision, and F1 score of the proposed method outperform other models, reaching 95.28%, 96.41%, and 95.90%, respectively, and the specificity surpasses the sub-optimal models by 0.28%, 0.90%, 0.24%, and 0.90%. However, the value of the IoU is inferior to the optimal model by 0.69%. The results show the proposed method has considerable ability in lung field segmentation. Our multi-organ segmentation results for the heart achieve Dice similarity and IoU values of 71.16% and 74.56%. The segmentation results on lung fields achieve Dice similarity and IoU values of 85.18% and 81.36%. Full article

Enterobacter hormaechei is part of the Enterobacter cloacae complex (ECC), which is widespread in nature. It is a facultative Gram-negative bacterium of medical and industrial importance. We assessed the metabolic and genetic repertoires of a new Enterobacter isolate. Here, we report the whole-genome sequence of a furfural- and 5-hydroxymethyl furfural (HMF)-tolerant strain of E. hormaechei (UW0SKVC1), which uses glucose, glycerol, xylose, lactose and arabinose as sole carbon sources. This strain exhibits high tolerance to furfural (IC₅₀ = 34.2 mM; ~3.3 g/L) relative to Escherichia coli DH5α (IC₅₀ = 26.0 mM; ~2.5 g/L). Furfural and HMF are predominantly converted to their less-toxic alcohols. E. hormaechei UW0SKVC1 produces 2,3-butanediol, acetoin, and acetol, among other compounds of industrial importance. E. hormaechei UW0SKVC1 produces as high as ~42 g/L 2,3-butanediol on 60 g/L glucose or lactose. The assembled genome consists of a 4,833,490-bp chromosome, with a GC content of 55.35%. Annotation of the assembled genome revealed 4586 coding sequences and 4516 protein-coding genes (average length 937-bp) involved in central metabolism, energy generation, biodegradation of xenobiotic compounds, production of assorted organic compounds, and drug resistance. E. hormaechei UW0SKVC1 shows considerable promise as a biocatalyst and a genetic repository of genes whose protein products may be harnessed for the efficient bioconversion of lignocellulosic biomass, abundant glycerol and lactose-replete whey permeate to value-added chemicals. Full article

Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultra-wide-field fundus images (UFI). The proposed method utilizes contrastive learning to pre-train a deep learning model and then fine-tune it with a small set of labeled images. This approach reduces the reliance on labeled data, which is often limited and costly to obtain, and has the potential to improve disease detection in UFI. This method employs two contrastive learning techniques, namely bi-lateral contrastive learning and multi-modality pre-training, to form positive pairs using the data correlation. Bi-lateral learning fuses multiple views of the same patient’s images, and multi-modality pre-training leverages the complementary information between UFI and conventional fundus images (CFI) to form positive pairs. The results show that the proposed contrastive learning method achieves state-of-the-art performance with an area under the receiver operating characteristic curve (AUC) score of 86.96, outperforming other approaches. The findings suggest that self-supervised learning is a promising direction for medical image analysis, with potential applications in various clinical settings. Full article

Injectable hydrogels offer numerous advantages in various areas, which include tissue engineering and drug delivery because of their unique properties such as tunability, excellent carrier properties, and biocompatibility. These hydrogels can be administered with minimal invasiveness. In this study, we synthesized an injectable hydrogel by rehydrating lyophilized mixtures of guar adamantane (Guar-ADI) and poly-β-cyclodextrin (p-βCD) in a solution of phosphate-buffered saline (PBS) maintained at pH 7.4. The hydrogel was formed via host-guest interaction between modified guar (Guar-ADI), obtained by reacting guar gum with 1-adamantyl isocyanate (ADI) and p-βCD. Comprehensive characterization of all synthesized materials, including the hydrogel, was performed using nuclear magnetic resonance (NMR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and rheology. The in vitro drug release study demonstrated the hydrogel’s efficacy in controlled drug delivery, exemplified by the release of bovine serum albumin (BSA) and anastrozole, both of which followed first-order kinetics. Furthermore, the hydrogel displayed excellent biocompatibility and served as an ideal scaffold for promoting the growth of mouse osteoblastic MC3T3 cells as evidenced by the in vitro biocompatibility study. Full article

Background: To compare the efficacy of CO₂ fractional laser with that of topical estriol for treating genitourinary syndrome of menopause and to investigate the relationship between epithelial thickness and vaginal atrophy. Methods: Twenty-five menopausal women were randomized to receive either laser or estrogen treatment. Vaginal biopsies before and after treatment were compared to assess the amount and distribution of estrogen and progesterone receptors. Results: Estrogen receptor levels were statistically similar between groups before and after treatment. Although there was no change over time in the estrogen group, an increase in receptor levels was confirmed in the laser group. Changes in estrogen receptor levels showed no association with treatment. Progesterone receptor levels were statistically similar between groups throughout treatment. There was no change over time in both groups. These changes displayed no association with the type of treatment. There was no significant correlation between epithelium thickness and estrogen or progesterone receptor levels. Conclusions: Estrogen and progesterone receptor levels increased and were maintained, respectively, in the vaginal epithelium in both groups. There was no significant relationship between epithelium thickness and receptor density. Laser therapy had similar outcomes to the gold standard without involving the disadvantages of hormone therapy. Full article

Adolescent idiopathic scoliosis (AIS) is a three-dimensional axial deviation of the spine diagnosed in adolescence. Despite a long daily sitting duration, there are no studies on whether scoliosis can be positively influenced by sitting on a seat wedge. For the prospective study, 99 patients with AIS were measured with the DIERS formetric III 4D average, in a standing position, on a level seat and with three differently inclined seat wedges (3°, 6° and 9°). The rasterstereographic parameters ‘scoliosis angle’ and ‘lateral deviation RMS’ were analysed. The side (ipsilateral/contralateral) on which the optimal correcting wedge was located in relation to the lumbar/thoraco-lumbar convexity was investigated. It was found that the greatest possible correction of scoliosis occurred with a clustering in wedges with an elevation on the ipsilateral side of the convexity. This clustering was significantly different from a uniform distribution (p < 0.001; chi-square = 35.697 (scoliosis angle); chi-square = 54.727 (lateral deviation RMS)). It should be taken into account that the effect of lateral seat wedges differs for individual types of scoliosis and degrees of severity. The possibility of having a positive effect on scoliosis while sitting holds great potential, which is worth investigating in follow-up studies. Full article

Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research. Full article

The increasing global population and urbanization have led to a pressing need for effective solutions to manage the organic fraction of municipal solid waste (OFMSW). High-solids anaerobic digestion (HS-AD) has garnered attention as a sustainable technology that offers reduced water demand and energy consumption, and an increased biogas production rate. However, challenges such as rheology complexities and slow mass transfer hinder its widespread application. To address these limitations, this review emphasizes the importance of process optimization and the mass transfer enhancement of HS-AD, and summarizes various strategies for enhancing mass transfer in the field of HS-AD for the OFMSW, including substrate pretreatments, mixing strategies, and the addition of biochar. Additionally, the incorporation of innovative reactor designs, substrate pretreatment, the use of advanced modeling and simulation techniques, and the novel conductive materials need to be investigated in future studies to promote a better coupling between mass transfer and methane production. This review provides support and guidance to promote HS-AD technology as a more viable solution for sustainable waste management and resource recovery. Full article

Hyaline cartilage’s inability to self-repair can lead to osteoarthritis and joint replacement. Various treatments, including cell therapy, have been developed for cartilage damage. Autologous chondrocyte implantation (ACI) is considered the best option for focal chondral lesions. In this article, we aimed to create a narrative review that highlights the evolution and enhancement of our chondrocyte implantation technique: High-Density-ACI (HD-ACI) Membrane-assisted Autologous Chondrocyte Implantation (MACI) improved ACI using a collagen membrane as a carrier. However, low cell density in MACI resulted in softer regenerated tissue. HD-ACI was developed to improve MACI, implanting 5 million chondrocytes per cm², providing higher cell density. In animal models, HD-ACI formed hyaline-like cartilage, while other treatments led to fibrocartilage. HD-ACI was further evaluated in patients with knee or ankle defects and expanded to treat hip lesions and bilateral defects. HD-ACI offers a potential solution for cartilage defects, improving outcomes in regenerative medicine and cell therapy. HD-ACI, with its higher cell density, shows promise for treating chondral defects and advancing cartilage repair in regenerative medicine and cell therapy. Full article

This study introduces a novel convolutional neural network (CNN) architecture, encompassing both single and multi-head designs, developed to identify a user’s locomotion activity while using a wearable lower limb robot. Our research involved 500 healthy adult participants in an activities of daily living (ADL) space, conducted from 1 September to 30 November 2022. We collected prospective data to identify five locomotion activities (level ground walking, stair ascent/descent, and ramp ascent/descent) across three terrains: flat ground, staircase, and ramp. To evaluate the predictive capabilities of the proposed CNN architectures, we compared its performance with three other models: one CNN and two hybrid models (CNN-LSTM and LSTM-CNN). Experiments were conducted using multivariate signals of various types obtained from electromyograms (EMGs) and the wearable robot. Our results reveal that the deeper CNN architecture significantly surpasses the performance of the three competing models. The proposed model, leveraging encoder data such as hip angles and velocities, along with postural signals such as roll, pitch, and yaw from the wearable lower limb robot, achieved superior performance with an inference speed of 1.14 s. Specifically, the F-measure performance of the proposed model reached 96.17%, compared to 90.68% for DDLMI, 94.41% for DeepConvLSTM, and 95.57% for LSTM-CNN, respectively. Full article

Super-resolution structured illumination microscopy (SR-SIM) is an optical fluorescence microscopy method which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing raw data and coarser illumination patterns, we imaged through a 150-micrometer-thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7-fold beyond conventional widefield imaging. Full article

The blood-brain barrier (BBB) is a dynamic interface that regulates the molecular exchanges between the brain and peripheral blood. The permeability of the BBB is primarily regulated by the junction proteins on the brain endothelial cells. In vitro BBB models have shown great potential for the investigation of the mechanisms of physiological function, pathologies, and drug delivery in the brain. However, few studies have demonstrated the ability to monitor and evaluate the barrier integrity by quantitatively analyzing the junction presentation in 3D microvessels. This study aimed to fabricate a simple vessel-on-chip, which allows for a rigorous quantitative investigation of junction presentation in 3D microvessels. To this end, we developed a rapid protocol that creates 3D microvessels with polydimethylsiloxane and microneedles. We established a simple vessel-on-chip model lined with human iPSC-derived brain microvascular endothelial-like cells (iBMEC-like cells). The 3D image of the vessel structure can then be “unwrapped” and converted to 2D images for quantitative analysis of cell–cell junction phenotypes. Our findings revealed that 3D cylindrical structures altered the phenotype of tight junction proteins, along with the morphology of cells. Additionally, the cell–cell junction integrity in our 3D models was disrupted by the tumor necrosis factor α. This work presents a “quick and easy” 3D vessel-on-chip model and analysis pipeline, together allowing for the capability of screening and evaluating the cell–cell junction integrity of endothelial cells under various microenvironment conditions and treatments. Full article

Quadrupole mass spectrometers (QMS) are widely used for clinical diagnosis and chemical analysis. To obtain the best experimental results, mass spectrometers must be calibrated to an ideal setting before use. However, tuning the current QMS is challenging. Traditional tuning techniques possess low automation levels and rely primarily on skilled engineers. Therefore, in this study, we propose an innovative auto-tuning algorithm for QMS based on the improved particle swarm optimization (PSO) algorithm to automatically find the optimal solution of QMS parameters and make the QMS reach the optimal state. The improved PSO algorithm is combined with simulated annealing, multiple inertia weights, dynamic boundaries, and other methods to prevent the traditional PSO algorithm from the issue of a local optimal solution and premature convergence. According to the characteristics of the mass spectrum peaks, a termination function is proposed to simplify the termination conditions of the PSO algorithm and further improve the automation level of the mass spectrometer. The results of auto-calibration testing of resolution and mass axis show that both resolution and mass axis calibration could effectively meet the requirements of mass spectrometry experiments. By the experiment of auto-optimization testing of lens and ion source parameters, these parameters were all in the vicinity of the optimal solution, which achieved the expected performance. Through numerous experiments, the reproducibility of the algorithm was established as meeting the auto-tuning function of the QMS. The proposed method can automatically tune the mass spectrometer from its non-optimal condition to the optimal one, which can effectively reduce the tuning difficulty of QMS. Full article

Background: CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue structure and characteristics and are more likely to pick up abnormalities and lesions. The purpose of this paper is to review studies which use deep learning methods to generate synthetic medical images of modalities such as MRI and CT. Methods: A literature search was performed in March 2023, and relevant articles were selected and analyzed. The year of publication, dataset size, input modality, synthesized modality, deep learning architecture, motivations, and evaluation methods were analyzed. Results: A total of 103 studies were included in this review, all of which were published since 2017. Of these, 74% of studies investigated MRI to CT synthesis, and the remaining studies investigated CT to MRI, Cross MRI, PET to CT, and MRI to PET. Additionally, 58% of studies were motivated by synthesizing CT scans from MRI to perform MRI-only radiation therapy. Other motivations included synthesizing scans to aid diagnosis and completing datasets by synthesizing missing scans. Conclusions: Considerably more research has been carried out on MRI to CT synthesis, despite CT to MRI synthesis yielding specific benefits. A limitation on medical image synthesis is that medical datasets, especially paired datasets of different modalities, are lacking in size and availability; it is therefore recommended that a global consortium be developed to obtain and make available more datasets for use. Finally, it is recommended that work be carried out to establish all uses of the synthesis of medical scans in clinical practice and discover which evaluation methods are suitable for assessing the synthesized images for these needs. Full article