Controlled Molecular Arrangement of Cinnamic Acid in Layered Double Hydroxide through pi-pi Interaction for Controlled Release

Cinnamic acid (CA) was successfully incorporated into Zn-Al layered double hydroxide (LDH) through coprecipitation. The CA moiety was stabilized in the interlayer space through not only electrostatic interaction but also intermolecular π-π interaction. It was noteworthy that the CA arrangement was fairly independent of the charge density of LDH, showing the important role of the layer-CA and CA-CA interactions in molecular stabilization. Computer simulations using the Monte Carlo method as well as analytical approaches including infrared, UV-vis spectroscopy, and differential scanning calorimetry showed the existence of intermolecular interaction. In order to reinforce molecular stabilization, a neutral derivative of CA, cinnamaldehyde (CAD), was additionally incorporated into LDH. It was clearly shown that CAD played a role as a π-π interaction mediator to enhance the stabilization of CA. The time-dependent release of CA from LDH was first governed by the layer charge density of LDH; however, the existence of CAD provided additional stabilization to the CA arrangement to slow down the release kinetics.

Cervical spine reconstruction after total vertebrectomy using customized three-dimensional-printed implants in dogs

BACKGROUND

Sufficient surgical resection is necessary for effective tumor control, but is usually limited for vertebral tumors, especially in the cervical spine in small animal neurosurgery.

OBJECTIVE

To evaluate the primary stability and safety of customized three-dimensional (3D)-printed implants for cervical spine reconstruction after total vertebrectomy.

METHODS

Customized guides and implants were designed based on computed tomography (CT) imaging of five beagle cadavers and were 3D-printed. They were used to reconstruct C5 after total vertebrectomy. Postoperative CT images were obtained to evaluate the safety and accuracy of screw positioning. After harvesting 10 vertebral specimens (C3-C7) from intact (group A) and implanted spines (group B), implant stability was analyzed using a 4-point bending test comparing with groups A and C (reconstituted with plate and pins/polymethylmethacrylate after testing in Group A).

RESULTS

All customized implants were applied without gross neurovascular damage. In addition, 90% of the screws were in a safe area, with 7.5% in grade 1 (< 1.3 mm) and 2.5% in grade 2 (> 1.3 mm). The mean entry point and angular deviations were 0.81 ± 0.43 mm and 6.50 ± 5.11°, respectively. Groups B and C significantly decreased the range of motion (ROM) in C3-C7 compared with intact spines (p = 0.033, and 0.018). Both groups reduced overall ROM and neutral zone in C4-C6, but only group B showed significance (p = 0.005, and 0.027).

CONCLUSION

Customized 3D-printed implants could safely and accurately replace a cervical vertebra in dog cadavers while providing primary stability.

Electrospun Poly(L-Lactic Acid)/Gelatin Hybrid Polymer as a Barrier to Periodontal Tissue Regeneration

Poly(L-lactic acid) (PLLA) and PLLA/gelatin polymers were prepared via electrospinning to evaluate the effect of PLLA and gelatin content on the mechanical properties, water uptake capacity (WUC), water contact angle (WCA), degradation rate, cytotoxicity and cell proliferation of membranes. As the PLLA concentration increased from 1 wt% to 3 wt%, the tensile strength increased from 5.8 MPa to 9.1 MPa but decreased to 7.0 MPa with 4 wt% PLLA doping. The WUC decreased rapidly from 594% to 236% as the PLLA content increased from 1 to 4 wt% due to the increased hydrophobicity of PLLA. As the gelatin content was increased to 3 wt% PLLA, the strength, WUC and WCA of the PLLA/gelatin membrane changed from 9.1 ± 0.9 MPa to 13.3 ± 2.3 MPa, from 329% to 1248% and from 127 ± 1.2° to 0°, respectively, with increasing gelatin content from 0 to 40 wt%. However, the failure strain decreased from 3.0 to 0.5. The biodegradability of the PLLA/gelatin blend increased from 3 to 38% as the gelatin content increased to 40 wt%. The viability of L-929 and MG-63 cells in the PLLA/gelatin blend was over 95%, and the excellent cell proliferation and mechanical properties suggested that the tunable PLLA/gelatin barrier membrane was well suited for absorbable periodontal tissue regeneration.

Transfer Learning for Effective Urolithiasis Detection

PURPOSE

Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology.

METHODS

The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model's performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics.

RESULTS

The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process.

CONCLUSION

This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.

CRNet: a multimodal deep convolutional neural network for customer revisit prediction

Since mobile food delivery services have become one of the essential issues for the restaurant industry, predicting customer revisits is highlighted as one of the significant academic and research topics. Considering that the use of multimodal datasets has gained notable attention from several scholars to address multiple industrial issues in our society, we introduce CRNet, a multimodal deep convolutional neural network for predicting customer revisits. We evaluated our approach using two datasets [a customer repurchase dataset (CRD) and mobile food delivery revisit dataset (MFDRD)] and two state-of-the-art multimodal deep learning models. The results showed that CRNet obtained accuracies and Fi-Scores of 0.9575 (CRD) and 0.9436 (MFDRD) and 0.9730 (CRD) and 0.9509 (MFDRD), respectively, thus achieving higher performance levels than current state-of-the-art multimodal frameworks (accuracy: 0.7417-0.9012; F1-Score: 0.7461-0.9378). Future research should aim to address other resources that can enhance the proposed framework (e.g., metadata information).

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40537-022-00674-4.

Cold Atmospheric Pressure Microplasma Pipette for Disinfection of Methicillin-Resistant Staphylococcus aureus

Microbial infections should be controlled and prevented for successful wound healing and tissue regeneration. Various disinfection methods exist that use antibiotics, ultraviolet (UV), heat, radiation, or chemical disinfectants; however, cold atmospheric pressure plasma has exhibited a unique and effective antibacterial ability that is not affected by antibiotic resistance or pain. This study develops a cold atmospheric pressure microplasma pipette (CAPMP) that outputs an Ar plasma plume through a tube with an inner radius of 180 μm for disinfection in a small area. The CAPMP was evaluated using Staphylococcus aureus and methicillin-resistant Staphylococcus aureus diluted in liquid media, spread on solid agar, or covered by dressing gauze. An increase in the treatment time of CAPMP resulted in a decrease in the number of colonies of the grown microorganism (colony forming unit) and an increase in the disinfected area for both bacteria. The disinfection ability of CAPMP was observed when the bacteria were covered with dressing gauze and was dependent on the number of gauze layers.

Distribution of Trauma Deaths in a Province of Korea: Is "Trimodal" Distribution Relevant Today?

PURPOSE

This study was designed to provide a basis for building a master plan for a regional trauma system by analyzing the distribution of trauma deaths in the most populous province in Korea.

MATERIALS AND METHODS

We investigated the time distribution to death for trauma patients who died between January and December 2017. The time distribution to death was categorized into four groups (within a day, within a week, within a month, and over a month). Additionally, the distribution of deaths within 24 hours was further analyzed. We also reviewed the distribution of deaths according to the cause of death and mechanism of injury.

RESULTS

Of the 1546 trauma deaths, 328 cases were included in the final study population. Patients who died within a day were the most prevalent (40.9%). Of those who died within a day, the cases within an hour accounted for 40.3% of the highest proportion. The majority of trauma deaths within 4 hours were caused by traffic-related accidents (60.4%). The deaths caused by bleeding and central nervous system injuries accounted for most (70.1%) of the early deaths, whereas multi-organ dysfunction syndrome/sepsis had the highest ratio (69.7%) in the late deaths. Statistically significant differences were found in time distribution according to the mechanism of injury and cause of death (p<0.001).

CONCLUSION

The distribution of overall timing of death was shown to follow a bimodal pattern rather than a trimodal model in Korea. Based on our findings, a suitable and modified trauma system must be developed.