Antifouling Zwitterionic Nanofibrous Wound Dressing for Long-Lasting Antibacterial Photodynamic Therapy.

Nanofibrous mats as a wound dressing have received great attention in recent year. The development of biocompatible dressings with antibiofouling capability and long-lasting antibacterial properties is important but challenging. Antibacterial photodynamic therapy (aPDT) effectively eliminates pathogens via a photodynamic process that can circumvent the emergence of antibiotic-resistant pathogens. In this study, we integrated the zwitterionic materials (2-methacryloyloxyethyl phosphorylcholine (MPC) moiety) and aPDT photosensitizer, methylene blue (MB), to fabricate a long-lasting antibacterial nanofibrous mat using electrospinning technology. The prepared nanofibers possessed an appropriate water absorption and retention ability, superior cytocompatibility, and antibiofouling ability against both proteins and L929 cell adhesion. MB-loaded nanofibrous mats have exhibited superior aPDT against Gram-positive Staphylococcus aureus compared to Gram-negative Escherichia coli under moderate irradiation (100 W m-2) due to the presence of an extra outer membrane of Gram-negative bacteria serving as a protective barrier. In vitro release study demonstrated that the nanofibrous mat had a long-lasting drug release profile, which can efficiently suppress bacterial growth via aPDT. The antibacterial ability of the MB-loaded nanofibrous mat was commensurate or slightly inferior to antibiotics such as tetracycline and kanamycin, suggesting that it has the potential to be used as an antibiotic alternative. Overall, this zwitterionic nanofibrous mat with long-lasting aPDT function and nonadherent properties has potential as a promising antibacterial wound dressing.

Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5.

Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.

Inter-Multilevel Super-Orthogonal Space-Time Coding Scheme for Reliable ZigBee-Based IoMT Communications.

The Internet of Things (IoT) technology has revolutionized the healthcare industry by enabling a new paradigm for healthcare delivery. This paradigm is known as the Internet of Medical Things (IoMT). IoMT devices are typically connected via a wide range of wireless communication technologies, such as Bluetooth, radio-frequency identification (RFID), ZigBee, Wi-Fi, and cellular networks. The ZigBee protocol is considered to be an ideal protocol for IoMT communication due to its low cost, low power usage, easy implementation, and appropriate level of security. However, maintaining ZigBee's high reliability is a major challenge due to multi-path fading and interference from coexisting wireless networks. This has increased the demand for more efficient channel coding schemes that can achieve a more reliable transmission of vital patient data for ZigBee-based IoMT communications. To meet this demand, a novel coding scheme called inter-multilevel super-orthogonal space-time coding (IM-SOSTC) can be implemented by combining the multilevel coding and set partitioning of super-orthogonal space-time block codes based on the coding gain distance (CGD) criterion. The proposed IM-SOSTC utilizes a technique that provides inter-level dependency between adjacent multilevel coded blocks to facilitate high spectral efficiency, which has been compromised previously by the high coding gain due to the multilevel outer code. In this paper, the performance of IM-SOSTC is compared to other related schemes via a computer simulation that utilizes the quasi-static Rayleigh fading channel. The simulation results show that IM-SOSTC outperforms other related coding schemes and is capable of providing the optimal trade-off between coding gain and spectral efficiency whilst guaranteeing full diversity and low complexity.

Effect of the Nintendo Ring Fit Adventure Exergame on Running Completion Time and Psychological Factors Among University Students Engaging in Distance Learning During the COVID-19 Pandemic: Randomized Controlled Trial.

BACKGROUND: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. OBJECTIVE: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). METHODS: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. RESULTS: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. CONCLUSIONS: RFA could either maintain or improve an individual's physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. TRIAL REGISTRATION: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040.

Development and validation of a deep learning-based algorithm for colonoscopy quality assessment.

BACKGROUND: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure. METHODS: A deep learning system for classifying colonoscopy images for quality assurance purposes was developed and its performance was assessed with an independent dataset. The system was utilized to analyze captured images and results were compared with those of real-world reports. RESULTS: In total, 10,417 images from the hospital endoscopy database and 3157 from Hyper-Kvasir open dataset were utilized to develop the quality assurance algorithm. The overall accuracy of the algorithm was 96.72% and that of the independent test dataset was 94.71%. Moreover, 761 real-world reports and colonoscopy images were analyzed. The accuracy of electronic reports about cecal intubation rate was 99.34% and that of the algorithm was 98.95%. The agreement rate for the assessment of polypectomy rates using the electronic reports and the algorithm was 0.87 (95% confidence interval 0.83-0.90). A good correlation was found between the withdrawal time calculated using the algorithm and that entered by the physician (correlation coefficient r = 0.959, p < 0.0001). CONCLUSION: We proposed a novel deep learning-based algorithm that used colonoscopy images for quality assurance purposes. This model can be used to automatically assess intra-procedural colonoscopy quality indicators in clinical practice.

Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

BACKGROUND: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of material for model development. We developed a deep learning-based endoscopic anatomy classification system through convolutional neural networks with an accelerated data preparation approach. PATIENTS AND METHODS: We retrospectively collected 8,041 images from esophagogastroduodenoscopy (EGD) procedures and labeled them using two experts for nine anatomical locations of the upper gastrointestinal tract. A base model for EGD image multiclass classification was first developed, and an additional 6,091 images were enrolled and classified by the base model. A total of 5,963 images were manually confirmed and added to develop the subsequent enhanced model. Additional internal and external endoscopy image datasets were used to test the model performance. RESULTS: The base model achieved total accuracy of 96.29%. For the enhanced model, the total accuracy was 96.64%. The overall accuracy improved with the enhanced model compared with the base model for the internal test dataset without narrowband images (93.05% vs. 91.25%, p < 0.01) or with narrowband images (92.74% vs. 90.46%, p < 0.01). The total accuracy was 92.56% of the enhanced model on the external test dataset. CONCLUSIONS: We constructed a deep learning-based model with an accelerated approach that can be used for quality control in endoscopy units. The model was also validated with both internal and external datasets with high accuracy.

Upper endoscopy photodocumentation quality evaluation with novel deep learning system.

OBJECTIVES: Visualization and photodocumentation during endoscopy procedures are suggested to be one indicator for endoscopy performance quality. However, this indicator is difficult to measure and audit manually in clinical practice. Artificial intelligence (AI) is an emerging technology that may solve this problem. METHODS: A deep learning model with an accuracy of 96.64% was developed from 15,305 images for upper endoscopy anatomy classification in the unit. Endoscopy images for asymptomatic patients receiving screening endoscopy were evaluated with this model to assess the completeness of photodocumentation rate. RESULTS: A total of 15,723 images from 472 upper endoscopies performed by 12 endoscopists were enrolled. The complete photodocumentation rate from the pharynx to the duodenum was 53.8% and from the esophagus to the duodenum was 78.0% in this study. Endoscopists with a higher adenoma detection rate had a higher complete examination rate from the pharynx to duodenum (60.0% vs. 38.7%, P < 0.0001) and from esophagus to duodenum (83.0% vs. 65.7%, P < 0.0001) compared with endoscopists with lower adenoma detection rate. The pharynx, gastric angle, gastric retroflex view, gastric antrum, and the first portion of duodenum are likely to be missed by endoscopists with lower adenoma detection rates. CONCLUSIONS: We report the use of a deep learning model to audit endoscopy photodocumentation quality in our unit. Endoscopists with better performance in colonoscopy had a better performance for this quality indicator. The use of such an AI system may help the endoscopy unit audit endoscopy performance.

A giant malignant phyllodes tumor of breast post mastectomy with metastasis to stomach manifesting as anemia: a case report and review of literature.

BACKGROUND: Phyllodes tumors (PTs) are well known for local recurrence and progression. Less than 10% of these tumors grow larger than 10 cm. Distant metastases have been reported in up to 22% of malignant PTs, with most metastases being discovered in the lungs. PTs of the breast rarely metastasize to the gastrointestinal tract, and reported cases are scarce. To date, a review of the English literature revealed only 3 cases, including our case, of PTs metastasis to stomach. CASE PRESENTATION: An 82-year-old female patient had 10-year-duration of palpable huge tumor on left breast which was in rapid growth in recent months. Total mastectomy of left breast was performed thereafter, and pathology diagnosis was malignant phyllodes tumor. Adjuvant radiotherapy was suggested while she declined out of personal reasons initially. For PTs recurred locally on left chest wall 2 months later, and excision of the recurrent PTs was performed. She, at length, completed adjuvant radiation therapy since then. Six months later, she was diagnosed of metastasis to stomach due to severe anemia with symptom of melena. Gastrostomy with tumor excision was performed for uncontrollable tumor bleeding. CONCLUSION: For PTs presenting as anemia without known etiologies, further studies are suggested to rule out possible gastrointestinal tract metastasis though such cases are extremely rare. Management of metastatic gastric tumor from PTs should be done on a case-to-case basis, surgical intervention may be needed if there is persistent active bleeding despite medical treatment. Adjuvant radiotherapy is recommended in borderline and malignant PTs with tumor-free margin < 1 cm and high-risk malignant tumors. Adjuvant chemotherapy or target therapy may be helpful for metastatic PTs. Molecular and genomic techniques may predict clinical outcomes of benign and borderline PTs more precisely.

Important role of surface plasmon coupling with the quantum wells in a surface plasmon enhanced color-converting structure of colloidal quantum dots on quantum wells.

To improve the color-conversion efficiency based on a quantum-well (QW) light-emitting diode (LED), a more energy-saving strategy is needed to increase the energy transfer efficiency from the electrical input power of the LED into the emission of over-coated color-converter, not just from LED emission into converted light. In this regard, the efficiency of energy transfer of any mechanism from LED QW into the color-converter is an important issue. By overlaying blue-emitting QW structures and GaN templates with both deposited metal nanoparticles (DMNPs) and color-converting quantum dot (QD) linked synthesized metal nanoparticles (SMNPs) of different localized surface plasmon (LSP) resonance wavelengths for producing multiple surface plasmon (SP) coupling mechanisms with the QW and QD, we study the enhancement variations of their internal quantum efficiencies and photoluminescence decay times. By comparing the QD emission efficiencies between the samples with and without QW, one can observe the advantageous effect of QW coupling with LSP resonances on QD emission efficiency. Also, with the LSP resonance wavelengths of both DMNPs and SMNPs close to the QW emission wavelength for producing strong SP coupling with the QW and hence QD absorption, a higher QD emission or color-conversion efficiency can be obtained.

Simulation study on light color conversion enhancement through surface plasmon coupling.

A theoretical model together with a numerical algorithm of surface plasmon (SP) coupling are built for simulating SP-enhanced light color conversion from a shorter-wavelength radiating dipole (representing a quantum well - QW) into a longer-wavelength one (representing a quantum dot - QD) through QD absorption at the shorter wavelength. An Ag nanoparticle (NP) located between the two dipoles is designed for producing strong SP couplings simultaneously at the two wavelengths. At the QW emission wavelength, SP couplings with the QW and QD dipoles lead to the energy transfer from the QW into the QD and hence the absorption enhancement of the QD. At the QD emission wavelength, SP coupling with the excited QD dipole results in the enhancement of QD emission efficiency. The combination of the SP-induced effects at the two wavelengths leads to the increase of overall color conversion efficiency. The color conversion efficiencies in using Ag NPs of different geometries or SP resonance behaviors for producing different QD absorption and emission enhancement levels are compared.