Beef peptides mitigate skeletal muscle atrophy in C2C12 myotubes through protein degradation, protein synthesis, and the oxidative stress pathway.

This study aimed to investigate the potential of beef peptides (BPs) in mitigating muscle atrophy induced by dexamethasone (DEX) with underlying three mechanisms in vitro (protein degradation, protein synthesis, and the oxidative stress pathway). Finally, the anti-atrophic effect of BPs was enhanced through purification and isolation. BPs were generated using beef loin hydrolyzed with alcalase/ProteAX/trypsin, each at a concentration of 0.67%, followed by ultrafiltration through a 3 kDa cut-off. BPs (10-100 µg mL-1) dose-dependently counteracted the DEX-induced reductions in myotube diameters, differentiation, fusion, and maturation indices (p < 0.05). Additionally, BPs significantly reduced FoxO1 protein dephosphorylation, thereby suppressing muscle-specific E3 ubiquitin ligases such as muscle RING-finger containing protein-1 and muscle atrophy F-box protein in C2C12 myotubes at concentrations exceeding 25 µg mL-1 (p < 0.05). BPs also enhanced the phosphorylation of protein synthesis markers, including mTOR, 4E-BP1, and p70S6K1, in a dose-dependent manner (p < 0.05) and increased the mRNA expression of antioxidant enzymes. Fractionated peptides derived from BPs, through size exclusion and polarity-based fractionation, also demonstrated enhanced anti-atrophic effects compared to BPs. These peptides downregulated the mRNA expression of primary muscle atrophy markers while upregulated that of antioxidant enzymes. Specifically, peptides GAGAAGAPAGGA (MW 924.5) and AFRSSTKK (MW 826.4) were identified from fractionated peptides of BPs. These findings suggest that BPs, specifically the peptide fractions GAGAAGAPAGGA and AFRSSTKK, could be a potential strategy to mitigate glucocorticoid-induced skeletal muscle atrophy by reducing the E3 ubiquitin ligase activity.

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography.

Importance: Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important. Objective: To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home. Design, Setting, and Participants: This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023. Main Outcomes and Measures: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds. Results: Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively. Conclusions and Relevance: This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy.

Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.

Endoscopists performance in optical diagnosis of colorectal polyps in artificial intelligence studies.

Widespread adoption of optical diagnosis of colorectal neoplasia is prevented by suboptimal endoscopist performance and lack of standardized training and competence evaluation. We aimed to assess diagnostic accuracy of endoscopists in optical diagnosis of colorectal neoplasia in the framework of artificial intelligence (AI) validation studies. Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to April 2022 were performed to identify articles evaluating accuracy of individual endoscopists in performing optical diagnosis of colorectal neoplasia within studies validating AI against a histologically verified ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), positive and negative likelihood ratio (LR) and area under the curve (AUC for sROC) for predicting adenomas versus non-adenomas. Six studies with 67 endoscopists and 2085 (IQR: 115-243,5) patients were evaluated. Pooled sensitivity and specificity for adenomatous histology was respectively 84.5% (95% CI 80.3%-88%) and 83% (95% CI 79.6%-85.9%), corresponding to a PPV, NPV, LR+, LR- of 89.5% (95% CI 87.1%-91.5%), 75.7% (95% CI 70.1%-80.7%), 5 (95% CI 3.9%-6.2%) and 0.19 (95% CI 0.14%-0.25%). The AUC was 0.82 (CI 0.76-0.90). Expert endoscopists showed a higher sensitivity than non-experts (90.5%, [95% CI 87.6%-92.7%] vs. 75.5%, [95% CI 66.5%-82.7%], p < 0.001), and Eastern endoscopists showed a higher sensitivity than Western (85%, [95% CI 80.5%-88.6%] vs. 75.8%, [95% CI 70.2%-80.6%]). Quality was graded high for 3 studies and low for 3 studies. We show that human accuracy for diagnosis of colorectal neoplasia in the setting of AI studies is suboptimal. Educational interventions could benefit by AI validation settings which seem a feasible framework for competence assessment.

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations.

Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to the 2021 U.S. Preventive Services Task Force (USPSTF) recommendations in a health check-up sample. Materials and Methods This single-center retrospective study included consecutive current and former smokers aged 50-80 years who underwent chest radiography during a health check-up between January 2004 and June 2018. Discrimination performance, including receiver operating characteristic curve analysis and area under the receiver operating characteristic curve (AUC) calculation, of the model for incident lung cancers was evaluated. The added value of the model to the 2021 USPSTF recommendations was investigated for lung cancer inclusion rate, proportion of selected CT screening candidates, and positive predictive value (PPV). Results For model validation, a total of 19 488 individuals (mean age, 58 years ± 6 [SD]; 18 467 [95%] men) and the subset of USPSTF-eligible individuals (n = 7835; mean age, 57 years ± 6; 7699 [98%] men) were assessed, and the AUCs for incident lung cancers were 0.68 (95% CI: 0.62, 0.73) and 0.75 (95% CI: 0.68, 0.81), respectively. In individuals with pack-year information (n = 17 390), when excluding low- and indeterminate-risk categories from the USPSTF-eligible sample, the proportion of selected CT screening candidates was reduced to 35.8% (6233 of 17 390) from 45.1% (7835 of 17 390, P < .001), with three missed lung cancers (0.2%). The cancer inclusion rate (0.3% [53 of 17 390] vs 0.3% [56 of 17 390], P = .85) and PPV (0.9% [53 of 6233] vs 0.7% [56 of 7835], P = .42) remained unaffected. Conclusion An externally validated deep learning model showed the added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening in reducing the number of screening candidates while maintaining the inclusion rate and positive predictive value for incident lung cancer. © RSNA, 2022 Online supplemental material is available for this article.

Augmented Reality-Based Visual Cue for Guiding Central Catheter Insertion in Pediatric Oncologic Patients.

BACKGROUND: Pediatric hemato-oncologic patients require central catheters for chemotherapy, and the junction of the superior vena cava and right atrium is considered the ideal location for catheter tips. Skin landmarks or fluoroscopic supports have been applied to identify the cavoatrial junction; however, none has been recognized as the gold standard. Therefore, we aim to develop a safe and accurate technique using augmented reality technology for the location of the cavoatrial junction in pediatric hemato-oncologic patients. METHODS: Fifteen oncology patients who underwent chest computed tomography were enrolled for Hickman catheter or chemoport insertion. With the aid of augmented reality technology, three-dimensional models of the internal jugular veins, external jugular veins, subclavian veins, superior vena cava, and right atrium were constructed. On inserting the central vein catheters, the cavoatrial junction identified using the three-dimensional models were marked on the body surface, the tip was positioned at the corresponding location, and the actual insertion location was confirmed using a portable x-ray machine. The proposed method was evaluated by comparing the distance from the cavoatrial junction to the augmented reality location with that to the conventional location on x-ray. RESULTS: The mean distance between the cavoatrial junction and augmented reality location on x-ray was 1.2 cm, which was significantly shorter than that between the cavoatrial junction and conventional location (1.9 cm; P = 0.027). CONCLUSIONS: Central catheter insertion using augmented reality technology is more safe and accurate than that using conventional methods and can be performed at no additional cost in oncology patients.

Effect of an anti-adhesion agent on vision-based assessment of cervical adhesions after thyroid surgery: randomized, placebo-controlled trial.

Many patients experience cervical adhesions after thyroid surgery. To date, however, no studies have objectively measured the effects of anti-adhesion agents on cervical adhesion symptoms. This study evaluated the effects of an anti-adhesion agent on cervical adhesions after thyroid surgery, as determined using a system that measures the extent of marker movement objectively. One hundred patients were randomized in a 1:1 ratio to undergo thyroid surgery with or without the anti-adhesion agent Collabarrier. Using specially manufactured recording equipment, the position of the marker on neck skin was measured before surgery, and 2 weeks, 3 months, and 9 months after surgery. Relative change in marker distance, calculated by subtracting the marker position before surgery from the marker positions 2 weeks, 3 months, and 9 months after surgery, differed significantly in the groups of patients who underwent thyroid surgery with and without the anti-adhesion agent (P < 0.05). A novel measuring system can objectively evaluate the effectiveness of a thyroid anti-adhesion agent. The anti-adhesion agent used significantly reduced adhesions compared with the control group. The trial is registered at www.cris.nih.go.kr (KCT0005745; date of registration, 08/01/2021).

Concanavalin A targeting N-linked glycans in spike proteins influence viral interactions.

Lectins, which exhibit viral-interaction abilities, have garnered attention in the current pandemic era as potential neutralizing agents and vaccine candidates. Viral invasion through envelope proteins is modulated by N-linked glycosylation in the spike (S) protein. This study demonstrates the biophysical aspects between lectins and high-mannose and -galactose N-glycans to provide insights into binding events.

Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations.

As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques. It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments. This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons' skills in performing procedures by robotic surgery. This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments. In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments. The tracking method was applied to 54 video segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis. The RMSE was 3.52 mm, the AUC of 1 mm, 2 mm, and 5 mm were 0.7, 0.78, and 0.86, respectively, and Pearson's correlation coefficients were 0.9 on the x-axis and 0.87 on the y-axis. The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS). The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method.

Vision-based tracking system for augmented reality to localize recurrent laryngeal nerve during robotic thyroid surgery.

We adopted a vision-based tracking system for augmented reality (AR), and evaluated whether it helped surgeons to localize the recurrent laryngeal nerve (RLN) during robotic thyroid surgery. We constructed an AR image of the trachea, common carotid artery, and RLN using CT images. During surgery, an AR image of the trachea and common carotid artery were overlaid on the physical structures after they were exposed. The vision-based tracking system was activated so that the AR image of the RLN followed the camera movement. After identifying the RLN, the distance between the AR image of the RLN and the actual RLN was measured. Eleven RLNs (9 right, 4 left) were tested. The mean distance between the RLN AR image and the actual RLN was 1.9 ± 1.5 mm (range 0.5 to 3.7). RLN localization using AR and vision-based tracking system was successfully applied during robotic thyroidectomy. There were no cases of RLN palsy. This technique may allow surgeons to identify hidden anatomical structures during robotic surgery.

Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps by endoscopists of different skill levels. METHODS: We developed convolutional neural networks (CNNs) for evaluation of diminutive colorectal polyps, based on efficient neural architecture searches via parameter sharing with augmentation using NBIs of diminutive (≤5 mm) polyps, collected from October 2015 through October 2017 at the Seoul National University Hospital, Healthcare System Gangnam Center (training set). We trained the CNN using images from 1100 adenomatous polyps and 1050 hyperplastic polyps from 1379 patients. We then tested the system using 300 images of 180 adenomatous polyps and 120 hyperplastic polyps, obtained from January 2018 to May 2019. We compared the accuracy of 22 endoscopists of different skill levels (7 novices, 4 experts, and 11 NBI-trained experts) vs the CNN in evaluation of images (adenomatous vs hyperplastic) from 180 adenomatous and 120 hyperplastic polyps. The endoscopists then evaluated the polyp images with knowledge of the CNN-processed results. We conducted mixed-effect logistic and linear regression analyses to determine the effects of AI assistance on the accuracy of analysis of diminutive colorectal polyps by endoscopists (primary outcome). RESULTS: The CNN distinguished adenomatous vs hyperplastic diminutive polyps with 86.7% accuracy, based on histologic analysis as the reference standard. Endoscopists distinguished adenomatous vs hyperplastic diminutive polyps with 82.5% overall accuracy (novices, 73.8% accuracy; experts, 83.8% accuracy; and NBI-trained experts, 87.6% accuracy). With knowledge of the CNN-processed results, the overall accuracy of the endoscopists increased to 88.5% (P < .05). With knowledge of the CNN-processed results, the accuracy of novice endoscopists increased to 85.6% (P < .05). The CNN-processed results significantly reduced endoscopist time of diagnosis (from 3.92 to 3.37 seconds per polyp, P = .042). CONCLUSIONS: We developed a CNN that significantly increases the accuracy of evaluation of diminutive colorectal polyps (as adenomatous vs hyperplastic) and reduces the time of diagnosis by endoscopists. This AI assistance system significantly increased the accuracy of analysis by novice endoscopists, who achieved near-expert levels of accuracy without extra training. The CNN assistance system can reduce the skill-level dependence of endoscopists and costs.

CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists.

OBJECTIVES: To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model and expert radiologists. METHODS: This study included 525 patients (309 women; median, 62 years) to develop models, and an independent cohort of 101 patients (57 women; median, 66 years) was used for validation. A size-based logistic model and deep learning models using 2.5-dimension (2.5D) and three-dimension (3D) CT images were developed to discriminate IAC from less invasive pathologies. Overall performance, discrimination, and calibration were assessed. Diagnostic performances of the three thoracic radiologists were compared with those of the deep learning model. RESULTS: The overall performances of the deep learning models (Brier score, 0.122 for the 2.5D DenseNet and 0.121 for the 3D DenseNet) were superior to those of the size-based logistic model (Brier score, 0.198). The area under the receiver operating characteristic curve (AUC) of the 2.5D DenseNet (0.921) was significantly higher than that of the 3D DenseNet (0.835; p = 0.037) and the size-based logistic model (0.836; p = 0.009). At equally high sensitivities of 90%, the 2.5D DenseNet showed significantly higher specificity (88.2%; all p < 0.05) and positive predictive value (97.4%; all p < 0.05) than other models. Model calibration was poor for all models (all p < 0.05). The 2.5D DenseNet had a comparable performance with the radiologists (AUC, 0.848-0.910). CONCLUSION: The 2.5D DenseNet model could be used as a highly sensitive and specific diagnostic tool to differentiate IACs among SSNs for surgical candidates. KEY POINTS: • The deep learning model developed using 2.5D DenseNet showed higher overall performance and discrimination than the size-based logistic model for the differentiation of invasive adenocarcinomas among subsolid nodules for surgical candidates. • The 2.5D DenseNet demonstrated a thoracic radiologist-level diagnostic performance and had higher specificity (88.2%) at equal sensitivities (90%) than the size-based logistic model (specificity, 52.9%). • The 2.5D DenseNet could be used to reduce potential overtreatment for the indolent subsolid nodules or to select candidates for sublobar resection instead of the standard lobectomy.

The Durability of Active Surveillance in Patients with Asymptomatic Kidney Stones: A Systematic Review.

Introduction: The natural progression of asymptomatic kidney stones remains unclear. Such knowledge may promote value-aligned care for patients and reduce potentially unnecessary procedures. We sought to evaluate the natural history of asymptomatic kidney stones in adults undergoing active surveillance. Materials and Methods: Using themes of "Kidney Stone" and "Active Surveillance," we performed a systematic review by searching for studies in MEDLINE, all Cochrane libraries, EMBASE, Cumulative Index to Nursing and Allied Health Literature, BIOSIS, Scopus, and Web of Science from inception through October 2017-in addition to ClinicalTrials.gov, American Urological Association Annual Meeting abstracts (2014-2017), Google Scholar, and references of included studies and prior reviews. Two blinded reviewers independently extracted data and assessed methodological quality. We qualitatively summarized rates of surgical intervention (primary outcome), spontaneous stone passage, symptom development, and stone growth. We assessed the relationship between surveillance duration and rate of surgical intervention with Pearson's correlation coefficient. Results: Of 7034 unique records, 13 studies met final eligibility criteria. There was substantial variation in reported rates of surgical intervention from 6/85 (7.1%) to 80/301 (26.6%), spontaneous stone passage from 1/32 (3.1%) to 101/347 (29.1%), symptom development from 7/96 (7.3%) to 231/300 (77.0%), and stone growth from 5/96 (5.2%) to 33/50 (66.0%). Mean surveillance duration spanned from 11.3 to 80 months (range 2-180 months). Longer mean duration of surveillance did not correlate with an increase in surgical intervention rate across studies (n = 13, r = 0.01, p = 0.98), and this finding persisted when restricting analysis to observational studies (n = 9, r = 0.12, p = 0.76). Conclusions: Active surveillance appears to be a durable strategy for a majority of patients with asymptomatic kidney stones, as there was no increase in failure of watchful waiting despite increasing duration of surveillance. Higher quality studies are needed to ascertain which patients may benefit most from active surveillance.

Association of Long-term Use of Low-Dose Aspirin as Chemoprevention With Risk of Lung Cancer.

Importance: Low-dose aspirin use for chemoprevention of lung cancer risk remains controversial. Objectives: To investigate the association between low-dose aspirin use and lung cancer risk, and to identify specific subgroups that may derive the most benefit from low-dose aspirin use. Design, Setting, and Participants: This nationwide, retrospective, cohort study used data from the Korean National Health Information Database from 2002 to 2015. Data analyses were performed from October 2016 to December 2018. Eligible participants (n = 12 969 400) were people aged 40 to 84 years who had undergone national health screening between 2009 and 2010 and had no history of lung cancer between 2006 and 2010 and no standard-dose aspirin use for 6 months between 2002 and 2010. Main Outcomes and Measures: The duration of low-dose aspirin use between January 2002 and December 2010 was calculated for each participant. Lung cancer was defined as the first recorded diagnosis of lung cancer-using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes and expanding benefit coverage-between January 2011 and December 2015. Results: A total of 63 040 participants with a mean (SD) age of 66.4 (9.3) years received a diagnosis of lung cancer. Of these, 45 156 (71.6%) were men. The incidence rate of lung cancer was 98.8 per 100 000 person-years. The duration of low-dose aspirin use was none for 10 987 417 participants (84.7%), 1 to 2 years for 750 992 participants (5.8%), 3 to 4 years for 506 945 participants (3.9%), 5 to 6 years for 371 062 participants (2.9%), 7 to 8 years for 240 528 participants (1.9%), and 9 years for 112 456 participants (0.9%). Compared with no aspirin use, 5 to 6 years (adjusted hazard ratio, 0.96 [95% CI, 0.92-0.99]), 7 to 8 years (adjusted hazard ratio, 0.94 [95% CI, 0.90-0.99]), and 9 years (adjusted hazard ratio, 0.89 [95% CI, 0.84-0.94]) of aspirin use were significantly associated with reduced lung cancer risk. After stratified analysis, a significant reduction of lung cancer risk was observed among people aged 65 years or older and among people without diabetes. Conclusions and Relevance: Although the use of low-dose aspirin for more than 5 years was associated with decreased risk of lung cancer, particularly among elderly participants and among people without diabetes, the observed effect size was quite modest. Future prospective studies are needed to determine whether there is a causal association.

Preliminary study on application of augmented reality visualization in robotic thyroid surgery.

PURPOSE: Increased robotic surgery is attended by increased reports of complications, largely due to limited operative view and lack of tactile sense. These kinds of obstacles, which seldom occur in open surgery, are challenging for beginner surgeons. To enhance robotic surgery safety, we created an augmented reality (AR) model of the organs around the thyroid glands, and tested the AR model applicability in robotic thyroidectomy. METHODS: We created AR images of the thyroid gland, common carotid arteries, trachea, and esophagus using preoperative CT images of a thyroid carcinoma patient. For a preliminary test, we overlaid the AR images on a 3-dimensional printed model at five different angles and evaluated its accuracy using Dice similarity coefficient. We then overlaid the AR images on the real-time operative images during robotic thyroidectomy. RESULTS: The Dice similarity coefficients ranged from 0.984 to 0.9908, and the mean of the five different angles was 0.987. During the entire process of robotic thyroidectomy, the AR images were successfully overlaid on the real-time operative images using manual registration. CONCLUSION: We successfully demonstrated the use of AR on the operative field during robotic thyroidectomy. Although there are currently limitations, the use of AR in robotic surgery will become more practical as the technology advances and may contribute to the enhancement of surgical safety.

Augmented Reality to Localize Individual Organ in Surgical Procedure.

OBJECTIVES: Augmented reality (AR) technology has become rapidly available and is suitable for various medical applications since it can provide effective visualization of intricate anatomical structures inside the human body. This paper describes the procedure to develop an AR app with Unity3D and Vuforia software development kit and publish it to a smartphone for the localization of critical tissues or organs that cannot be seen easily by the naked eye during surgery. METHODS: In this study, Vuforia version 6.5 integrated with the Unity Editor was installed on a desktop computer and configured to develop the Android AR app for the visualization of internal organs. Three-dimensional segmented human organs were extracted from a computerized tomography file using Seg3D software, and overlaid on a target body surface through the developed app with an artificial marker. RESULTS: To aid beginners in using the AR technology for medical applications, a 3D model of the thyroid and surrounding structures was created from a thyroid cancer patient's DICOM file, and was visualized on the neck of a medical training mannequin through the developed AR app. The individual organs, including the thyroid, trachea, carotid artery, jugular vein, and esophagus were localized by the surgeon's Android smartphone. CONCLUSIONS: Vuforia software can help even researchers, students, or surgeons who do not possess computer vision expertise to easily develop an AR app in a user-friendly manner and use it to visualize and localize critical internal organs without incision. It could allow AR technology to be extensively utilized for various medical applications.

Cadmium-Substituted Concanavalin A and Its Trimeric Complexation.

Concanavalin A (ConA) interacts with carbohydrates as a lectin, and recent reports proposed its application for detecting a diversity of viruses and pathogens. Structural studies have detailed the interaction between ConA and carbohydrates and the metal coordination environment with manganese and calcium ions (Mn-Ca-ConA). In this study, ConA was crystallized with a cadmium-containing precipitant, and the refined structure indicates that Mn²âº was replaced by Cd²âº (Cd-Ca-ConA). The structural comparison with ConA demonstrates that the metal-coordinated residues of Cd-Ca-ConA, that is Glu8, Asp10, Asn14, Asp19, and His24, do not have conformational shifts, but residues for sugar binding, including Arg228, Tyr100, and Leu99, reorient their side chains, slightly. Previous studies demonstrated that excess cadmium ions can coordinate with other residues, including Glu87 and Glu183, which were not coordinated with Cd²âº in this study. The trimeric ConA in this study coordinated Cd²âº with other residues, including Asp80 and Asp82, for the complex generation. The monomers does not have specific interaction near interface regions with the other monomer, but secondary cadmium coordinated with two aspartates (Asp80 and Asp82) from monomer 1 and one aspartate (Asp16) from monomer 2. This study demonstrated that complex generation was induced via coordination with secondary Cd²âº and showed the application potential regarding the design of complex formation for specific interactions with target saccharides.

Case Report of a Large Common Hepatic Artery Aneurysm.

Hepatic artery aneurysm is rare, but appropriate treatment is mandatory. We report a 50-year-old woman with an asymptomatic large hepatic artery aneurysm. The aneurysm was found as a result of abdominal computed tomography (CT) performed as a part of the screening. An open surgery was performed due to the size of the aneurysm. Aneurysmectomy was achieved, and the proper hepatic artery was anastomosed with gastroduodenal artery for adequate blood flow to the liver. Adequate hepatic circulation was confirmed postoperatively by duplex ultrasonography and CT. The patient was discharged on the 9th postoperative day.

Simultaneous blockade of VEGF and Dll4 by HD105, a bispecific antibody, inhibits tumor progression and angiogenesis.

Several angiogenesis inhibitors targeting the vascular endothelial growth factor (VEGF) signaling pathway have been approved for cancer treatment. However, VEGF inhibitors alone were shown to promote tumor invasion and metastasis by increasing intratumoral hypoxia in some preclinical and clinical studies. Emerging reports suggest that Delta-like ligand 4 (Dll4) is a promising target of angiogenesis inhibition to augment the effects of VEGF inhibitors. To evaluate the effects of simultaneous blockade against VEGF and Dll4, we developed a bispecific antibody, HD105, targeting VEGF and Dll4. The HD105 bispecific antibody, which is composed of an anti-VEGF antibody (bevacizumab-similar) backbone C-terminally linked with a Dll4-targeting single-chain variable fragment, showed potent binding affinities against VEGF (KD: 1.3 nM) and Dll4 (KD: 30 nM). In addition, the HD105 bispecific antibody competitively inhibited the binding of ligands to their receptors, i.e., VEGF to VEGFR2 (EC50: 2.84 ± 0.41 nM) and Dll4 to Notch1 (EC50: 1.14 ± 0.06 nM). Using in vitro cell-based assays, we found that HD105 effectively blocked both the VEGF/VEGFR2 and Dll4/Notch1 signaling pathways in endothelial cells, resulting in a conspicuous inhibition of endothelial cell proliferation and sprouting. HD105 also suppressed Dll4-induced Notch1-dependent activation of the luciferase gene. In vivo xenograft studies demonstrated that HD105 more efficiently inhibited the tumor progression of human A549 lung and SCH gastric cancers than an anti-VEGF antibody or anti-Dll4 antibody alone. In conclusion, HD105 may be a novel therapeutic bispecific antibody for cancer treatment.