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Using 3D-Printed Models to Advance Clinical Care

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Cardiovascular Innovations and Applications 2019年第B04期4卷 53-61页

作者： Molly Pantelic Milan Pantelic,md Todd Pietila Marianne Rollet Eric Myers Thomas song,md William W.O’Neill,md Dee Dee Wang,md,FACC,FASE,FSCCT Center for Structural Heart Disease Division of CardiologyHenry Ford Health System2799 West Grand BoulevardDetroitMI 48202USA Department of Radiology Henry Ford Health System2799 West Grand BoulevardDetroitMI 48202USA Materialise 44650 Helm CtPlymouthMI 48170USA Henry Ford Innovation Institute Henry Ford Health System2799 West Grand BoulevardDetroitMI 48202USA

Structural Heart is a new fi eld within the division of cardiovascular service *** heart has broadened the scope of delivery of cardiovascular care with its ability to deliver new valves and devices to heart patients who were once turned down for traditional open-heart surgery through the use of transcatheter delivery systems and device ***,in the absence of an open-surgical fi eld,the main limitation in transcatheter device development and patient-centric care is the inability of the Structural Heart Implanter to palpate the patient’s cardiac anatomy for device sizing and *** of 3D printing and 3D modeling are becoming a useful toolkit for Structural Heart Implanters,Imagers,and Device specialists within the Heart Team to use as a communication tool and case planning resource to optimize patient care,and patient *** interventions have revolutionized not only the fi eld of cardiology,but additionally the fi eld of biomedical engineering within Cardiovascular Medicine through the incorporation of 3D simulation technology.

关键词： 3D printing Left atrial appendage transcatheter mitral valve structural heart TAVR

Status and prospects for symmetric organic redox flow batteries

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Journal of Energy Chemistry 2025年第1期100卷 125-143页

作者： md Al Raihan C.Adam Dyker Department of Chemistry University of New Brunswick

As environmental concerns from fossil fuel consumption intensify, large-scale energy storage becomes imperative for the integration of renewable sources like wind, hydro, and solar with the electrical *** flow batteries, particularly those employing organic molecules, are positioned as a key technology for this purpose. This review explores the growing field of symmetric organic redox flow batteries(ORFBs) within this context. Unlike traditional asymmetric designs based on unique active materials for each electrode, symmetric ORFBs involve a single bipolar species for both electrodes. This review highlights the benefits of a symmetric design, and categorizes five distinct classes of organic bipolar molecules used in both aqueous and non-aqueous solvents. By providing a comprehensive overview of their cell cycling and performance characteristics, the strengths and weaknesses of the diverse categories of bipolar molecules are highlighted for both solvent systems, as are opportunities for future development. This should guide new research directions and advance the development of practical symmetric ORFBs.

关键词： Redox flow battery Symmetric battery Organic battery Aqueous electrolyte Non-aqueous electrolyte

Effects of Location and Extension of Portal Vein Tumor Thrombus on Long-Term Outcomes of Surgical Treatment for Hepatocellular Carcinoma

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Effects of Location and Extension of Portal Vein Tumor Throm...

Regeneration mechanisms and therapeutic strategies for neuromuscular junctions in aging and diseases

山东抗癌协会普外肿瘤专业委员会第三次学术会议

作者： Xiao-Ping Chen, md, PhD, FACS, Fa-Zu Qiu, md, PhD, Zai-De Wu, md, PhD, Zhi-Wei Zhang, md, PhD, Zhi-Yong Huang, md, PhD, Yi-Fa Chen, md, PhD, Bi-Xiang Zhang, md, PhD, song-Qing He, md, PhD, and Wan-Guang Zhang, md, PhD Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan. People''s Republic of China

＜正＞Background: The role of surgical resection and thrombectomy for hepatocellular carcinoma （HCC） with portal vein tumor thrombus （PVTT） is controversial. This study aimed to evaluate the effects of the location and extent of PVTT on the long-term outcomes of surgical treatment for HCC. Methods: A total of 438 patients with HCC and PVTT underwent liver resection with or without thrombectomy. These 438 patients were divided into 2 groups: in group A, PVTT was located in the hepatic resection area or protruded into the first branch of the main portal vein beyond the resection line for ＜ 1 cm （286 patients）, and in group B, PVTT extended into the main portal vein （1S2 patients）. Concomitant thrombectomy was performed in 147 patients （51.4%） of group A and in all patients of group B. Results: PVTT recurrence within 6 months after surgery in group B was significantly higher than that in group A: 76.9% vs. 11.3%. Remnant liver recurrence within 1 year after surgery was 43.0% in group A and 78.8% in group B. The cumulative 1-, 2-. 3-, and 5-year overall survival rates were 58.7%, 39.9%, 22.7%, and 18.1% for group A and 39.5%. 20.4%, 5.7%, and 0% for group B, respectively. The overall survivals were significantly better in group A than group B （P ＜ .02）. Conclusions: Liver resection with thrombectomy yielded better outcomes in the HCC patients with PVTT confined to the first or second branch of the main portal vein compared with PVTT extending into the main portal vein.

关键词： Hepatocellular carcinoma-Portal vein tumor thrombus-Liver resection-Survival-Thrombectomy.

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Neural Regeneration Research 2025年第1期20卷 193-194页

作者： Masashi Fujitani Abu md Mamun Tarif Yoshinori Otani Department of Anatomy and Neuroscience Faculty of MedicineShimane UniversityShimaneJapan

The neuromuscular junction(NMJ)is an essential synaptic structure composed of motor neurons,skeletal muscles,and glial cells that orchestrate the critical process of muscle contraction(Li et al.,2018).The typical NMJ structure is classically described as having a“pretzel-like”shape in mice(Figure 1),whereas human NMJs have a smaller,fragmented structure throughout *** NMJs exhibit smaller or fragmented endplates,partial denervation,reduced numbers of synaptic vesicles,abnormal presynaptic mitochondria,and dysfunctional perisynaptic Schwann cells(Alhindi et al.,2022).

关键词： aging structure Figure

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Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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Computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul Karim Ayon Farhan md.Siraj Jia Uddin Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big Data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

东莨菪碱透皮贴剂：可替代昂丹司琼和氟哌利多用于预防手术后呕吐症状的新制剂

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麻醉与镇痛 2008年第1期4卷 64-69页

作者： Patti F. White, PhD, md Jun Tang, md Dajun song, md, PhD Jayne E. Coleman, md Ronald H. Wender, md Babatunde Ogunnaike, md Alexander Sloninsky, md Rajani Kapu, md Mary Shah, md Tom Webb, md 潘明(译) 王多友(校) Department of Anesthesiology and Pain Management University of Texas Southwestern Medical Center at Dallas Dallas Texas Department of Anesthesiology Cedars Sinai Medical Center in Los Angeles Los Angeles California 不详

背景鉴于对使用氟哌利多的争议和5-HT3受体拮抗剂的高昂价格，人们期望出现一种效价合理、可常规用于预防呕吐的药物来替代它们。我们设计了两个平行、随机、双盲、空白和安慰剂对照研究来比较东莨菪碱透皮贴剂（TDS）1．5mg和静脉注射... 详细信息

背景鉴于对使用氟哌利多的争议和5-HT3受体拮抗剂的高昂价格，人们期望出现一种效价合理、可常规用于预防呕吐的药物来替代它们。我们设计了两个平行、随机、双盲、空白和安慰剂对照研究来比较东莨菪碱透皮贴剂（TDS）1．5mg和静脉注射昂丹司琼4mg及氟哌利多1．25mg作为多模式给药方案的一部分，对手术后呕吐“高危”人群早期和晚期的止吐效果及其副作用。方法150例患者参与本研究，其中包括腹腔镜手术（n=80）和整形外科手术（n=70）。患者入手术室前60分钟接受一枚有效的TDS贴剂（含东莨菪碱1．5mg）或一个外表相同的空白贴片。所有患者均接受标准全身麻醉。第2次给药是在手术接近结束时给予患者静脉注射2ml生理盐水（TDS贴剂的两组）、氟哌利多1．25mg或昂丹司琼4mg（空白贴片组）。记录患者手术后恶心、呕吐／干呕的发生率、是否需要止吐药物治疗、用药后的完全有效率（即用药后既无迁延性恶心亦无反复发生呕吐，无需使用止吐药物进行治疗）。此外，对手术后72小时之内的视觉紊乱、口干、昏睡和烦躁不安等不良反应进行记录。结果手术后72小时内，TDS贴剂组、氟哌利多组和昂丹司琼组患者呕吐和需要止吐药物治疗的患者差异无显著性。各治疗组中完全有效率为41％-51％，各组间差异无显著性。TDS组口干的发生率显著高于氟哌利多组和昂丹司琼组（21％vs3％）。结论TDS贴剂作为手术前给药用于预防手术后早期和晚期恶心、呕吐与氟哌利多1．25mg或昂丹司琼4mg效果相同，但是TDS贴剂更易引起口干。

关键词：手术后呕吐透皮贴剂氟哌利多呕吐症状昂丹司琼东莨菪碱预防 5-HT3受体拮抗剂

Ru/NiMnB spherical cluster pillar for highly proficient green hydrogen electrocatalyst at high current density

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Journal of Energy Chemistry 2025年第1期100卷 397-408页

作者： md Ahasan Habib Shusen Lin Mehedi Hasan Joni Sumiya Akter Dristy Rutuja Mandavkar Jae-Hun Jeong Jihoon Lee Department of Electronic Engineering College of Electronics and Information Kwangwoon University

Advanced OER/HER electrocatalytic alternatives are crucial for the wide adaptation of green hydrogen ***,Ru/NiMnB spherical cluster pillar （SCP）,denoted as Ru/NiMnB,is synthesized using a combination of electro-deposition and hydrothermal *** investigation of Ru doping in the NiMnB matrix revealed significant improvements in electrocatalytic *** Ru/NiMnB SCPs demonstrate superior OER/HER activity with low overpotentials of 150 and 103 mV at 50mA/cm2in 1 M KOH,making them highly competitive with state-of-the-art ***,the Ru/NiMnB SCPs exhibit a low 2-E cell voltage of 2.80 V at ultra-high current density of 2,000 m A/cm2in 1 M KOH,outperforming the standard benchmark electrodes of RuO2||Pt/C,thereby positioning Ru/NiMnB as one of the best bifunctional *** SCPs exhibit exceptional high-current characteristics,stability and corrosion resistance,as evidenced by continuous operation at 1,000 mA/cm2high-current density for over 150 h in 6 M KOH at elevated temperatures under harsh industrial *** a small amount of Ru incorporation significantly enhances the electrocatalytic performances of NiMnB,attributed to increased active sites and improved intrinsic properties such as conductivity,adsorption/desorption capability and reaction ***,Ru/NiMnB SCPs present a promising bi-functional electrode concept for efficient green H2production.

关键词： Potassium hydroxide

Current Cryopreservation Technologies for the Shelf Living Cell Products

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Current Cryopreservation Technologies for the Shelf Living C...

Comparison of CNN-based deep learning architectures for rice diseases classification

第五届全国低温生物医学及器械学术大会

作者： Ying C. song, md, PhD Director of Research, Xytex Research Inc, 1100 Emmett Street, Augusta, GA 30904 Clinical Associate Professor, Department of Surgery & Institute of Molecular Medicine and Genetics, Medical College of Georgia, CA-2135,1120 15th Street, Augusta, GA 30912

Tissue engineering, applying the principles of biology and engineering, has the potential to provide strategically designed substitutes for a variety of human tissues and organs in need of repair, restoration or replacement. The ultimate success of tissue engineering depends upon the availability of practical product storage and transportation methods. The only methods currently used for unlimited storage and stabilization of cells and tissues involve the application of cryobiology. Traditional cryopreservation methods do not adequately preserve complex natural or engineered multi-cellular tissues due to the ice formation in the extracellular matrices. Vitrification is an alternate ice-free method for cryopreservation. Novel approaches being investigated to controlling ice in complex tissues and organs include combining conventional cryoprotective agents with synthetic ice blocker molecules or naturally-occurring antifreeze peptides. These approaches permit reduction of the cryoprotectant concentrations employed for vitrification resulting in minimization of cytotoxic risks. The pros and cons of cryopreservation by freezing and cryopreservation by vitrification will be discussed in the context of biomaterials, cell-scaffold matrix interaction, mechanical strength, in vivo remodeling and wound healing. Comparing the effects of vitrification and conventional cryopreservation on tissue engineered constructs, polyglycolic acid (PGA), complete-cellular and collagen-based vascular constructs (TEBVs);tissue engineered pancreatic substitute (TEPS) and tissue engineered cartilage (TEC) were distributed into fresh, frozen and vitrified groups for evaluation of mechanical properties, cell viability and tissue

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Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： md Taimur Ahad Yan Li Bo song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection