Diverse Set Research Articles

VeriGen: A Large Language Model for Verilog Code Generation

In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by automatically completing partial Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We evaluate the functional correctness of the generated Verilog code using a specially designed test suite, featuring a custom problem set and testing benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation. We release our training/evaluation scripts and LLM checkpoints as open-source contributions.

Acculturation and Openness of Generation Z Consumers to New Cultural Goods and Services

Acculturation is the process of signifying the human potential to adapt and grow for productive living. What implications does acculturation have on Generation Z consumer’s willingness to explore new cultural goods and services? Gen Z consumers’ receptivity to cultural products and services comes via acculturation benefiting marketers utilizing transformants to achieve success of a firm. The purpose of this research was to comprehend the process by which Gen Z adjusts to and embraces various cultures in a diversified setting, and to establish a framework that relates this acculturation with consumer enthusiasm for cultural products and services. Twelve respondents in a multicultural setting who were going through the process of acculturation were surveyed. Ideas about how acculturation affects consumer readiness for new experiences were developed through a qualitative approach that employed grounded theory analysis techniques. The level of Gen Z individuals’ openness to seek new experiences offered by other cultures (appreciating new goods, different value systems, and approaches to life), to learn new languages, and travel were significant findings of the study. A model termed the “Hierarchy of Acculturation-Needs Cycle” was constructed with the aim of advancing research and discussion on the topic of acculturation.

An Examination of Black Undergraduate Women's Intersectional Experiences and Academic Motivation in Computing Education

Objectives. We aim to understand, from a motivational perspective, how Black undergraduate women in computing make sense of their intersectional computing experiences. We examine their motivation to major in computing, their experiences as Black women in computing, and how these vary across institutional contexts. Participants. A sample of 77 Black undergraduate women in computing programs (computer science, computer engineering, information systems, information technology, and software engineering) from diverse college settings was recruited to participate in the study. Study Method. Participants responded to an online survey that included several Likert-scale measures for identity, motivation and retention variables, demographic questions, and two open-ended questions: Why did you choose to study computing? and What is it like for you to be a Black female student in computing? Findings. Key themes that emerged from our quantitative and qualitative analyses revealed the range of motivational factors driving Black undergraduate women to study computing and the nuances across their intersectional experiences in the computing education context. Additionally, Black undergraduate women located in HBCU computing education contexts demonstrated both similar and unique perspectives and experiences compared to their counterparts in non-HBCU settings. Conclusions. In addition to the isolation, pressure, power dynamics and epistemic violence experienced by Black women in the intersectional computing education context, the findings of this study display the unique cultural-social-psychological strengths of Black women in their positive valence and resilience, as well as their joy, empowerment, and achievement in computing. Increasing and complicating our knowledge of the motivations and intersectional experiences of Black women in undergraduate computing education, as well as understanding when and how their beliefs and perspectives vary across institutional context, will better inform efforts to retain them and promote their success both in college and into their careers.

Promoting Global Citizenship Education: Strategies for Developing Intercultural Competence in Students

This research article focuses on promoting global citizenship education by examining strategies for developing intercultural competence in students. Employing a qualitative methodology involving literature review and library research, the study aims to identify effective approaches to foster intercultural understanding and competence among students in diverse educational settings. The literature review explores various dimensions of global citizenship education, intercultural competence, and related pedagogical strategies. By analyzing existing research and theoretical frameworks, the article seeks to elucidate key concepts and best practices in promoting intercultural learning. The findings highlight the importance of incorporating experiential learning, cultural immersion programs, and cross-cultural communication activities into the curriculum to enhance students' intercultural competence. Additionally, the study underscores the significance of promoting critical reflection, empathy, and respect for diversity as foundational principles of global citizenship education. Moreover, the article discusses the role of educators in facilitating intercultural learning experiences and creating inclusive learning environments that encourage dialogue and collaboration among students from diverse backgrounds.

Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Customer review summarization (CRS) offers business owners summarized customer feedback. The functionality of CRS mainly depends on the sentiment analysis (SA) model; hence it needs an efficient SA technique. The aim of this study is to construct an SA model employing deep learning for CRS (SADL-CRS) to present summarized data and assist businesses in understanding the behavior of their customers. The SA model employing deep learning (SADL) and CRS phases make up the proposed automatic SADL-CRS model. The SADL consists of review preprocessing, feature extraction, and sentiment classification. The preprocessing stage removes irrelevant text from the reviews using natural language processing (NLP) methods. The proposed hybrid approach combines review-related features and aspect-related features to efficiently extract the features and create a unique hybrid feature vector (HF) for each review. The classification of input reviews is performed using a deep learning (DL) classifier long short-term memory (LSTM). The CRS phase performs the automatic summarization employing the outcome of SADL. The experimental evaluation of the proposed model is done using diverse research data sets. The SADL-CRS model attains the average recall, precision, and F1-score of 95.53%, 95.76%, and 95.06%, respectively. The review summarization efficiency of the suggested model is improved by 6.12% compared to underlying CRS methods.

Parent Awareness of and Attitudes Toward Gender-Affirming Pediatric Health Care: A Cross-Sectional Survey.

We aimed to characterize parent attitudes toward gender-affirming healthcare for transgender youth, from a general parent sample in a diverse urban setting. We surveyed Chicago parents through the Voices of Child Health in Chicago Parent Panel Survey via web and phone in English and Spanish from May-July 2022. We used both probability-based and nonprobability-based sampling, with calibration weights for the nonprobability sample. Parents responded about their awareness of a debate about and support for autonomy in gender-affirming healthcare for transgender youth and provided demographic information. We used descriptive analyses and logistic regression to examine predictors of awareness and support. Data were weighted to be representative of Chicago's parent population. Surveys were completed by 1,059 parents. The survey completion rate for the probability sample was 43.1% (a completion rate was not available for the nonprobability sample from online, opt-in surveys). Most parents were unaware of the debate about gender-affirming healthcare (56.0%). More than two-thirds of parents (68.9%) support decisions about gender-affirming healthcare being left to children, their parents, and their doctor. Parents who were aware of the debate were more likely to support gender-affirming healthcare (83.7%) than parents who were not aware (57.2%, p < .0001). Parents who were aware of the debate had higher odds of supporting gender-affirming care for youth (adjusted odds ratio= 3.00, 95% confidence interval: 1.93-4.66) in a multivariable logistic regression model. Broad parent support for gender-affirming healthcare for transgender youth is an important perspective to consider in policy discussions at state and federal levels.

Phosphorus Fertilization Can Improve Young Almond Tree Growth in Multiple Replant Settings

Young almond (Prunus amygdalus) orchards replanted where old orchards of stone fruits (Prunus sp.) have been removed are subject to physical, chemical, and biotic stressors. Among biotic challenges, for example, is almond/stone fruit replant disease (ARD; formally known as Prunus replant disease), which specifically suppresses the growth and yields of successive almond and other stone fruit plantings and is caused, in part, by a soil microbial complex. During four orchard trials representing different almond replant practices and scenarios in the San Joaquin Valley in California, we examined the impacts of phosphorus (P) fertilization on the growth of replanted almond. During all trials, P was applied to tree root zones just after replanting, and the impact was assessed according to trunk cross-sectional area (TCSA) growth for 2 years. Expt. 1 was performed where a previous almond orchard was cleared using whole orchard recycling (i.e., the old orchard was “chipped” and then turned into the soil). The land was replanted without preplant soil fumigation. We tested separate fertilizer treatments based on various P, nitrogen, micronutrient, and “complete” formulations. Expt. 2 was also performed where an old almond orchard was recycled, but the soil was preplant-fumigated before replanting. Here, we tested only P fertilization. Expts. 3 and 4 were conducted where an old peach (Prunus persica) orchard was removed. Here, P and nitrogen fertilizer treatments were tested among additional factors, including preplant soil fumigation (Expts. 3, 4) and whole orchard recycling chips (Expt. 4). During all four trials, P fertilization (P at 2.2 to 2.6 oz/tree within a few weeks after planting) significantly increased TCSA growth. The growth benefit was nuanced, however, by almond cultivar, date of replanting, rootstock, and other site-specific factors. Although P fertilization did not match the benefit of preplant soil fumigation for the management of ARD, our data indicated that P fertilization can improve the growth of young almond orchards in diverse replant settings with or without preplant soil fumigation and should be considered by California almond producers as a general best management practice.

English as Second Language Challenge Faced by Students: Comparative View of Public and Private Sector Students

The present study diagnosed English reading difficulties faced by students among primary school learners in public and private sector schools. To investigate the problem, teachers' perceptions were taken to find out the prevailing situation. A survey study was conducted using a rating scale instrument. Data analysis highlighted significant differences in pronunciation problems, with public school learners encountering more problems. On the other hand, public school learners also experience more problems regarding speed, but the difference between both sectors was not significant. Comprehension problems are comparable between the two groups. Gender-based disparities show that male students in public schools encounter more pronunciation challenges, though overall reading problems are significant for both genders in public schools. Recommendations include targeted programs for pronunciation improvement and enhanced comprehension instruction. This study contributes valuable insights for educators, policymakers, and curriculum developers aiming to address English reading challenges in diverse school settings.

A survey on brain MRI segmentation

Neurological disorders pose a significant challenge in medical diagnostics, requiring accurate and efficient analysis of brain Magnetic Resonance Imaging (MRI) scans. This study introduces a novel approach for enhanced neurological diagnosis through the application of deep learning techniques to automate the segmentation of brain structures in MRI images. The proposed method leverages the power of convolutional neural networks (CNNs) to extract intricate patterns and features from complex neuro imaging data. The research involves the development and training of a deep learning model capable of accurately delineating key anatomical regions, such as the cortex, hippocampus, and ventricles. The model is trained on a large data set of annotated MRI scans, optimizing its performance through rigorous validation processes. The utilization of deep learning enables the algorithm to learn and generalize from diverse imaging data, improving its adaptability to variations in patient demographics and scanner characteristics. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a diverse set of MRI datasets, encompassing various neurological conditions. Quantitative metrics, including Dice coefficient and Hausdorff distance, are employed to assess the segmentation accuracy compared to ground truth annotations. Additionally, the clinical relevance of the automated segmentation is validated through collaboration with neurologists and radiologists. The results demonstrate that the deep learning- enabled segmentation method consistently outperforms traditional image processing techniques, providing more accurate and reliable segmentation results. The proposed approach not only streamlines the diagnostic process but also has the potential to uncover subtle abnormalities that may be overlooked by manual inspection. In conclusion, the integration of deep learning into the segmentation of brain MRI scans presents a promising avenue for enhancing neurological diagnosis. The automated and precise delineation of brain structures contributes to the efficiency and accuracy of diagnostic workflows, ultimately improving patient outcomes and facilitating timely interventions in the realm of neurological disorders.

Modeling of High-Harmonic Generation in the C60 Fullerene Using Ab Initio, DFT-Based, and Semiempirical Methods.

We report calculations of the high-harmonic generation spectra of the C60 fullerene molecule carried out by employing a diverse set of real-time time-dependent quantum chemical methods. All methodologies involve expanding the propagated electronic wave function in bases consisting of the ground and singly excited time-independent eigenstates obtained through the solution of the corresponding linear-response equations. We identify the correlation and exchange effect in the spectra by comparing the results from methods relying on the Hartree-Fock reference determinant with those obtained using approaches based on the density functional theory with different exchange-correlation functionals. The effect of the full random-phase approximation treatment of the excited electronic states is also analyzed and compared with the configuration interaction singles and the Tamm-Dancoff approximation. We also showcase the fact that the real-time extension of the semiempirical method INDO/S can be effectively applied for an approximate description of laser-driven dynamics in large systems.

Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary algorithm

As a pivotal part of cathode air supply system, centrifugal air compressors play a central position in ensuring efficient operations of onboard fuel cells. To improve the overall performance of compressors, comprehensive performance tests were conducted and an integrated simulation model was developed by using computational fluid dynamics (CFD) methods. On this basis, the prediction performance of Linear-based Boosting models was investigated, and multi-objective optimization of impeller structural parameters was carried out through the Reference Vector Guided Evolutionary Algorithm (RVEA). Simulation results indicate that the impeller of the original compressor exhibits significant entropy increase, insufficient gas compression and serious energy dissipation, highlighting considerable room for design optimization. The eXtreme Gradient Boosting (XGBoost) model with 29 estimators has superior generalization ability and prediction performance, making it the preferred Boosting model for the compressor. Multi-objective optimizations have strong universality and rationality, resulting in a well-distributed and diversified final non-dominated solution set. The isentropic efficiency and pressure ratio of the Max_σ solution are improved by 18.7% and 70.1%, while those of the Max_ηc solution are enhanced by 23.0% and 48.9%, respectively. After optimization, the gas experiences reduced shock loss, diminished entropy increase and enhanced flow stability. These findings can provide data support, theoretical basis and directional guidance for performance improvement of centrifugal air compressors.

What High-Impact Practices Work for Minoritized Students? Institutional Inequities in College Learning Opportunities and Outcomes

This mixed-methods study examines what high-impact practices (HIPs) help improve the chances of college and career success among minoritized students. Building on transformative and ecological perspectives of HIPs, the study tracks U.S. 4-year college students’ learning opportunities towards bachelor’s degree completion followed by job employment or graduate/professional school enrollment. It explores a more comprehensive and diverse set of HIPs: academic and sociocultural engagement, study abroad, foreign language, co-op/internship, student teaching, advanced math and writing courses, research, and volunteer activities. Statistical analyses of the Beginning Postsecondary Students (BPS) data reveal racial and socioeconomic inequities in HIP participation among different types of institutions, with relatively favorable opportunities and outcomes in private or research (doctorate-granting) universities. The qualitative analyses of college student interviews offer insights into the questions of why and how HIPs work (or not) for minoritized students. The study gives evidence-based policy guidelines for improving minoritized students’ college and career success by tackling institutional inequities in high-impact practices and learning opportunities.

Abstract 2898: Development of the lipid kinase PIKfyve PROTAC degrader against neuroendocrine prostate cancer

Abstract Therapies targeting the androgen receptor (AR) as the main driver of prostate cancer (PCa) can lead to various mechanisms of resistance and promote progression to castration-resistant PCa (CRPC), which has a median survival of only 13-23 months. Amongst recurrent CRPC, 17%-30% of patients develop neuroendocrine prostate cancer (NEPC), which is a PCa subtype characterized by a unique histology. NEPC exhibits a loss of AR signaling during neuroendocrine transdifferentiation which results in resistance to AR-targeted therapies and gain of cell characteristics resembling poorly differentiated neuroendocrine tumors. Despite advances in the understanding of NEPC development, treatment options remain limited, with platinum-based chemotherapy as the first-line treatment for both de novo and treatment-induced NEPC. However, response to first-line chemotherapy in NEPC is short, with a median survival of only seven months. The poor prognosis of NEPC is attributed in part to late diagnosis and a lack of effective therapeutic agents. Our previous work demonstrated that NEPC and AR-negative PCa exhibit higher dependency on the lipid kinase PIKfyve than AR-positive CRPC. Thus, development of PIKfyve inhibitory therapies targeting the emergent vulnerabilities of NEPC or AR-negative forms of PCa is a promising approach. Using a proteolysis targeting chimera (PROTAC) technology, we designed a class of PIKfyve specific degrader. The in vitro degradation efficiency were determined by immunoblotting, and DC50s (degradation concentration at 50%) were calculated for top candidates. Pharmacokinetic and pharmacodynamic were evaluated for in vivo study. In summary, we have developed a class of PIKfyve specific degrader using the E3 ligase von Hippel-Lindau (VHL) as a ligand via a diverse set of linkers. Mechanistic studies revealed that it induced PIKfyve degradation in a VHL- and proteasome-dependent manner. Citation Format: Yuanyuan Qiao, Chungen Li, Yang Zheng, Xia Jiang, Sarah Nicole Yee, Caleb Cheng, Yi Bao, Yuping Zhang, Yuzhuo Wang, Zhen Wang, Ke Ding, Arul Chinnaiyan. Development of the lipid kinase PIKfyve PROTAC degrader against neuroendocrine prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2898.

Exploring knowledge and implementation gaps of activity-based therapy in centers lacking specialized spinal cord injury services: understanding therapists’ perspectives

Study designQualitative exploratoryObjectivesRehabilitation following spinal cord injury (SCI) is a life-long process involving healthcare in a variety of settings, including facilities lacking SCI-specific services (i.e., non-SCI-specialized centers). Activity-based therapy (ABT) is a neurorestorative approach involving intensive, task-specific movement practice below the injury level. This study explored the existing knowledge, perceptions, and implementation of ABT among physical and occupational therapists working in non-SCI-specialized centers.SettingCanadian hospitals and community clinicsDesign/methodsSemi-structured interviews were conducted with Canadian therapists who worked at non-SCI-specialized centers and treated at least one patient with SCI within the last 18 months. The Theoretical Domains Framework was used to develop interview questions that queried therapists’ experiences in delivering SCI rehabilitation, their understanding of ABT and experience with its implementation. Interviews were audio-recorded, transcribed verbatim and analyzed using interpretive description.ResultsFour physical therapists and three occupational therapists, from diverse settings (i.e., acute care, inpatient rehabilitation, long-term care, outpatient rehabilitation, rural outpatient clinic) participated. Three themes were identified: (1) Available knowledge, resources and therapy time in non-SCI-specialized centers challenge ABT implementation, (2) How current therapy practices in non-SCI-specialized centers align with ABT and (3) Desire for ABT knowledge. Although participants were not familiar with the term ABT, it was identified that they were unknowingly incorporating some components of ABT into their practice. Participants expressed a keenness to learn more about ABT.ConclusionCurrent knowledge and implementation of ABT in non-SCI-specialized centers is limited. Tailoring ABT education to therapists at non-SCI-specialized centers may increase ABT implementation.

Comparative analysis of the clinical features of nonvalvular atrial fibrillation among Tibetan, Han, and Hui patients in Qinghai Province, China

BackgroundGlobal nonvalvular AF rises, impacting health severely. In Qinghai, China's diverse setting, studying AF among varied ethnic groups is crucial ObjectivesThe purpose of this study was to compares cardiac features in AF among Tibetan, Han, and Hui patients to develop tailored prevention and treatment strategies for this region, the goal was to enhance the understanding of AF and provide an empirical basis for developing prevention and treatment strategies specific to this region MethodsThis study included a total of 3445 Tibetan, Han, and Hui patients diagnosed with nonvalvular atrial fibrillation and treated at the Qinghai Cardiovascular and Cerebrovascular Specialist Hospital, China, between January 2019 and January 2021. We analyzed the differences in cardiac structure, comorbidities, and other influencing factors among the different ethnic groups ResultsWe found significant differences in gender, age, smoking history, lone atrial fibrillation, left heart failure, dilated cardiomyopathy, and diabetes between Tibetan, Han, and Hui patients (P < 0.05). Tibetan, Han, and Hui patients also differed with regard to left ventricular end-diastolic volume, left ventricular ejection fraction, fractional shortening, NT-proBNP, glycated hemoglobin, red blood cell distribution width, platelet count, platelet hematocrit, platelet distribution width, homocysteine (Hcy), C-reactive protein, and superoxide dismutase (SOD) (P < 0.05) ConclusionOur study revealed variations in comorbidities, cardiac structure, and blood indexes among Tibetan, Han, and Hui AF patients, highlighting distinct patterns in complications and biomarker levels across ethnic groups.

Introduction to Sharia Economics and Financial Literacy for Elementary School Students

This research delves into the introduction of Sharia Economics and financial literacy among students at the International School of Kuala Lumpur. Recognizing the importance of financial education and ethical financial practices, especially in diverse educational settings, this study aims to bridge the gap in understanding Islamic finance principles and fostering financial literacy among students. The background of the problem lies in the necessity to equip students with comprehensive knowledge of Sharia Economics, aligning with the multicultural context of Kuala Lumpur. The primary objective of this research is to assess the effectiveness of integrating Sharia Economics education and financial literacy within the curriculum of an international school. Through a systematic methodology, including surveys, workshops, and curriculum analysis, the research evaluates the students' understanding and engagement with Sharia Economics principles and financial literacy concepts. The findings reveal the significance of incorporating Sharia Economics and financial literacy education in the curriculum, highlighting improved awareness and comprehension among students. The integration of real-life examples and interactive learning methods enhances students' practical understanding of ethical financial practices within the Islamic framework. In conclusion, this research underscores the importance of promoting Sharia Economics education and financial literacy among students in international schools, emphasizing its role in shaping responsible financial behaviors and fostering a deeper understanding of ethical economic principles. The implications of this study extend beyond the classroom, emphasizing the broader significance of financial education in promoting economic sustainability and ethical financial practices in diverse global contexts.

Art Crime Does not pay: Multiplexed Social Network Analysis in Cultural Heritage Trafficking Forensics

Nowadays, crimes connected to cultural heritage can feature as a staple for organised crime networks and act as financial enablers for international conflicts, including terrorism organisations and even inter-state conflicts, in several ways. Goods of cultural significance include a range of valuable objects related to human cultures, like works of art, historical artefacts, and other antiques, but also forgeries based on such objects. These crimes are almost always transnational, for instance, involving theft or looting in one country and goods moved across borders to be sold. This article presents an intelligence methodology based on Social Network Analysis (SNA) techniques that can support law enforcement agencies (LEAs) in their daily struggle against criminals that also pose a threat to national security. The methodology proposed is based on the building of a blended, multiplexed social network graph, deriving from the fusion of a diverse set of data sources, both in the open-source domain (OSINT) and in the classified domain. We will present data collection methods, correlation between sources, possible ways to generate blended links between individuals that retain information from different sources, and SNA techniques applied to intelligence and investigations. The article provides an answer to the following research questions: how we can detect and identify criminal activities and networks related to cultural goods crimes, how we can assist LEAs in countering illicit trafficking, and how we can ensure that art crime does not pay.

Physics on autopilot: exploring the use of an AI assistant for independent problem-solving practice

This study investigates the efficacy of large language model (LLM)-powered chatbots in guiding physics problem-solving, examining whether they can effectively supplement teacher-led learning. A customised chatbot was developed leveraging ChatGPT to provide step-by-step assistance through a structured problem-solving algorithm. Its impact was evaluated via an experimental study with 12th-grade physics students (N = 24) randomly assigned to a teacher-guided or chatbot-guided group for problem-solving practice. A Mann-Whitney U test revealed no significant differences in problem-solving competency between conditions. Qualitative analysis of conversational logs indicates the chatbot successfully emulated key teacher scaffolding behaviours. Our findings suggest AI tutors can deliver personalised, interactive support akin to human teachers, offering viable supplements to augment physics learning. Further research should explore optimising LLM training, human-chatbot balances, and impacts across diverse educational settings.

From insight to measurement: A self-assessment tool development for entry-level teachers’ instructional competence

This research addresses the critical need for assessing and enhancing instructional competence among entry-level teachers through the development of a quantitative self-assessment tool. The study follows best practices, employing Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and reliability testing with a sample of 1000 teachers. The resulting six-factor model includes effective lesson planning and development, alignment with educational and career goals, collaboration and stakeholder engagement, classroom management and leadership, well-being and stress management, and student engagement and passion, validated through CFA with a statistically significant fit. The scale development involves defining constructs, expert opinions, and rigorous statistical analyses, demonstrating high internal consistency in reliability testing. This study offers valuable insights into entry-level teachers’ instructional competence, recommending the refinement of the scale, integration into training programs, and prioritization of ongoing professional development. Suggestions for longitudinal impact research and context-specific exploration will deepen understanding. Overall, this research lays a strong foundation for improving teacher induction programs and enhancing the effectiveness of entry-level educators in diverse educational settings.

Anatomical and Technical Considerations in Fascicular Nerve Transfers for Foot Drop.

Foot drop is the common endpoint for a diverse set of nerve injuries, affecting over 128,000 in the United States each year. The wide and oftentimes underappreciated zone of injury, finite pace of regeneration, and exponential decay in the percentage of motor end-plates reinnervated over time may explain the limited success with natural recovery and/or peripheral nerve surgery. Previously described nerve transfer techniques also suffer from incompletely balancing the foot and ankle, poor donor-target nerve synergy, and/or not effectively bypassing the zone of injury. Based on an understanding of why past nerve techniques have failed to correct foot drop, a set of surgical principles can be codified to optimize functional outcomes. Surgical technique should be versatile enough to address foot drop from any of the three common pathways of injury (lumbar spine, sciatic nerve, and common peroneal nerve). For maximal stability, one should look to balance the foot in both dorsiflexion and eversion. Detailed description and illustrations of the branching anatomy for the peroneal and tibial nerves are provided, with specific application to nerve transfer reconstruction. With increasing familiarity using this once poorly understood anatomical region, limitations with past nerve transfer techniques may be overcome.