Genetics in neovascular age-related macular degeneration susceptibility and treatment response to anti-VEGF intravitreal injection: A case series study.

BACKGROUND: To identify genotypes associated with neovascular age-related macular degeneration (nAMD) and investigate the associations between genotype variations and anti-vascular endothelial growth factor (VEGF) treatment response. METHODS: This observational, retrospective, case series study enrolled patients diagnosed with nAMD who received anti-VEGF treatment in National Taiwan University Hospital with at least one-year follow-up between 2012 and 2020. A genome-wide association study (GWAS) was conducted on enrolled patients and controls. Correlations between the genotypes identified from GWAS and the treatment response of functional/anatomical biomarkers, including visual acuity (VA), presence of intraretinal or subretinal fluid (SRF), serous or fibrovascular pigmented epithelium detachment (PED), and disruption of the ellipsoid zone (EZ), were analysed. RESULTS: In total, 182 patients with nAMD and 1748 controls were enrolled. GWAS revealed 16 single nucleotide polymorphisms (SNPs) as risk loci for nAMD, including seven loci in CFH and ARMS2/HTRA1 and nine novel loci, including rs117517872 and rs79835234(COPB2-DT), rs7525578(RAP1A), rs2123738(LOC105376755), rs1374879(CNTN3), rs3812692(SAR1A), rs117501587(PRKCA), rs9965945(CNDP1), and rs189769231(MATK). Our study revealed rs800292(CFH), rs11200638(HTRA1), and rs2123738(LOC105376755) correlated with poor treatment response in VA (P = 0.005), SRF (P = 0.044), and fibrovascular PED (P = 0.007), respectively. Rs9965945(CNDP1) was correlated with poor response in disruption of EZ (P = 0.046) and serous PED (P = 0.049). CONCLUSIONS: Among the 16 SNPs found in the GWAS, four loci-CFH, ARMS2/HTRA1, and two novel loci-were correlated with the susceptibility of nAMD and anatomical/functional responses after anti-VEGF treatment.

Pharmacogenomic study-A pilot study of the effect of pharmacogenomic phenotypes on the adequate dosing of verapamil for migraine prevention.

OBJECTIVE: To investigate factors affecting the efficacy and tolerability of verapamil for migraine prevention using individual pharmacogenomic phenotypes. BACKGROUND: Verapamil has a wide range of dosing in headache disorders without reliable tools to predict the optimal doses for an individual. METHODS: This is a retrospective chart review examining adults with existing pharmacogenomic reports at Mayo Clinic who had used verapamil for migraine. Effects of six cytochrome P450 phenotypes on the doses of verapamil for migraine prevention were assessed. RESULTS: Our final analysis included 33 migraine patients (82% with aura). The mean minimum effective and maximum tolerable doses of verapamil were 178.2(20-320) mg and 227.9(20-480) mg. A variety of CYP2C9, CYP2D6, and CYP3A5 phenotypes were found, without significant association with the verapamil doses after adjusting for age, sex, body mass index, and smoking status. CONCLUSIONS: We demonstrated a wide range of effective and tolerable verapamil doses used for migraine in a cohort with various pharmacogenomic phenotypes.

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes. BACKGROUND: Headache frequency, defined as the number of days with any headache in a month (or 4 weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional NLP algorithms. METHODS: This was a retrospective cross-sectional study with patients identified from two tertiary headache referral centers, Mayo Clinic Arizona and Mayo Clinic Rochester. All neurology consultation notes written by 15 specialized clinicians (11 headache specialists and 4 nurse practitioners) between 2012 and 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model, (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) model zero-shot, (3) GPT-2 QA model few-shot training fine-tuned on clinical notes, and (4) GPT-2 generative model few-shot training fine-tuned on clinical notes to generate the answer by considering the context of included text. RESULTS: The mean (standard deviation) headache frequency of our training and testing datasets were 13.4 (10.9) and 14.4 (11.2), respectively. The GPT-2 generative model was the best-performing model with an accuracy of 0.92 (0.91, 0.93, 95% confidence interval [CI]) and R2 score of 0.89 (0.87, 0.90, 95% CI), and all GPT-2-based models outperformed the ClinicalBERT model in terms of exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy of 0.27 (0.26, 0.28), it demonstrated a high R2 score of 0.88 (0.85, 0.89), suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. CONCLUSION: We developed a robust information extraction model based on a state-of-the-art large language model, a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT-2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub. Additional fine-tuning of the algorithm might be required when applied to different health-care systems for various clinical use cases.

Adherence to dietary guidelines associated with lower medical service utilization in preschoolers: a longitudinal study.

OBJECTIVE: We aimed to evaluate the association between dietary guideline adherence and overall, outpatient, and emergency medical service utilization in Taiwanese preschoolers. METHODS: We selected 614 preschoolers (2-6 years) who had one day of 24-h dietary recall data from the 2013-2016 Nutrition and Health Survey in Taiwan. The Taiwanese Children Healthy Eating Index (TCHEI) was developed on the basis of Taiwanese Food-Based Dietary Guidelines; it assesses dietary adequacy and eating behavior. Data on the participants' outpatient and emergency medical service utilization were obtained for 2013-2018 from the National Health Insurance Research Database. A multivariable generalized linear model was used to evaluate the association between the TCHEI and medical service utilization for all disease and respiratory diseases. RESULTS: After adjustment for confounding factors, children aged 2-3 years in the Tertile (T) 2 and T3 groups of the TCHEI exhibited 25% (95% CI 0.69-0.83) and 16% (95% CI 0.77-0.92) lower overall medical visits, respectively. The same pattern was noted in the outpatient and emergency visits for all diseases and respiratory diseases. The children aged 4-6 years in the T2 group exhibited 15% (95% CI 0.80-0.91) and 11% (95% CI 0.82-0.97) lower overall visits and visits for respiratory diseases, respectively. Moreover, preschoolers in the T2 group exhibited lower overall medical expenditures than did those in the T1 group. CONCLUSIONS: TCHEI score was positively correlated with better nutritional status. Optimal dietary intake associated with lower medical service utilization among Taiwan preschoolers.

Use of Technetium-99m-Pyrophosphate Single-Photon Emission Computed Tomography/Computed Tomography in Monitoring Therapeutic Changes of Eplontersen in Patients With Hereditary Transthyretin Amyloid Cardiomyopathy.

BACKGROUND: Hereditary transthyretin amyloid cardiomyopathy (hATTR-CM) is a progressive and fatal disease. Recent evidence indicates that bone scintigraphy may serve as a tool to monitor the effectiveness of hATTR-CM treatment. The objective of this study was to examine how eplontersen therapy influences the semiquantitative uptake of technetium-99m-pyrophosphate in individuals diagnosed with hATTR-CM. METHODS AND RESULTS: We retrospectively analyzed a prospective cohort from the NEURO-TTRansform trial, including patients with hATTR-CM receiving eplontersen (45 mg/4 weeks). A control group comprised patients with hATTR-CM who had not received eplontersen, inotersen, tafamidis, or patisiran. Technetium-99m-pyrophosphate single-photon emission computed tomography/computed tomography was conducted at baseline and during follow-up. Thirteen patients with hATTR-CM were enrolled, with 6 receiving eplontersen and 7 serving as the control group. The median follow-up time was 544 days. The eplontersen group exhibited a significant decrease in volumetric heart and lung ratio (3.774 to 2.979, P=0.028), whereas the control group showed no significant change (4.079 to 3.915, P=0.237). Patients receiving eplontersen demonstrated a significantly greater reduction in volumetric heart and lung ratio compared with the control group (-20.7% versus -3.4%, P=0.007). CONCLUSIONS: The volumetric heart and lung ratio used to quantify technetium-99m-pyrophosphate uptake showed a significant reduction subsequent to eplontersen treatment in individuals diagnosed with hATTR-CM. These findings suggest the potential efficacy of eplontersen in treating hATTR-CM and highlight the value of technetium-99m-pyrophosphate single-photon emission computed tomography/computed tomography as a tool for monitoring therapeutic effectiveness.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising.

BACKGROUND: Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis. NEW METHOD: In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered. RESULT: SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates. COMPARISON WITH EXISTING METHODS: SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering. CONCLUSION: The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Complete genomic profiles of 1496 Taiwanese reveal curated medical insights.

INTRODUCTION: The population of Taiwan has a long history of ethno-cultural evolution. The Taiwanese population was isolated from other large populations such as the European, Han Chinese, and Japanese population. The Taiwan Biobank (TWB) project has built a nationwide database, particularly for personal whole-genome sequence (WGS) to facilitate basic and clinical collaboration nationally and internationally, making it one of the most valuable public datasets of the East Asian population. OBJECTIVES: This study provides comprehensive medical genomic findings from TWB WGS data, for better characterization of disease susceptibility and the choice of ideal treatment regimens in Taiwanese population. METHODS: We reanalyzed 1496 WGS using a PrecisionFDA Truth challenge winner method Sentieon DNAscope. Single nucleotide variants (SNV) and small insertions/deletions (INDEL) were benchmarked. We also analyzed pharmacogenomic (PGx) drug-associated alleles, and copy number variants (CNV). Multiple practicing clinicians reviewed and curated the clinically significant variants. Variant annotations can be browsed at TaiwanGenomes (https://genomes.tw). RESULTS: We found that each participant had an average of 6,870.7 globally novel variants and 75.3% (831/1103) of the participants harbored at least one PharmGKB-selected high evidence level human leukocyte antigen (HLA) risk allele. 54 PharmGKB-reported high-level instances of evidence of Cytochrome P450 variant-drug pairs, with a population frequency of over 13.2%. We also identified 23 variants in the ACMG secondary finding V3 gene list from 25 participants, suggesting that 1.67% (25/1496) of the population is harboring at least one medical actionable variant. Our carrier status analyses suggest that one in 25 couples (3.94%) would risk having offspring with at least one pathogenic variant, which is in line with rates found in Japan and Singapore. For pathogenic CNV, we detected 6.88% and 2.02% carrier rates for alpha thalassemia and spinal muscular atrophy, respectively. CONCLUSION: Our study highlights the overall medical insights of a complete Taiwanese genomic profile.

Remission and long-term remission of pediatric-onset systemic lupus erythematosus.

Objectives: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with diverse clinical presentations and prognoses. Remission can be achieved with or without glucocorticoid (GC) use, and several recent studies have suggested that long-term remission can be achieved in a small portion of patients. Nevertheless, few studies have investigated remission or long-term remission in the pediatric-onset SLE subgroup. This study analyzed the characteristics and factors associated with long-term remission and GC use in pediatric-onset SLE. Methods: We enrolled 226 patients aged <18 years who received a diagnosis of SLE between January 2006 and December 2016. Three remission condition groups were defined: (A) complete remission, (B) clinical remission off GCs, and (C) clinical remission on GCs. Long-term remission was defined as remission for more than 5 years. We analyzed the treatment durations before remission, durations of remission, and risk factors for non-remission with persistent GC use. Results: During follow-up, 8 patients (3.5%) achieved complete remission, 35 patients (15.5%) achieved clinical remission off GCs, and 93 patients (41.2%) achieved clinical remission on GCs. In groups A, B, and C, 12.5%, 68.6%, and 65.6% of patients, respectively, remained in remission for >1 year. Conclusion: This study assessed remission of pediatric-onset SLE. Up to 60.2% of patients had clinical remission after treatment, and 19% of patients achieved remission off GCs. Long-term remission is rarer in pediatric-onset SLE than in adult-onset SLE.

Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.

Purpose: The inherent characteristics of transthoracic echocardiography (TTE) images such as low signal-to-noise ratio and acquisition variations can limit the direct use of TTE images in the development and generalization of deep learning models. As such, we propose an innovative automated framework to address the common challenges in the process of echocardiography deep learning model generalization on the challenging task of constrictive pericarditis (CP) and cardiac amyloidosis (CA) differentiation. Approach: Patients with a confirmed diagnosis of CP or CA and normal cases from Mayo Clinic Rochester and Arizona were identified to extract baseline demographics and the apical 4 chamber view from TTE studies. We proposed an innovative preprocessing and image generalization framework to process the images for training the ResNet50, ResNeXt101, and EfficientNetB2 models. Ablation studies were conducted to justify the effect of each proposed processing step in the final classification performance. Results: The models were initially trained and validated on 720 unique TTE studies from Mayo Rochester and further validated on 225 studies from Mayo Arizona. With our proposed generalization framework, EfficientNetB2 generalized the best with an average area under the curve (AUC) of 0.96 (±0.01) and 0.83 (±0.03) on the Rochester and Arizona test sets, respectively. Conclusions: Leveraging the proposed generalization techniques, we successfully developed an echocardiography-based deep learning model that can accurately differentiate CP from CA and normal cases and applied the model to images from two sites. The proposed framework can be further extended for the development of echocardiography-based deep learning models.

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.

Background: Headache frequency, defined as the number of days with any headache in a month (or four weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional natural language processing (NLP) algorithms. Methods: This was a retrospective cross-sectional study with human subjects identified from three tertiary headache referral centers- Mayo Clinic Arizona, Florida, and Rochester. All neurology consultation notes written by more than 10 headache specialists between 2012 to 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training fine-tuned on Mayo Clinic notes to generate the answer by considering the context of included text. Results: The GPT-2 generative model was the best-performing model with an accuracy of 0.92[0.91 - 0.93] and R2 score of 0.89[0.87, 0.9], and all GPT2-based models outperformed the ClinicalBERT model in terms of the exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R2 score 0.88[0.85, 0.89], suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. Conclusion: We developed a robust model based on a state-of-the-art large language model (LLM)- a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub.

Using Augmented Mean Arterial Pressure to Identify High Mortality Risk Patients With Moderate Aortic Stenosis.

OBJECTIVE: To study the usefulness of a novel echocardiographic marker, augmented mean arterial pressure (AugMAP = [(mean aortic valve gradient + systolic blood pressure) + (2 × diastolic blood pressure)] / 3), in identifying high-risk patients with moderate aortic stenosis (AS). PATIENTS AND METHODS: Adults with moderate AS (aortic valve area, 1.0-1.5 cm2) at Mayo Clinic sites from January 1, 2010, through December 31, 2020, were identified. Baseline demographic, echocardiographic, and all-cause mortality data were retrieved. Patients were grouped into higher and lower AugMAP groups using a cutoff value of 80 mm Hg for analysis. Kaplan-Meier and Cox regression models were used to assess the performance of AugMAP. RESULTS: A total of 4563 patients with moderate AS were included (mean ± SD age, 73.7±12.5 years; 60.5% men). Median follow-up was 2.5 years; 36.0% of patients died. The mean ± SD left ventricular ejection fraction (LVEF) was 60.1%±11.4%, and the mean ± SD AugMAP was 99.1±13.1 mm Hg. Patients in the lower AugMAP group, with either preserved or reduced LVEF, had significantly worse survival performance (all P<.001). Multivariate Cox regression showed that AugMAP (hazard ratio, 0.962; 95% CI, 0.942 to 0.981 per 5-mm Hg increase; P<.001) and AugMAP less than 80 mm Hg (hazard ratio, 1.477; 95% CI, 1.241 to 1.756; P<.001) were independently associated with all-cause mortality. CONCLUSION: AugMAP is a simple and effective echocardiographic marker to identify high-risk patients with moderate AS independent of LVEF. It can potentially be used in the candidate selection process if moderate AS becomes indicated for aortic valve intervention in the future.

Cortical excitability in patients with REM sleep behavior disorder with abnormal TRODAT-1 SPECT scan: an insight into prodromal Parkinson's disease.

Introduction: REM Sleep Behavior Disorder (RBD) has been highlighted to identify a patient with prodromal Parkinson's disease (PD). Although many studies focus on biomarkers to predict an RBD patient's evolution from prodromal PD to clinical PD, the neurophysiological perturbation of cortical excitability has not yet been well elucidated. Moreover, no study describes the difference between RBD with and without abnormal TRODAT-1 SPECT. Methods: By measuring the amplitude of motor evoked potentials (MEP), the cortical excitability changes after transcranial magnetic stimulation (TMS) were evaluated in 14 patients with RBD and eight healthy controls (HC). Seven of the 14 patients with RBD showed abnormal TRODAT-1 (TRA-RBD), and seven were normal (TRN-RBD). The tested parameters of cortical excitability include resting motor threshold (RMT), active motor threshold (AMT), short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), contralateral silence period (CSP), and input-output recruitment curve. Results: The RMT and AMT showed no difference among the three studied groups. There was only SICI at inter-stimuli-interval 3 ms revealing group differences. The TRA-RBD demonstrated significant differences to HC in these aspects: decreased SICI, increased ICF, shortening of CSP, and augmented MEP amplitude at 100% RMT. Moreover, the TRA-RBD had a smaller MEP facilitation ratio at 50% and 100% of maximal voluntary contraction when compared to TRN-RBD. The TRN-RBD did not present any difference to HC. Conclusion: We showed that TRA-RBD shared similar cortical excitability changes with clinical PD. These findings would provide further insight into the concept that RBD is the highly prevalent entity in prodromal PD.

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

Etv5a Suppresses Neural Progenitor Cell Proliferation by Inhibiting sox2 Transcription.

Neural progenitor cells are self-renewable, proliferative, and multipotent cell populations that generate diverse types of neurons and glia to build the nervous system. Transcription factors play critical roles in regulating various cellular processes; however, the transcription factors that regulate the development of neural progenitors are yet to be identified. In the present study, we demonstrated that zebrafish etv5a is expressed in the neural progenitor cells of the neuroectoderm. Downregulation of endogenous Etv5a function by etv5a morpholino or an etv5a dominant-negative variant increased the proliferation of sox2-positive neural progenitor cells, accompanied by inhibition of neurogenesis and gliogenesis. These phenotypes in Etv5a-depleted embryos could be rescued by a co-injection with etv5a cRNA. Etv5a overexpression reduced sox2 expression. Direct binding of Etv5a to the regulatory elements of sox2 was affirmed by chromatin immunoprecipitation. These data revealed that Etv5a directly suppressed sox2 expression to reduce the proliferation of neural progenitor cells. In addition, the expression of foxm1, a putative target gene of Etv5a and a direct upstream transcription factor of sox2, was upregulated in Etv5a-deficient embryos. Moreover, the suppression of Foxm1 function by the foxm1 dominant-negative construct nullified the phenotype of upregulated sox2 expression caused by Etv5a deficiency. Overall, our results indicated that Etv5a regulates the expression of sox2 via direct binding to the sox2 promoter and indirect regulation by inhibiting foxm1 expression. Hence, we revealed the role of Etv5a in the transcriptional hierarchy that regulates the proliferation of neural progenitor cells.

Identification of the Time Period during Which BMP Signaling Regulates Proliferation of Neural Progenitor Cells in Zebrafish.

Bone morphogenetic protein (BMP) signaling regulates neural induction, neuronal specification, and neuronal differentiation. However, the role of BMP signaling in neural progenitors remains unclear. This is because interruption of BMP signaling before or during neural induction causes severe effects on subsequent neural developmental processes. To examine the role of BMP signaling in the development of neural progenitors in zebrafish, we bypassed the effect of BMP signaling on neural induction and suppressed BMP signaling at different time points during gastrulation using a temporally controlled transgenic line carrying a dominant-negative form of Bmp receptor type 1aa and a chemical inhibitor of BMP signaling, DMH1. Inhibiting BMP signaling from 8 hpf could bypass BMP regulation on neural induction, induce the number of proliferating neural progenitors, and reduce the number of neuronal precursors. Inhibiting BMP signaling upregulates the expression of the Notch downstream gene hairy/E(spl)-related 2 (her2). Inhibiting Notch signaling or knocking down the Her2 function reduced neural progenitor proliferation, whereas inactivating BMP signaling in Notch-Her2 deficient background restored the number of proliferating neural progenitors. These results reveal the time window for the proliferation of neural progenitors during zebrafish development and a fine balance between BMP and Notch signaling in regulating the proliferation of neural progenitor cells.

Heterotopic Digital Replantation in Mutilating Hand Injuries: An Algorithmic Approach Based on 53 Cases and Literature Review.

BACKGROUND: Reconstruction of the mutilated hand is one of the most difficult challenges for hand microsurgeons. When multiple digits are amputated, orthotopic digital replantation of the available remnants may not adequately restore the hand function. In such cases, heterotopic digital replantation may provide a more functional reconstruction. METHODS: Between 1997 and 2018, 53 patients with mutilating hand injuries were treated with heterotopic digital replantation at our institution. A retrospective chart review was conducted to determine the details of the injury, indications for heterotopic digital replantation, and functional outcomes. RESULTS: In total, 173 digits were amputated from 53 patients (one patient suffered from bilateral hand injuries, so totally 54 hands). Sixty-eight digits underwent heterotopic digital replantation, 30 digits had orthotopic digital replantation, and 75 stumps were terminalized. The survival rate of digits treated by heterotopic digital replantation and orthotopic digital replantation was 83.8% (57/68) and 86.7% (26/30), respectively (p = 1). Tripod grip was achieved in 83.3% (45/54) of patients following replantation and optional secondary reconstructive surgeries. CONCLUSION: Heterotopic digital replantation is a practical and reliable method for achieving optimal hand function following mutilating hand injuries. The basic principles are to restore a functional thumb in the first instance, followed by at least two adjacent fingers against which the thumb can oppose. This method is particularly indicated when orthotopic digital replantation of the available amputated parts would yield a suboptimal result.