Transcription factor KLF9 inhibits the proliferation, invasion, and migration of pancreatic cancer cells by repressing KIAA1522.

AIM: Pancreatic cancer (PC) has a poor prognosis and high mortality. Kruppel-like factor 9 (KLF9), a transcription factor, is aberrantly expressed in various neoplasms. The current study sought to analyze the functional role of KLF9 in the proliferation, invasion, and migration of PC cells. METHODS: The expression patterns of KLF9 and KIAA1522 in normal pancreatic cells (HPDE-C7) and PC cells (Panc 03.27, BxPc3, SW1990) were determined by real-time quantitative polymerase chain reaction and Western blot assay. After treatment of KLF9 overexpression, proliferation, invasion, and migration were evaluated by cell counting kit-8, 5-ethynyl-2'-deoxyuridine staining, and Transwell assays. The binding of KLF9 to the KIAA1522 promoter was analyzed by dual-luciferase assay and chromatin immunoprecipitation. The rescue experiment was conducted to analyze the role of KIAA1522. RESULTS: KLF9 was downregulated, while KIAA1522 was upregulated in PC cells. KLF9 overexpression mitigated the proliferation, invasion, and migration of PC cells. Enrichment of KLF9 led to inhibition of the KIAA1522 promoter and repressed KIAA1522 expression. KIAA1522 overexpression neutralized the inhibitory role of KLF9 in PC cell functions. CONCLUSION: KLF9 is enriched in the KIAA1522 promoter and negatively regulates KIAA1522 expression, thereby mitigating the proliferation, invasion, and migration of PC cells.

Exercise training modalities in prediabetes: a systematic review and network meta-analysis.

Background: Lifestyle modification based on exercise intervention is still the primary way to delay or reverse the development of diabetes in patients with prediabetes. However, there are still challenges in setting up a detailed exercise prescription for people with prediabetes. This study mainly ranks exercise prescriptions by comparing the improvement of glucose and lipid metabolism and the level of weight loss in patients. Method: All studies on exercise intervention in prediabetes were identified by searching five electronic databases. Risk assessment and meta-analysis were performed on eligible studies. Results: Twenty-four studies involving 1946 patients with prediabetes and seven exercise intervention models were included in the final analysis. The meta-analysis showed that exercise of any type was more effective for glycemic control in prediabetes than no exercise. However, the changes in blood glucose were moderate. In prediabetes, combining moderate-intensity aerobic exercise with low-to moderate-load resistance training showed the most significant improvements in glycosylated hemoglobin (HbA1c), body mass index (BMI), body weight (BW), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL) (P-score=0.82; 0.70; 0.87; 1; 0.99), low-to moderate-load resistance training showed the most significant improvements in fasting blood glucose (FBG) (P-score=0.98), the vigorous-intensity aerobic exercise showed the most significant improvements in 2-hour post-meal blood glucose (2hPG) and systolic blood pressure (SBP) (P-score=0.79; 0.78), and moderate-intensity aerobic exercise showed the most significant improvements in diastolic blood pressure (DBP) (P-score=0.78). Conclusion: In summary, moderate-intensity aerobic exercise, low-to moderate-load resistance training and the combination of both have beneficial effects on glycemic control, weight loss, and cardiovascular health in patients with prediabetes. These findings provide valuable guidance for rehabilitation clinicians and patients alike to follow. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD 42021284922.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Progress of plant polyphenol extracts in treating depression by anti-neuroinflammatory mechanism: A review.

There is a growing body of evidence supporting the involvement of central nervous system inflammation in the pathophysiology of depression. Polyphenols are a diverse group of compounds known for their antioxidative and anti-inflammatory properties. They offer a promising and effective supplementary approach to alleviating neuropsychiatric symptoms associated with inflammation-induced depression. This paper provides a summary of the potential anti-neuroinflammatory mechanisms of plant polyphenol extracts against depression. This includes direct interference with inflammatory regulators and inhibition of the expression of pro-inflammatory cytokines. Additionally, it covers downregulating the expression of pro-inflammatory cytokines by altering protein kinases or affecting the activity of the signaling pathways that they activate. These pathways interfere with the conduction of signaling molecules, resulting in the destruction and reduced synthesis of all inflammatory mediators and cytokines. This reduces the apoptosis of neurons and plays a neuroprotective role. This paper provides a theoretical basis for the clinical application of plant polyphenols.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.

OBJECTIVE: The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results. METHODS: The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). RESULTS: In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. CONCLUSION: To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Detection of Medication Mentions and Medication Change Events in Clinical Notes Using Transformer-Based Models.

In this paper, we address the related tasks of medication extraction, event classification, and context classification from clinical text. The data for the tasks were obtained from the National Natural Language Processing (NLP) Clinical Challenges (n2c2) Track 1. We developed a named entity recognition (NER) model based on BioClinicalBERT and applied a dictionary-based fuzzy matching mechanism to identify the medication mentions in clinical notes. We developed a unified model architecture for event classification and context classification. The model used two pre-trained models-BioClinicalBERT and RoBERTa to predict the class, separately. Additionally, we applied an ensemble mechanism to combine the predictions of BioClinicalBERT and RoBERTa. For event classification, our best model achieved 0.926 micro-averaged F1-score, 5% higher than the baseline model. The shared task released the data in different stages during the evaluation phase. Our system consistently ranked among the top 10 for Releases 1 and 2.

Evaluation of a modified ultrasound-assisted technique for mid-thoracic epidural placement: a prospective observational study.

BACKGROUND: Although mid-thoracic epidural analgesia benefits patients undergoing major surgery, technical difficulties often discourage its use. Improvements in technology are warranted to improve the success rate on first pass and patient comfort. The previously reported ultrasound-assisted technique using a generic needle insertion site failed to demonstrate superiority over conventional landmark techniques. A stratified needle insertion site based on sonoanatomic features may improve the technique. METHODS: Patients who presented for elective abdominal or thoracic surgery requesting thoracic epidural analgesia for postoperative pain control were included in this observational study. A modified ultrasound-assisted technique using a stratified needle insertion site based on ultrasound images was adopted. The number of needle passes, needle skin punctures, procedure time, overall success rate, and incidence of procedure complications were recorded. RESULTS: One hundred and twenty-eight subjects were included. The first-pass success and overall success rates were 75% (96/128) and 98% (126/128), respectively. In 95% (122/128) of patients, only one needle skin puncture was needed to access the epidural space. The median [IQR] time needed from needle insertion to access the epidural space was 59 [47-122] seconds. No complications were observed during the procedure. CONCLUSIONS: This modified ultrasound-assisted mid-thoracic epidural technique has the potential to improve success rates and reduce the needling time. The data shown in our study may be a feasible basis for a prospective study comparing our ultrasound-assisted epidural placements to conventional landmark-based techniques.

Baicalin-peptide supramolecular self-assembled nanofibers effectively inhibit ferroptosis and attenuate doxorubicin-induced cardiotoxicity.

Doxorubicin, an anthracycline chemotherapeutic agent, elicits a deleterious cardiotoxicity known as doxorubicin-induced cardiomyopathy (DIC) that circumscribes its chemotherapy utility for malignancies. Recent empirical evidence implicates ferroptosis, an iron-dependent form of regulated cell death, as playing a pivotal role in the pathogenesis of DIC. We postulated that anti-ferroptosis agents may constitute a novel therapeutic strategy for mitigating DIC. To test this hypothesis, we engineered baicalin-peptide supramolecular self-assembled nanofibers designed to selectively target the angiotensin II type I receptor (AT1R), which is upregulated in doxorubicin-damaged cardiomyocytes. This enabled targeted delivery of baicalin, a natural antioxidant compound, to inhibit ferroptosis in the afflicted myocardium. In vitro, the nanofibers ameliorated cardiomyocyte death by attenuating peroxide accumulation and suppressing ferroptosis. In a murine model of DIC, AT1R-targeted baicalin delivery resulted in efficacious cardiac accumulation and superior therapeutic effects compared to systemic administration. This investigation delineates a promising framework for developing targeted therapies that alleviate doxorubicin-induced cardiotoxicity by inhibiting the ferroptosis pathway in cardiomyocytes.

Ultrasound-assisted lumbar puncture with a horizontal and perpendicular paramedian approach based on positioning in patients with abnormal spinal anatomy: a case report and technical description.

BACKGROUND: The use of ultrasound has been reported to be beneficial in challenging neuraxial procedures. The angled probe is responsible for the main limitations of previous ultrasound-assisted techniques. We developed a novel technique for challenging lumbar puncture, aiming to locate the needle entry point which allowed for a horizontal and perpendicular needle trajectory and thereby addressed the drawbacks of earlier ultrasound-assisted techniques. CASE PRESENTATION: Patient 1 was an adult patient with severe scoliosis who underwent a series of intrathecal injections of nusinersen. The preprocedural ultrasound scan revealed a cephalad probe's angulation (relative to the edge of the bed) in the paramedian sagittal oblique view, and then the probe was rotated 90° into a transverse plane and we noted that a rocking maneuver was required to obtain normalized views. Then the shoulders were moved forward to eliminate the need for cephalad angulation of the probe. The degree of rocking was translated to a lateral offset from the midline of the spine through an imaginary lumbar puncture's triangle model, and a needle entry point was marked. The spinal needle was advanced through this marking-point without craniocaudal and lateromedial angulation, and first-pass success was achieved in all eight lumbar punctures. Patient 2 was an elderly patient with ankylosing spondylitis who underwent spinal anesthesia for transurethral resection of the prostate. The patient was positioned anteriorly obliquely to create a vertebral rotation that eliminated medial angulation in the paramedian approach. The procedure succeeded on the first pass. CONCLUSIONS: This ultrasound-assisted paramedian approach with a horizontal and perpendicular needle trajectory may be a promising technique that can help circumvent challenging anatomy. Larger case series and prospective studies are warranted to define its superiority to alternative approaches of lumbar puncture for patients with difficulties.

Pausing behavior of late bilingual and monolingual English speakers.

This study examined the differences in the pausing behavior between native and non-native English speakers. Specifically, it examined the location and duration of pauses in relation to the syntactic and lexical complexity of the clauses in which these pauses occur and the nature of the prosodic phrasing of the utterances containing pauses. Speech samples from 10 native (L1) English and 10 Mandarin non-native English speakers from the Archive of L1 and L2 Scripted and Spontaneous Transcripts and Recordings (ALLSSTAR) were included in the analysis. The results showed that lower-level prosodic boundaries and syntactically complex phrases were associated with significantly longer pause duration in the L2 speech. Additionally, phrases with less frequent words tended to induce longer pauses. These findings suggest that insufficient knowledge of the L2 syntax, lexicon, and prosody might determine the location and duration of pauses and ultimately affect the speech fluency of L2 speakers.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at the AMIA 2023 Annual Symposium.

The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of five tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). In total, 29 teams registered, representing 18 countries. In this paper, we present the annotated corpora, a technical summary of the systems, and the performance results. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. To facilitate future work, the datasets-a total of 61,353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Allelopathic effect of phenolic acids in various extracts of wheat against Fusarium wilt in faba bean.

Allelopathy is the main reason for disease control in intercropping systems. The effects of different extracts, root secretions and phenolic acids of wheat and faba bean on Fusarium oxysporum f. fabae (FOF) growth were studied to explore the allelopathy mechanism of wheat in disease control of faba bean. Various extracts and root exudate of faba bean were promoted but those of wheat inhibited the growth and reproduction of FOF. High-performance liquid chromatography revealed significant differences in the contents of phenolic acids in the various extracts and root exudate of wheat and faba bean. The total content of syringic acid (SA) was much higher, but that of other five phenolic acids were lower in wheat than in faba bean. The in vitro addition of these phenolic acids revealed that cinnamic acid (CA), p-hydroxybenzoic acid (PHBA), benzoic acid (BA), vanillic acid (VA) and ferulic acid (FA) exhibited significant promoting effects and SA exhibited strong inhibitory effects on the growth of FOF. These results suggest that the inhibitory effect of various extracts and root exudates from wheat on FOF growth may be due to differences in phenolic acid content and high levels of SA.

Augmented Super-Resolution Radial Fluctuations (aSRRF) Pushing the Limits of Structured Illumination Microscopy.

Structured illumination microscopy (SIM) is a versatile super-resolution technique known for its compatibility with a wide range of probes and fast implementation. While 3D SIM is capable of achieving a spatial resolution of ∼120 nm laterally and ∼300 nm axially, attempting to further enhance the resolution through methods such as nonlinear SIM or 4-beam SIM introduces complexities in optical configurations, increased phototoxicity, and reduced temporal resolution. Here, we have developed a novel method that combines SIM with augmented super-resolution radial fluctuations (aSRRF) utilizing a single image through image augmentation. By applying aSRRF reconstruction to SIM images, we can enhance the SIM resolution to ∼50 nm isotopically, without requiring any modifications to the optical system or sample acquisition process. Additionaly, we have incorporated the aSRRF approach into an ImageJ plugin and demonstrated its versatility across various fluorescence microscopy images, showcasing a remarkable two-fold resolution increase.

An aspect-level sentiment analysis dataset for therapies on Twitter.

The dataset described is an aspect-level sentiment analysis dataset for therapies, including medication, behavioral and other therapies, created by leveraging user-generated text from Twitter. The dataset was constructed by collecting Twitter posts using keywords associated with the therapies (often referred to as treatments). Subsequently, subsets of the collected posts were manually reviewed, and annotation guidelines were developed to categorize the posts as positive, negative, or neutral. The dataset contains a total of 5364 posts mentioning 32 therapies. These posts are further categorized manually into 998 (18.6%) positive, 619 (11.5%) negatives, and 3747 (69.9%) neutral sentiments. The inter-annotation agreement for the dataset was evaluated using Cohen's Kappa score, achieving an 0.82 score. The potential use of this dataset lies in the development of automatic systems that can detect users' sentiments toward therapies based on their posts. While there are other sentiment analysis datasets available, this is the first that encodes sentiments associated with specific therapies. Researchers and developers can utilize this dataset to train sentiment analysis models, natural language processing algorithms, or machine learning systems to accurately identify and analyze the sentiments expressed by consumers on social media platforms like Twitter.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems.

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology.

Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels.

Prediction of water transport properties on an anisotropic wetting surface via deep learning.

Understanding the water flow behavior on an anisotropic wetting surface is of practical significance in nanofluidic devices for their performance improvement. However, current methods of experiments and simulations face challenges in measuring water transportation in real time and visually displaying it. Here, molecular dynamics simulation was integrated with our developed multi-attribute point cloud dataset and a customized network of deep learning to achieve mapping from an anisotropic wetting surface to the static and dynamic behaviors of water molecules and realize the high-performance prediction of water transport behavior. More importantly, for the chaotic phenomenon of water molecule flow caused by thermal fluctuation and limited sampling, we proposed a nanoparticle tracking optimization strategy to improve the prediction performance of the velocity field. The prediction results proved that the deep learning framework proposed in this work had superior performance in terms of accuracy, computational cost and visualization, and had the potential for generality to model the transport behavior of different molecules. Our framework can be expected to motivate the development of real-time water flow prediction at an interface and contribute to the optimization and design of surface structures in nanofluidic devices.

Conocarpus lancifolius (Combretaceae): Pharmacological Effects, LC-ESI-MS/MS Profiling and In Silico Attributes.

In folklore medicine, Conocarpus lancifolius is used to treat various illnesses. The main objective of this study was a comprehensive investigation of Conocarpus lancifolius leaf aqueous extract (CLAE) for its antioxidant, cardioprotective, anxiolytic, antidepressant and memory-enhancing capabilities by using different in vitro, in vivo and in silico models. The in vitro experimentation revealed that CLAE consumed an ample amount of total phenolics (67.70 ± 0.15 µg GAE/mg) and flavonoids (47.54 ± 0.45 µg QE/mg) with stronger antiradical effects through DPPH (IC50 = 16.66 ± 0.42 µg/mL), TAC (77.33 ± 0.41 µg AAE/mg) and TRP (79.11 ± 0.67 µg GAE/mg) assays. The extract also displayed suitable acetylcholinesterase (AChE) inhibitory (IC50 = 110.13 ± 1.71 µg/mL) activity through a modified Ellman's method. The toxicology examination presented no mortality or any signs of clinical toxicity in both single-dose and repeated-dose tests. In line with the cardioprotective study, the pretreatment of CLAE was found to be effective in relieving the isoproterenol (ISO)-induced myocardial injury in rats by normalizing the heart weight index, serum cardiac biomarkers, lipid profile and various histopathological variations. In the noise-stress-induced model for behavior attributes, the results demonstrated that CLAE has the tendency to increase the time spent in the central zone and elevated open arms in the open field and elevated plus maze tests (examined for anxiety assessment), reduced periods of immobility in the forced swimming test (for depression) and improved recognition and working memory in the novel object recognition and Morris water maze tests, respectively. Moreover, the LC-ESI-MS/MS profiling predicted 53 phytocompounds in CLAE. The drug-likeness and ADMET analysis exhibited that the majority of the identified compounds have reasonable physicochemical and pharmacokinetic profiles. The co-expression of molecular docking and network analysis indicated that top-ranked CLAE phytoconstituents act efficiently against the key proteins and target multiple signaling pathways to exert its cardiovascular-protectant, anxiolytic, antidepressant and memory-enhancing activity. Hence, this artifact illustrates that the observed biological properties of CLAE elucidate its significance as a sustainable source of bioactive phytochemicals, which appears to be advantageous for pursuing further studies for the development of new therapeutic agents of desired interest.

Few-shot learning for medical text: A review of advances, trends, and opportunities.

BACKGROUND: Few-shot learning (FSL) is a class of machine learning methods that require small numbers of labeled instances for training. With many medical topics having limited annotated text-based data in practical settings, FSL-based natural language processing (NLP) holds substantial promise. We aimed to conduct a review to explore the current state of FSL methods for medical NLP. METHODS: We searched for articles published between January 2016 and October 2022 using PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. We also searched the preprint servers (e.g., arXiv, medRxiv, and bioRxiv) via Google Scholar to identify the latest relevant methods. We included all articles that involved FSL and any form of medical text. We abstracted articles based on the data source, target task, training set size, primary method(s)/approach(es), and evaluation metric(s). RESULTS: Fifty-one articles met our inclusion criteria-all published after 2018, and most since 2020 (42/51; 82%). Concept extraction/named entity recognition was the most frequently addressed task (21/51; 41%), followed by text classification (16/51; 31%). Thirty-two (61%) articles reconstructed existing datasets to fit few-shot scenarios, and MIMIC-III was the most frequently used dataset (10/51; 20%). 77% of the articles attempted to incorporate prior knowledge to augment the small datasets available for training. Common methods included FSL with attention mechanisms (20/51; 39%), prototypical networks (11/51; 22%), meta-learning (7/51; 14%), and prompt-based learning methods, the latter being particularly popular since 2021. Benchmarking experiments demonstrated relative underperformance of FSL methods on biomedical NLP tasks. CONCLUSION: Despite the potential for FSL in biomedical NLP, progress has been limited. This may be attributed to the rarity of specialized data, lack of standardized evaluation criteria, and the underperformance of FSL methods on biomedical topics. The creation of publicly-available specialized datasets for biomedical FSL may aid method development by facilitating comparative analyses.