Manual Extraction Research Articles

End-to-end deep learning on classification of neutral-to-ground voltage in secondary distribution systems

Neutral-to-ground voltage (NTGV) abnormalities in secondary distribution systems (SDS) pose significant power quality (PQ) challenges, including safety hazards, power losses, and equipment damage. Despite their importance, these abnormalities remain relatively understudied. Accurate classification of NTGV events is crucial for effective mitigation strategies. Existing research primarily relies on machine learning (ML) models trained on manually extracted features from simulated or real-world signals. This paper introduces a novel end-to-end deep learning approach that leverages Gate Recurrent Units (GRU) to bypass manual feature extraction, directly utilizing real-world signals from three NTGV event categories: ground fault, lightning strike, and normal conditions. This is first time that GRU has been used for NTGV classification using raw data. The model's generalizability is assessed through 5-fold cross-validation. A comparative analysis with baseline models and traditional ML techniques demonstrates the proposed model's superior performance and computational efficiency due to its ability to directly process raw data.

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.

A geospatial approach to delineate lineaments on the granite gneissic terrain of Mpwapwa district, Dodoma, central Tanzania

The study sought to utilise Geographic Information Systems (GIS), Remote Sensing, and a blend of manual and automatic techniques to delineate and analyse the spatial distribution and orientation of lineaments within the terrain of Mpwapwa District. The study employed various techniques, including the Optimum Index Factor (OIF), Band Combination, Principal Component Analysis (PCA), and Directional Filtering on the Landsat 8 imagery, and Hill Shading technique was used on Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). These techniques were employed in the processing steps for both manual and automatic lineament extraction, utilizing various software tools. Lineament control and validation were executed using digitized geological maps, topographical maps, and Google Earth images.The results revealed lineaments with a total length of 2060.6 km, non-uniformly distributed and predominantly present in hard rock lithologies, such as migmatitic granite, migmatitic biotite gneiss, biotite gneiss, granitoid gneiss, and porphyroblastic gneiss. The findings further indicate that NE-SW and NW-SE are the dominant lineament trends. Additionally, the study revealed that areas with medium, high, and very high lineament density and intersection are primarily located in the southwest and northeast parts of the Mpwapwa District. Meanwhile, regions with low lineament density are spread through the entire district.In this study, GIS and remote sensing emerged as valuable tools for identifying and mapping lineaments within a challenging hydrogeological setting. Findings from this study will serve as a foundational framework for subsequent geological and hydrogeological investigations in the area of study.

A LiDAR-Camera Joint Calibration Algorithm Based on Deep Learning.

Multisensor (MS) data fusion is important for improving the stability of vehicle environmental perception systems. MS joint calibration is a prerequisite for the fusion of multimodality sensors. Traditional calibration methods based on calibration boards require the manual extraction of many features and manual registration, resulting in a cumbersome calibration process and significant errors. A joint calibration algorithm for a Light Laser Detection and Ranging (LiDAR) and camera is proposed based on deep learning without the need for other special calibration objects. A network model constructed based on deep learning can automatically capture object features in the environment and complete the calibration by matching and calculating object features. A mathematical model was constructed for joint LiDAR-camera calibration, and the process of sensor joint calibration was analyzed in detail. By constructing a deep-learning-based network model to determine the parameters of the rotation matrix and translation matrix, the relative spatial positions of the two sensors were determined to complete the joint calibration. The network model consists of three parts: a feature extraction module, a feature-matching module, and a feature aggregation module. The feature extraction module extracts the image features of color and depth images, the feature-matching module calculates the correlation between the two, and the feature aggregation module determines the calibration matrix parameters. The proposed algorithm was validated and tested on the KITTI-odometry dataset and compared with other advanced algorithms. The experimental results show that the average translation error of the calibration algorithm is 0.26 cm, and the average rotation error is 0.02°. The calibration error is lower than those of other advanced algorithms.

Enhancing clinical data retrieval with Smart Watchers: a NiFi-based ETL pipeline for Elasticsearch queries

BackgroundThe aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate near real-time alerts for specific clinical scenarios, with an additional goal to improve clinical decision-making through accuracy and reliability.MethodsThe automated clinical alert system, named Smart Watchers, was developed using Apache NiFi and Python scripts to create flexible data processing pipelines and customisable clinical alerts. A comparative analysis between Smart Watchers and the legacy Elastic Watchers was conducted to evaluate performance metrics such as accuracy, reliability, and scalability. The evaluation involved measuring the time taken for manual data extraction through the electronic patient record (EPR) front-end and comparing it with the automated data extraction process using Smart Watchers.ResultsDeployment of Smart Watchers showcased a consistent time savings between 90% to 98.67% compared to manual data extraction through the EPR front-end. The results demonstrate the efficiency of Smart Watchers in automating data extraction and alert generation, significantly reducing the time required for these tasks when compared to manual methods in a scalable manner.ConclusionsThe research underscores the utility of employing an automated clinical alert system, and its portability facilitated its use across multiple clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.

Algorithm-Driven Extraction of Point Cloud Data Representing Bottom Flanges of Beams in a Complex Steel Frame Structure for Deformation Measurement

Laser scanning has become a popular technology for monitoring structural deformation due to its ability to rapidly obtain 3D point clouds that provide detailed information about structures. In this study, the deformation of a complex steel frame structure is estimated by comparing the associated point clouds captured at two epochs. To measure its deformations, it is essential to extract the bottom flanges of the steel beams in the captured point clouds. However, manual extraction of numerous bottom flanges is laborious and the separation of beam bottom flanges and webs is especially challenging. This study presents an algorithm-driven approach for extracting all beams’ bottom flanges of a complex steel frame. RANdom SAmple Consensus (RANSAC), Euclidean clustering, and an originally defined point feature is sequentially used to extract the beam bottom flanges. The beam bottom flanges extracted by the proposed method are used to estimate the deformation of the steel frame structure before and after the removal of temporary supports to beams. Compared to manual extraction, the proposed method achieved an accuracy of 0.89 in extracting the beam bottom flanges while saving hours of time. The maximum observed deformation of the steel beams is 100 mm at a location where the temporal support was unloaded. The proposed method significantly improves the efficiency of the deformation measurement of steel frame structures using laser scanning.

Correlating Histopathological Microscopic Images of Creutzfeldt–Jakob Disease with Clinical Typology Using Graph Theory and Artificial Intelligence

Creutzfeldt–Jakob disease (CJD) is a rare, degenerative, and fatal brain disorder caused by abnormal proteins called prions. This research introduces a novel approach combining AI and graph theory to analyze histopathological microscopic images of brain tissues affected by CJD. The detection and quantification of spongiosis, characterized by the presence of vacuoles in the brain tissue, plays a crucial role in aiding the accurate diagnosis of CJD. The proposed methodology employs image processing techniques to identify these pathological features in high-resolution medical images. By developing an automatic pipeline for the detection of spongiosis, we aim to overcome some limitations of manual feature extraction. The results demonstrate that our method correctly identifies and characterize spongiosis and allows the extraction of features that will help to better understand the spongiosis patterns in different CJD patients.

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD.

Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. Inter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. LLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.

Research on Tool Wear Monitoring Based on Enhanced Convolutional Neural Networks

Abstract Identifying the wear status of cutting tools during the machining process is essential because failure to promptly replace severely worn tools can significantly impact the quality of workpiece machining. Presently, machine learning methods are predominantly utilized for monitoring cutting tool wear status. However, these methods rely on manual feature extraction and exhibit low accuracy. This study introduces a novel RRP-Net model, built upon the RepVgg and ResNet frameworks, integrating a parameter-free self-attention mechanism called SimAM to expedite the model’s solving speed without increasing parameters. Within the foundational module of the model, a structural reparameterization approach is employed to transform the multi-branch structure during training into a single-branch structure during validation. This method not only enhances model accuracy but also accelerates the model validation process. The publicly available cutting data from PHM2010 is employed for model training and validation. The findings demonstrate that RRP-Net surpasses classical convolutional neural network models in identifying cutting tool wear status within the PHM2010 dataset, achieving an average accuracy of 98.65% and enhancing recognition accuracy on relevant datasets by 2.41%. To verify the model’s practical applicability, specificity and recall during the Break stage are computed at 99.73% and 98.18%, respectively, affirming the model’s exceptional robustness and stability. The heightened accuracy and efficiency of RRP-Net further broaden its applicability within the industrial domain.

Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning

The precise identification of faults is an essential component in the effective operation and maintenance of rotating machinery (RM). The increasing adoption of deep learning in this field is likely due to the vast amounts of automatically generated monitoring data and its robust ability to learn and detect errors effectively. Deep learning techniques generally demonstrate high efficacy, particularly when limited fault data is available or when employing a two-stage diagnostic approach, involving manual feature extraction followed by fault classification. To address these challenges, we introduce the Expansive Dual-Attention Fusion Transformer (EDAF-Transformer), which is trained through a semi-supervised approach using minimal labeled data. Our method begins with a detailed explanation of the foundational theories behind transformers and semi-supervised learning strategies. This establishes the basis of our approach to intelligent fault diagnosis, particularly under the constraints of raw vibration signals and limited labeled data availability. We then present our novel model, the EDAF-Transformer , which comprises two main components: the Expansive Dual-Attention Enhanced Modules and the 1-D Twin Global Self-Attention Transformer Encoder Module. The first component employs an Expansive Dual-branch Attention Enhanced Module (EDAEM) within a dual-branch attention architecture. This design effectively enlarges the receptive field, capturing both global and local features. The second component, the Twin Sparse Self-Attention Fusion Module (TSSAFM), integrates Global Sparse Self-Attention with Squeeze-and-Excitation (SE) attention. This module is designed to enhance feature encoding capabilities while simultaneously reducing computational demands. Furthermore, we implement an uncertainty self-training semi-supervised strategy focused on balanced unlabeled sample selection. This approach significantly enhances the generalizability and performance of the EDAF-Transformer . We extensively tested our semi-supervised fault diagnosis method on publicly available motor bearing data and a specially curated transmission shaft dataset. The results from these tests demonstrate that our method surpasses other intelligent fault diagnosis methods in effectiveness.

Outcome of Phacoemulsification Cataract Surgery in Complicated Cataract Due to Uveitis

Background: Surgical removal of cataractous lens is the only of treatment of cataract irrespective of etiology. Phacoemulsification is a modern cataract surgery in which the eyes internal lens is emulsified with an ultrasonic hand-piece and aspirated from the eye. Complicated cataract due to anterior uveitis has difference in morphology and outcome from that of age-related cataract variety. Aim of the study: The purpose of this study was to evaluate the outcome of phacoemulsification in complicated cataract due to uveitis. Methods: This prospective observational cross-sectional study conducted in cataract clinic of National Institute of Ophthalmology & Hospital, Dhaka, Bangladesh, from September 2018 to March 2019. Sample was selected by purposive sampling technique. Total 50 patients suffering from complicated cataract due to uveitis were included according to selection criteria. Data were gathered from the informant and recorded in a structured case report format. Clinical examination and relevant investigation were done meticulously. All acquired data was entered into a Microsoft Excel Work Sheet and analyzed using descriptive statistics in SPSS 24.0. Results: In this study, the maximum number of patients 18(36.0%) were 51-60 years age group, mean age of the patient was 52.7±6.37 years. Male to female ratio was 1:1.17. Postoperative best corrected visual acuity (BCVA) was better progressively and reached significance difference (P<0.05). Astigmatism was measured in different follow up time. There was significant difference (p< 0.05) present in all post-operative days. In time wise comparison revealed, there was also significant difference in between 1st postoperative day and 30th postoperative day but no significance was present between 30th postoperative day and 180th postoperative day (P= 0.161). In this study postoperative complications included posterior capsule opacification (55.5%), posterior synechiae (11.1%), cystoid macular edema (22.2%) & glaucoma (11.1%). Conclusion: Present study concluded that phacoemulsification is safer for the uveitic cataract as less inflammation is induced than that by a manual extracapsular cataract extraction. In order to optimize the postoperative outcome of uveitic cataract surgery, it is necessary to be meticulous and thorough.

A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning

As connected and autonomous vehicles proliferate, the Controller Area Network (CAN) bus has become the predominant communication standard for in-vehicle networks due to its speed and efficiency. However, the CAN bus lacks basic security measures such as authentication and encryption, making it highly vulnerable to cyberattacks. To ensure in-vehicle security, intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks while remaining lightweight for practical deployment. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. These approaches overlook other exploitable features, making it challenging to adapt to new unseen attack variants and compromising security. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations. The proposed IDS employs a multi-stage approach: an artificial neural network (ANN) in the first stage to detect seen attacks, and a Long Short-Term Memory (LSTM) autoencoder in the second stage to detect new, unseen attacks. To understand and analyze diverse driving behaviors, update the model with the latest attack patterns, and preserve data privacy, we propose a theoretical framework to deploy our IDS in a hierarchical federated learning (H-FL) environment. Experimental results demonstrate that our IDS achieves an F1-score exceeding 0.99 for seen attacks and exceeding 0.95 for novel attacks, with a detection rate of 99.99%. Additionally, the false alarm rate (FAR) is exceptionally low at 0.016%, minimizing false alarms. Despite using DL algorithms known for their effectiveness in identifying sophisticated and zero-day attacks, the IDS remains lightweight, ensuring its feasibility for real-world deployment. This makes our model robust against seen and unseen attacks.

Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach.

Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network's security and integrity. This paper proposes a novel method-a Composite Ensemble Learning (CEL)-based neural network-for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

State of the art of the cow-calf systems in beef and dairy cattle (Bos taurus) operations in EU, USA, and Brazil from 1998 to 2023

Cow-calf systems represent a significant research area in animal husbandry, with differences depending on the final product (meat or milk). This study aimed to apply text mining and topic analysis on literature describing cow-calf systems in European, American, and Brazilian beef and dairy sectors between 1998 and 2023. Additionally, cow-calf contact (CCC) literature data was manually extracted. Our findings revealed the presence of 11 research areas among literature on cow-calf systems, with different priorities identified in the beef and dairy sectors. Beef industry mainly focused on animal proficiency and nutrition, while dairy on animal welfare and CCC, which showed a growing trend as emerging research topic, mostly in the EU. Current debates around calf welfare and EU's planned animal welfare legislation revision appeared to be driving the increasing interest in this topic. Studies in the beef sector were mainly localized in Brazil, showing that research in different contexts and species is important for CCC implementation. Manual data extraction showed considerable variation in the retained CCC documents regarding sample size, type of contact, methods and CCC duration. Learning about the varied CCC approaches used in beef and dairy farms in different locations, concentrating on their strengths and weaknesses, will help to develop novel solutions to global challenges. Adopting validated and robust indicators would help scientists and policymakers to monitor the system's quality. To improve CCC feasibility, match consumer demands, and move towards One Welfare and One Health, future research should focus on a variety of situations to overcome the current shortcomings.

ESFD-YOLOv8n: Early Smoke and Fire Detection Method Based on an Improved YOLOv8n Model

Ensuring fire safety is essential to protect life and property, but modern infrastructure and complex settings require advanced fire detection methods. Traditional object detection systems, often reliant on manual feature extraction, may fall short, and while deep learning approaches are powerful, they can be computationally intensive, especially for real-time applications. This paper proposes a novel smoke and fire detection method based on the YOLOv8n model with several key architectural modifications. The standard Complete-IoU (CIoU) box loss function is replaced with the more robust Wise-IoU version 3 (WIoUv3), enhancing predictions through its attention mechanism and dynamic focusing. The model is streamlined by replacing the C2f module with a residual block, enabling targeted feature extraction, accelerating training and inference, and reducing overfitting. Integrating generalized efficient layer aggregation network (GELAN) blocks with C2f modules in the neck of the YOLOv8n model further enhances smoke and fire detection, optimizing gradient paths for efficient learning and high performance. Transfer learning is also applied to enhance robustness. Experiments confirmed the excellent performance of ESFD-YOLOv8n, outperforming the original YOLOv8n by 2%, 2.3%, and 2.7%, with a mean average precision (mAP@0.5) of 79.4%, precision of 80.1%, and recall of 72.7%. Despite its increased complexity, the model outperforms several state-of-the-art algorithms and meets the requirements for real-time fire and smoke detection.

Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques.

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina's response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classical ML models. Our findings demonstrated the superiority of utilizing the Visual Transformer architecture with a Hamming window function, showcasing its advantage in ERG signal classification. Also, as a result, we recommend the RF algorithm for scenarios necessitating manual feature extraction, particularly with the Boxcar (rectangular) or Bartlett window functions. By elucidating the optimal methodologies for feature extraction and classification, this study contributes to advancing the diagnostic capabilities of ERG analysis in clinical settings.

Thermal, chemical and mechanical characterization of Azadirachta indica tree gum

This study focuses on the comprehensive thermal, chemical, and mechanical characterization of neem gum, a natural, colorless, tasteless polysaccharide obtained from Azadirachta indica tree’s trunk. The collection process of gum involves manual extraction from crude neem tree exudates, followed by thorough cleaning and homogenization with water. A portion of the resulting gum is then filtered, allowed to thicken, and naturally dried for subsequent mechanical testing. Various analytical techniques, such as thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) to determine the thermal properties, Fourier transform infrared spectrometry (FTIR) to find the functional groups, scanning electron microscopy (SEM), and micro-X-ray fluorescence analysis (µ-XRF) to decide the elemental composition, microhardness, tensile, flexural, and compression tests to determine the mechanical properties, are employed in this study. The thermal and mechanical properties of neem gum have been relatively unexplored, and this highlights the novelty and importance of this research. The results are analyzed and compared with other natural tree gums. The pH of 5.07 shows that the gum is slightly acidic in nature, and the peaks obtained from thermal analysis demonstrate that it doesn’t have a melting point. The microhardness value, tensile strength, flexural strength, and compressive strength of neem gum are 238.64 MPa, 1.0155 MPa, 1.899 MPa, and 1.1023 MPa, respectively. This research provides valuable insights into the diverse characteristics and potential applications of neem gum in various fields. It is being used in the pharmaceutical industry. It can also be used as a cap for cutting tools, such as drill bits, after manufacturing, during transportation to protect their sharp edges from damage.

Hydrogel-coated and active clearance chest drains in cardiac surgery: real-world results of a single-center study

PurposeCardiac surgery patients require chest drains for postoperative fluid drainage. Innovations in this field include chemical drain coating and manual clot extraction systems, aiming to provide reduced clotting and improved patient comfort. This study compares outcomes using hydrogel-coated, active clearance and conventional chest drains.MethodsPatients with cardiac surgery at our institution from January 2023 to September 2023 were included. Drain allocation was based on surgeon’s choice, with either a combination of hydrogel-coated and conventional, active clearance and conventional, or conventional drains alone. Drain data and clinical outcomes were recorded prospectively.ResultsOne hundred seventy-eight patients (62.9 ± 11.7 years, 67.4% male) received a total of 512 chest drains intraoperatively. Hydrogel-coated and active clearance drains showed higher drainage volumes than conventional drains (p < 0.001, respectively). Patency was lowest in conventional drains (36.7% vs. 98.8% for hydrogel-coated, p < 0.001, and vs. 96.6% for active clearance drains, p < 0.001). Conventional drains showed 5.9 times the odds (95% CI 2.0–25.2) of large pleural effusions compared to hydrogel-coated and 12.0 times the odds (95% CI 1.9–504.1) compared to active clearance drains. Patients with hydrogel-coated drains had the shortest length of stay (p < 0.001).ConclusionHydrogel-coated and active clearance drains show improved outcomes compared to conventional drains in cardiac surgery.

Enhancing Clinical Data Extraction from Pathology Reports: A Comparative Analysis of Large Language Models.

This study evaluates the efficacy of a small large language model (sLLM) in extracting critical information from free-text pathology reports across multiple centers, addressing the challenges posed by the narrative and complex nature of these documents. Employing three variants of the Llama 2 model, with 7 billion, 13 billion, and 70 billion parameters, the research assesses model performance in both zero-shot and five-shot settings, offering insights into the impact of example-based learning. A specialized information extraction tool utilizing regular expressions for pattern identification serves as the benchmark for evaluating the models' accuracy. Conducted within a hospital's internal environment, the study emphasizes the clinical applicability of these findings. The results reveal significant variations in model performance, with the 70 billion parameter model achieving remarkable accuracy in the five-shot scenario, demonstrating the potential of sLLMs in enhancing the efficiency and accuracy of data extraction from pathology reports. The study highlights the importance of example-driven learning and the trade-offs between model size, accuracy, hallucination rates, and processing time. These findings contribute to the ongoing efforts to integrate advanced language models into clinical settings, potentially transforming patient care and biomedical research by mitigating the limitations of manual data extraction processes.