Search

Background: In recent years, many countries have actively implemented programs and strategies to promote physical education and sports. Despite these efforts, the increase in physical activity has been accompanied by a significant rise in muscle and tendon-ligament injuries, with Achilles tendon rupture being the most prevalent, accounting for 47 % of such injuries. This review aims to summarize all significant factors determining the predisposition of the Achilles tendon to rupture, to develop effective personalized prevention measures. Objective: To identify and evaluate the risk factors contributing to Achilles tendon rupture and to develop strategies for personalized prevention. Methods: This review utilized data from several databases, including Elsevier, Global Health, PubMed-NCBI, Embase, Medline, Scopus, ResearchGate, RSCI, Cochrane Library, Google Scholar, eLibrary.ru, and CyberLeninka. Both non-modifiable and modifiable risk factors for Achilles tendon injuries and ruptures were analyzed. Results: The analysis identified several non-modifiable risk factors, such as genetic predisposition, anatomical and functional features of the Achilles tendon, sex, and age. These factors should be considered when selecting sports activities and designing training programs. Modifiable risk factors included imbalanced nutrition, improper exercise regimens, and inadequate monitoring of Achilles tendon conditions in athletes. Early treatment of musculoskeletal injuries, Achilles tendon diseases, foot deformities, and metabolic disorders is crucial. Long-term drug use and its risk assessment were also highlighted as important considerations. Furthermore, recent clinical advancements in both conventional and surgical methods to treat Achilles tendon injuries were described. The efficacy of these therapies in enhancing functional outcomes in individuals with Achilles injuries was compared. Advancements in cell-based and scaffold-based therapies aimed at enhancing cell regeneration and repairing Achilles injuries were also discussed. Discussion: The combination of several established factors significantly increases the risk of Achilles tendon rupture. Addressing these factors through personalized prevention strategies can effectively reduce the incidence of these injuries. Proper nutrition, regular monitoring, timely treatment, and the correction of metabolic disorders are essential components of a comprehensive prevention plan. Conclusion: Early identification of Achilles tendon risk factors allows for the timely development of effective personalized prevention strategies. These measures can contribute significantly to public health preservation by reducing the incidence of Achilles tendon ruptures associated with physical activity and sports. Continued research and clinical advancements in treatment methods will further enhance the ability to prevent and manage Achilles tendon injuries. The translational potential of this article: This study identifies key modifiable and non-modifiable risk factors for Achilles tendon injuries, paving the way for personalized prevention strategies. Emphasizing nutrition, exercise, and early treatment of musculoskeletal issues, along with advancements in cell-based therapies, offers promising avenues for improving recovery and outcomes. These findings can guide clinical practices in prevention and rehabilitation, ultimately reducing Achilles injuries and enhancing public health.

Purpose: We aimed to evaluate the time and cost of developing prompts using large language model (LLM), tailored to extract clinical factors in breast cancer patients and their accuracy. Materials and Methods: We collected data from reports of surgical pathology and ultrasound from breast cancer patients who underwent radiotherapy from 2020 to 2022. We extracted the information using the Generative Pre-trained Transformer (GPT) for Sheets and Docs extension plugin and termed this the "LLM" method. The time and cost of developing the prompts with LLM methods were assessed and compared with those spent on collecting information with "full manual" and "LLM-assisted manual" methods. To assess accuracy, 340 patients were randomly selected, and the extracted information by LLM method were compared with those collected by "full manual" method. Results: Data from 2,931 patients were collected. We developed 12 prompts for Extract function and 12 for Format function to extract and standardize the information. The overall accuracy was 87.7%. For lymphovascular invasion, it was 98.2%. Developing and processing the prompts took 3.5 hours and 15 minutes, respectively. Utilizing the ChatGPT application programming interface cost US $65.8 and when factoring in the estimated wage, the total cost was US $95.4. In an estimated comparison, "LLM-assisted manual" and "LLM" methods were time- and cost-efficient compared to the "full manual" method. Conclusion: Developing and facilitating prompts for LLM to derive clinical factors was efficient to extract crucial information from huge medical records. This study demonstrated the potential of the application of natural language processing using LLM model in breast cancer patients. Prompts from the current study can be re-used for other research to collect clinical information. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is a recently emerged research domain in the field of information security domain with broad applications in medical images and meta-data handling in the cloud. The amount of data required to handle the healthcare sector has exponentially increased due to the increase in the population. Medical images and various reports such as discharge summaries and diagnosis reports are the most common data in the healthcare sector. The RDH schemes are widely explored to embed the medical reports in the medical image instead of sending them as separate files. The receiver can extract the clinical reports and recover the original medical image for further diagnosis. This manuscript proposes an approach that uses a new lossless compression-based RDH scheme that creates vacant room for data hiding. The proposed scheme uses run-length encoding and a modified Elias gamma encoding scheme on higher-order bit planes for lossless compression. The conventional Elias gamma encoding process is modified in the proposed method to embed some additional data bits during the encoding process itself. The revised approach ensures a high embedding rate and lossless recovery of medical images at the receiver side. The experimental study is conducted on both natural images and medical images. The average embedding rate from the proposed scheme for the medical images is 0.75 bits per pixel. The scheme achieved a 0 bit error rate during image recovery and data extraction. The experimental study shows that the newly introduced scheme performs better when compared with the existing RDH schemes. [ABSTRACT FROM AUTHOR]

Cerebral palsy (CP) is the most common form of childhood disability, and spasticity is the most common motor-manifestation. Thankfully, there has been a significant reduction in the percentage of children born with CP in recent years, but nonetheless, those afflicted children can face life long devastating disabilities. Spinal cord stimulation (SCS), an implanted device used to treat chronic neuropathic pain, has been used to treat children with spasticity. In some cases, it has been shown to produce a remarkable improvement but despite this, the technique is not commonly used. There are a number of case series, and retrospective reports of outcomes with the most successful demonstrating a nearly 1.5-point reduction in Ashworth scale, the least successful demonstrating no significant improvements. Here, the authors examine the clinical reports and propose a mechanism of action based on the current understanding of SCS. Technology development in SCS for pain management has progressed significantly since the early clinical experience with CP and some of the new technology may be able to better exploit the putative mechanism. The advent of compound action potential recording and closed-loop control could lead to new insights into the electrophysiology and how to better tune these devices to provide more substantial relief from symptoms.

Background: With the accelerated development of next‐generation sequencing (NGS), identified variants, and targeted therapies, clinicians who confront the complicated and multifarious genetic information may not effectively incorporate NGS‐based circulating tumor DNA (ctDNA) analysis into routine patient care. Consequently, standardized ctDNA testing reports are of vital importance. In an effort to guarantee high‐quality reporting performance, we conducted an investigation of the current detection and reporting practices for NGS‐based ctDNA analysis. Materials and Methods: A set of simulated ctDNA samples with known variants at known allelic frequencies and a corresponding case scenario were distributed to 66 genetic testing laboratories for ctDNA analysis. Written reports were collected to evaluate the detection accuracy, reporting integrity, and information sufficiency using 21 predefined criteria. Results: Current reporting practices for NGS‐based ctDNA analysis were found to be far from satisfactory, especially regarding testing interpretation and methodological details. Only 42.4% of laboratories reported the results in complete concordance with the expected results. Moreover, 74.2% of reports only listed aberrations with direct and well‐known treatment consequences for the tumor type in question. Genetic aberrations for which experimental agents and/or drug access programs are available may thus be overlooked. Furthermore, methodological details for the interpretation of results were missing from the majority of reports (87.9%). Conclusion: This proof‐of‐principle study suggests that the capacity for accurate identification of variants, rational interpretation of genotypes, comprehensive recommendation of potential medications, and detailed description of methodologies need to be further improved before ctDNA analysis can be formally implemented in the clinic. Implications for Practice: Accurate, comprehensive, and standardized clinical sequencing reports can help to translate complex genetic information into patient‐centered clinical decisions, thereby shepherding precision oncology into daily practice. However, standards, guidelines, and quality requirements for clinical reports of next‐generation sequencing (NGS)‐based circulating tumor DNA (ctDNA) analysis are currently absent. By using a set of simulated clinical ctDNA samples and a corresponding case scenario, current practices were evaluated to identify deficiencies in clinical sequencing reports of ctDNA analysis. The recommendations provided here may serve as a roadmap for the improved implementation of NGS‐based ctDNA analysis in the clinic. Understanding the whole picture of tumor variations will allow for customized treatment decisions. Circulating tumor DNA (ctDNA) is emerging as a valuable tool to guide targeted therapy. This study analyzed clinical reports of next‐generation sequencing‐based ctDNA analysis for detection accuracy and reporting capacity. [ABSTRACT FROM AUTHOR]

The rapid growth in the extraction of clinical events from unstructured clinical records has raised considerable challenges. In this paper, we propose the use of different features with a statical modeling method called conditional random fields, which is consider an algorithm for effectively solving problems of sequence tagging. Our goal is to determine which feature selection can affect the performance of four subtasks presented in SemEval Task-12: Clinical TempEval 2016. We applied a careful preprocessing, where the proposed method was tested on real clinical records from Task-12: Clinical TempEval 2016. The comparative analyses obtained indicate that our proposal achieves good results compared to the work presented in Task-12: Clinical TempEval 2016 challenges. [ABSTRACT FROM AUTHOR]

Recently, the extraction of clinical events from unstructured medical texts has attracted much attention of the research community. Machine learning approaches are popular for this task, due to their ability to solve the problem of sequence tagging effectively. It has been suggested previously that simple features, such as word unigrams, part-of-speech tags, chunk tags, among others, are sufficient for this task. We show that more careful preprocessing and feature selection can significantly improve the results. We used conditional random field classifier with more linguistically oriented features and outperformed the current state-of-the-art approaches. We also show that the popular and much simpler Viterbi algorithm (hidden Markov model-based classification algorithm) can produce competitive results, when its parameters are tuned using specific optimization techniques. We evaluate these algorithms for the task of extraction of medical events from the corpus developed for SemEval shared Task 12: Clinical TempEval (Temporal Evaluation) 2016, namely, for its two subtasks: (i) event detection and (ii) event classification based on contextual modality. [ABSTRACT FROM AUTHOR]

Timely detection and treatment of microaneurysms is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting microaneurysms in fundus images is a highly challenging task due to the low image contrast, misleading cues of other red lesions, and the large variation of imaging conditions. Existing methods tend to fail in face of the large intra-class variation and small inter-class variations for microaneurysm detection in fundus images. Recently, hybrid text/image mining computer-aided diagnosis systems have emerged to offer a promise of bridging the semantic gap between images and diagnostic information. In this paper, we focus on developing an interleaved deep mining technique to cope intelligently with the unbalanced microaneurysm detection problem. Specifically, we present a clinical report guided multi-sieving convolutional neural network, which leverages a small amount of supervised information in clinical reports to identify the potential microaneurysm regions via the image-to-text mapping in the feature space. These potential microaneurysm regions are then interleaved with fundus image information for multi-sieving deep mining in a highly unbalanced classification problem. Critically, the clinical reports are employed to bridge the semantic gap between low-level image features and high-level diagnostic information. We build an efficient microaneurysm detection framework based on the hybrid text/image interleaving and validate its performance on challenging clinical data sets acquired from diabetic retinopathy patients. Extensive evaluations are carried out in terms of fundus detection and classification. Experimental results show that our framework achieves 99.7% precision and 87.8% recall, comparing favorably with the state-of-the-art algorithms. Integration of expert domain knowledge and image information demonstrates the feasibility of reducing the difficulty of training classifiers under extremely unbalanced data distributions. [ABSTRACT FROM PUBLISHER]

Background and objective Probabilistic topic models provide an unsupervised method for analyzing unstructured text. These models discover semantically coherent combinations of words (topics) that could be integrated in a clinical automatic summarization system for primary care physicians performing chart review. However, the human interpretability of topics discovered from clinical reports is unknown. Our objective is to assess the coherence of topics and their ability to represent the contents of clinical reports from a primary care physician's point of view. Methods Three latent Dirichlet allocation models (50 topics, 100 topics, and 150 topics) were fit to a large collection of clinical reports. Topics were manually evaluated by primary care physicians and graduate students. Wilcoxon Signed-Rank Tests for Paired Samples were used to evaluate differences between different topic models, while differences in performance between students and primary care physicians (PCPs) were tested using Mann–Whitney U tests for each of the tasks. Results While the 150-topic model produced the best log likelihood, participants were most accurate at identifying words that did not belong in topics learned by the 100-topic model, suggesting that 100 topics provides better relative granularity of discovered semantic themes for the data set used in this study. Models were comparable in their ability to represent the contents of documents. Primary care physicians significantly outperformed students in both tasks. Conclusion This work establishes a baseline of interpretability for topic models trained with clinical reports, and provides insights on the appropriateness of using topic models for informatics applications. Our results indicate that PCPs find discovered topics more coherent and representative of clinical reports relative to students, warranting further research into their use for automatic summarization. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of mortality amongst world’s population, in which prostate cancer is one of the most encountered malignancies among men. Globally, it is the sixth leading cause of cancer-related death in men. Prostate cancer is more prevalent in the developed world and is increasing at alarming rates in the developing countries. Prostate cancer is mostly a very sluggish progressing disease, caused by the overproduction of steroidal hormones like dihydrotestosterone or due to over-expression of enzymes such as 5-α-reductase. Various studies have revealed that growth factors play a crucial role in the progression of prostate cancer as they act either by directly elevating the level of steroidal hormones or upregulating enzyme efficacy by the active feedback mechanism. Presently, treatment options for prostate cancer include radiotherapy, surgery and chemotherapy. If treatment is done with prevailing traditional chemotherapy; it leads to resistance and development of androgen-independent prostate cancer that further complicates the situation with no cure option left. The current review article is an attempt to cover and establish an understanding of some major signalling pathways intervened through survival factors (IGF-1R), growth factors (TGF-α, EGF), Wnt, Hedgehog, interleukin, cytokinins and death factor receptor which are frequently dysregulated in prostate cancer. This will enable the researchers to design and develop better therapeutic strategies targeting growth factors and their cross talks mediated prostate cancer cell signalling. [ABSTRACT FROM AUTHOR]

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising. [ABSTRACT FROM AUTHOR]

Abstract: Motivation: Expressions that refer to a real-world entity already mentioned in a narrative are often considered anaphoric. For example, in the sentence “The pain comes and goes,” the expression “the pain” is probably referring to a previous mention of pain. Interpretation of meaning involves resolving the anaphoric reference: deciding which expression in the text is the correct antecedent of the referring expression, also called an anaphor. We annotated a set of 180 clinical reports (surgical pathology, radiology, discharge summaries, and emergency department) from two institutions to indicate all anaphor–antecedent pairs. Objective: The objective of this study is to describe the characteristics of the corpus in terms of the frequency of anaphoric relations, the syntactic and semantic nature of the members of the pairs, and the types of anaphoric relations that occur. Understanding how anaphoric reference is exhibited in clinical reports is critical to developing reference resolution algorithms and to identifying peculiarities of clinical text that may alter the features and methodologies that will be successful for automated anaphora resolution. Results: We found that anaphoric reference is prevalent in all types of clinical reports, that annotations of noun phrases, semantic type, and section headings may be especially important for automated resolution of anaphoric reference, and that separate modules for reference resolution may be required for different report types, different institutions, and different types of anaphors. Accurate resolution will probably require extensive domain knowledge—especially for pathology and radiology reports with more part/whole and set/subset relations. Conclusion: We hope researchers will leverage the annotations in this corpus to develop automated algorithms and will add to the annotations to generate a more extensive corpus. [Copyright &y& Elsevier]

An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction [ABSTRACT FROM AUTHOR]

Recent renewed interest in de-identification (also known as “anonymisation”) has led to the development of a series of systems in the United States with very good performance on challenge test sets. De-identification needs however to be tuned to the local documents and their specificities. We address here two issues raised in this context. First, tuning is generally performed by language engineers who should not have to work on identified text. We therefore perform a first gross de-identification step in the hospital. Second, to set up a de-identification system for new documents in a language different from English, here French patient reports, we tested two methods: the first attempts to adapt an existing US de-identifier for English, the second re-develops a new system which applies the same methods. The first method involved localizing patterns designed for English, which proved cumbersome and did not quickly obtain good performance. With a similar effort, the latter method obtained much better results. Evaluated on a set of 23 randomly selected texts from a corpus of 21,749 clinical texts, it obtained 83% recall and 92% precision. [ABSTRACT FROM AUTHOR]

Background Children and adolescents with autistic spectrum disorder (ASD) presenting with significant limitations in conventional forms of verbal and non-verbal communication are found to respond positively to music therapy intervention involving both active, improvizational methods and receptive music therapy approaches. Improvizational musical activity with therapeutic objectives and outcomes has been found to facilitate motivation, communication skills and social interaction, as well as sustaining and developing attention. The structure and predictability found in music assist in reciprocal interaction, from which tolerance, flexibility and social engagement to build relationships emerge, relying on a systematic approach to promote appropriate and meaningful interpersonal responses. Results Published reports of the value and effectiveness of music therapy as an intervention for children with ASD range from controlled studies to clinical case reports. Further documentation has emphasized the role music therapy plays in diagnostic and clinical assessment. Music therapy assessment can identify limitations and weaknesses in children, as well as strengths and potentials. Research evidence from a systematic review found two randomized controlled trials that examined short-term effects of structured music therapy intervention. Significant effects were found in these studies even with extremely small samples, and the findings are important because they demonstrate the potential of the medium of music for autistic children. Case series studies were identified that examined the effects of improvizational music therapy where communicative behaviour, language development, emotional responsiveness, attention span and behavioural control improved over the course of an intervention of improvizational music therapy. [ABSTRACT FROM AUTHOR]

Spina bifida, anencephaly, and encephalocele are commonly grouped together and termed neural tube defects (NTD). Failure of closure of the neural tube during development results in anencephaly or spina bifida aperta but encephaloceles are possibly post-closure defects. NTD are associated with a number of other central nervous system (CNS) and non-neural malformations. Racial, geographic and seasonal variations seem to affect their incidence. Etiology of NTD is unknown. Most of the non-syndromic NTD are of multifactorial origin. Recent in vitro and in vivo studies have highlighted the molecular mechanisms of neurulation in vertebrates but the morphologic development of human neural tube is poorly understood. A multisite closure theory, extrapolated directly from mouse experiments highlighted the clinical relevance of closure mechanisms to human NTD. Animal models, such as circle tail, curly tail, loop tail, shrm and numerous knockouts provide some insight into the mechanisms of NTD. Also available in the literature are a plethora of chemically induced preclosure and a few post-closure models of NTD, which highlight the fact that CNS malformations are of hetergeneitic nature. No Mendelian pattern of inheritance has been reported. Association with single gene defects, enhanced recurrence risk among siblings, and a higher frequency in twins than in singletons indicate the presence of a strong genetic contribution to the etiology of NTD. Non-availability of families with a significant number of NTD cases makes research into genetic causation of NTD difficult. Case reports and epidemiologic studies have implicated a number of chemicals, widely differing therapeutic drugs, environmental contaminants, pollutants, infectious agents, and solvents. Maternal hyperthermia, use of valproate by epileptic women during pregnancy, deficiency and excess of certain nutrients and chronic maternal diseases (e.g. diabetes mellitus) are reported to cause a manifold increase in the incidence of NTD. A host of suspected teratogens are also available in the literature. The UK and Hungarian studies showed that periconceptional supplementation of women with folate (FA) reduces significantly both the first occurrence and recurrence of NTD in the offspring. This led to mandatory periconceptional FA supplementation in a number of countries. Encouraged by the results of clinical studies, numerous laboratory investigations focused on the genes involved in the FA, vitamin B12 and homocysteine metabolism during neural tube development. As of today no clinical or experimental study has provided unequivocal evidence for a definitive role for any of these genes in the causation of NTD suggesting that a multitude of genes, growth factors and receptors interact in controlling neural tube development by yet unknown mechanisms. Future studies must address issues of gene-gene, gene-nutrient and gene–environment interactions in the pathogenesis of NTD. [ABSTRACT FROM AUTHOR]

We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate performance on a corpus of 374,899 pathology reports obtained from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. Each pathology report is associated with five clinical classification tasks - site, laterality, behavior, histology, and grade. We compare the performance of the HiSAN against other machine learning and deep learning approaches commonly used on medical text data - Naive Bayes, logistic regression, convolutional neural networks, and hierarchical attention networks (the previous state-of-the-art). We show that HiSANs are superior to other machine learning and deep learning text classifiers in both accuracy and macro F-score across all five classification tasks. Compared to the previous state-of-the-art, hierarchical attention networks, HiSANs not only are an order of magnitude faster to train, but also achieve about 1% better relative accuracy and 5% better relative macro F-score. [ABSTRACT FROM AUTHOR]

Background: The amount of incoming data into physicians' offices is increasing, thereby making it difficult to process information efficiently and accurately to maximize positive patient outcomes. Current manual processes of screening for individual terms within long free-text documents are tedious and error-prone. This paper explores the use of statistical methods and computer systems to assist clinical data management. Objective: The objective of this study was to verify and validate the use of a naive Bayesian classifier as a means of properly prioritizing important clinical data, specifically that of free-text radiology reports. Methods: There were one hundred reports that were first used to train the algorithm based on physicians' categorization of clinical reports as high-priority or low-priority. Then, the algorithm was used to evaluate 354 reports. Additional beautification procedures such as section extraction, text preprocessing, and negation detection were performed. Results: The algorithm evaluated the 354 reports with discrimination between high-priority and low-priority reports, resulting in a bimodal probability distribution. In all scenarios tested, the false negative rates were below 1.1% and the recall rates ranged from 95.65% to 98.91%. In the case of 50% prior probability and 80% threshold probability, the accuracy of this Bayesian classifier was 93.50%, with a positive predictive value (precision) of 80.54%. It also showed a sensitivity (recall) of 98.91% and a F-measure of 88.78%. Conclusions: The results showed that the algorithm could be trained to detect abnormal radiology results by accurately screening clinical reports. Such a technique can potentially be used to enable automatic flagging of critical results. In addition to accuracy, the algorithm was able to minimize false negatives, which is important for clinical applications. We conclude that a Bayesian statistical classifier, by flagging reports with abnormal findings, can assist a physician in reviewing radiology reports more efficiently. This higher level of prioritization allows physicians to address important radiologic findings in a timelier manner and may also aid in minimizing errors of omission. [ABSTRACT FROM AUTHOR]