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Heliyon Jun 2024Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually....
Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually. However, this process relies on pathologists and can be a time-consuming task. Therefore, to improve the precision of clinical interpretation, researchers have developed computational algorithms that leverage next-generation sequencing technologies for automated mutation analysis. This paper utilized four deep learning classification models with training collections of biomedical texts. These models comprise bidirectional encoder representations from transformers for Biomedical text mining (BioBERT), a specialized language model implemented for biological contexts. Impressive results in multiple tasks, including text classification, language inference, and question answering, can be obtained by simply adding an extra layer to the BioBERT model. Moreover, bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) have been leveraged to produce very good results in categorizing genetic mutations based on textual evidence. The dataset used in the work was created by Memorial Sloan Kettering Cancer Center (MSKCC), which contains several mutations. Furthermore, this dataset poses a major classification challenge in the Kaggle research prediction competitions. In carrying out the work, three challenges were identified: enormous text length, biased representation of the data, and repeated data instances. Based on the commonly used evaluation metrics, the experimental results show that the BioBERT model outperforms other models with an F1 score of 0.87 and 0.850 MCC, which can be considered as improved performance compared to similar results in the literature that have an F1 score of 0.70 achieved with the BERT model.
PubMed: 38912449
DOI: 10.1016/j.heliyon.2024.e32279 -
Rehabilitation Oncology (American... Apr 2024Rehabilitation therapy is important to treat physical and functional impairments that may occur in individuals receiving physically taxing, yet potentially curative...
BACKGROUND
Rehabilitation therapy is important to treat physical and functional impairments that may occur in individuals receiving physically taxing, yet potentially curative hematopoietic stem cell transplants (HSCT). However, there is scarce data on how rehabilitation is delivered during HSCT in real-life setting. Our objective is to assess the rehabilitation practices for adult patients hospitalized for HSCT in the United States.
METHODS
A 48-question online survey with cancer centers with the top 10% HSCT volumes (per American registries). We obtained data on patient characteristics, rehabilitation therapy details (timing, indication, administering providers), physical function objective and subjective outcome measures, and therapy activity precautions.
RESULTS
Fourteen (out of 21) institutions were included. Rehabilitation therapy referrals occurred at admission for all patients at 35.7% of the centers for: functional decline (92.9%), fall risk (71.4%), and discharge planning (71.4%). Participating institutions had physical therapists (92.9%), occupational therapists (85.7%), speech language pathologists (64.3%) and therapy aides (35.7%) in their rehabilitation team. Approximately 71% of centers used objective functional measures including sit-to-stand tests (50.0%), balance measures (42.9%), and six-minute walk/gait speed (both 35.7%). Monitoring of blood counts to determine therapy modalities frequently occurred and therapies held for low platelet or hemoglobin values; but absolute neutrophil values were not a barrier to participate in resistance or aerobic therapies (42.9%).
DISCUSSION
Rehabilitation practices during HSCT varied among the largest volume cancer centers in the United States, but most centers provided skilled therapy, utilized objective, clinician and patient reported outcomes, and monitored blood counts for safety of therapy administration.
PubMed: 38912164
DOI: 10.1097/01.REO.0000000000000363 -
Cureus May 2024We present the case of a 52-year-old female with a giant phyllodes tumor (GPT), which was fungating through the skin that showed fleshy polypoid outgrowths. Histological...
We present the case of a 52-year-old female with a giant phyllodes tumor (GPT), which was fungating through the skin that showed fleshy polypoid outgrowths. Histological analysis revealed stromal atypia, mitotic activity, and stromal overgrowth; however, the tumor border was well-defined, and malignant heterologous elements were not observed. Therefore, as some but not all malignant histological characteristics were present, we diagnosed the patient with borderline GPT. In cases of phyllodes tumor (PT) with the unique gross findings of fungation through the skin as fleshy polypoid outgrowths, caution is required for the subsequent course because even if the PT is graded as benign histologically, a malignant process can occur. Pathologists should note that the sampling of the collection site and the ambiguity of the histological grading of PT may affect the final diagnosis of GPT. It is also important to perform surgery with adequate preservation of the resected margins to control recurrence for patients with GPT.
PubMed: 38910772
DOI: 10.7759/cureus.61020 -
Clinics in Dermatology Jun 2024Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific...
Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific focus on dermatopathology and neoplastic dermatopathology. AI, encompassing Machine Learning (ML) and Deep Learning (DL), has demonstrated its potential in tasks ranging from diagnostic applications on Whole Slide Imaging (WSI) to predictive and prognostic functions in skin pathology. In dermatopathology, studies have assessed AI's ability to identify skin lesions, classify melanomas, and improve diagnostic accuracy. Results indicate that AI, particularly Convolutional Neural Networks (CNNs), can outperform human pathologists in terms of sensitivity and specificity. Moreover, AI aids in predicting disease outcomes, identifying aggressive tumors, and differentiating between various skin conditions. Neoplastic dermatopathology showcases AI's prowess in classifying melanocytic lesions, discriminating between melanomas and nevi, and aiding dermatopathologists in making accurate diagnoses. Studies emphasize the reproducibility and diagnostic aid that AI provides, especially in challenging cases. In inflammatory and lymphoproliferative dermatopathology, limited research exists, but studies show attempts to use AI to differentiate conditions like Mycosis Fungoides and eczema. While some results are promising, further exploration is needed in these areas. We highlight the extraordinary interest AI has garnered in the scientific community and its potential to assist clinicians and pathologists. Despite the advancements, we have stress edthe importance of collaboration between medical professionals, computer scientists, bioinformaticians, and engineers to harness AI's benefits while acknowledging its limitations and risks. The integration of AI into dermatopathology holds great promise, positioning it as a valuable tool rather than as a replacement for human expertise.
PubMed: 38909860
DOI: 10.1016/j.clindermatol.2024.06.010 -
BMJ Open Jun 2024The underdevelopment of preterm infants can lead to delayed progression through key early milestones. Demonstration of safe oral feeding skills, constituting proper...
INTRODUCTION
The underdevelopment of preterm infants can lead to delayed progression through key early milestones. Demonstration of safe oral feeding skills, constituting proper suck-swallow reflex are requirements for discharge from the neonatal intensive care unit (NICU) to ensure adequate nutrition acquisition. Helping an infant develop these skills can be draining and emotional for both families and healthcare staff involved in the care of preterm infants with feeding difficulties. Currently, there are no systematic reviews evaluating both family and healthcare team perspectives on aspects of oral feeding. Thus, we first aim to evaluate the current knowledge surrounding the perceptions, experiences and needs of families with preterm babies in the context of oral feeding in the NICU. Second, we aim to evaluate the current knowledge surrounding the perceptions, experiences and needs of healthcare providers (physicians, advanced practice providers, nurses, dietitians, speech-language pathologists and occupational therapists) in the context of oral feeding in the NICU.
METHODS AND ANALYSIS
A literature search will be conducted in multiple electronic databases from their inception, including PubMed, CINHAL, Embase, the Cochrane Central Register for Controlled Trials and PsycINFO. No restrictions will be applied based on language or data of publication. Two authors will screen the titles and abstracts and then review the full text for the studies' inclusion in the review. The data will be extracted into a pilot-tested data collection sheet by three independent authors. To evaluate the quality, reliability and relevance of the included studies, the Critical Appraisal Skills Programme checklist will be used. The overall evidence will be assessed using the Grading of Recommendation Assessment, Development and Evaluation criteria. We will report the results of the systematic review by following the Enhancing Transparency in Reporting the synthesis of Qualitative research checklist.
ETHICS AND DISSEMINATION
Ethical approval of this project is not required as this is a systematic review using published and publicly available data and will not involve contact with human subjects. Findings will be published in a peer-reviewed journal.
PROSPERO REGISTRATION NUMBER
CRD42023479288.
Topics: Humans; Intensive Care Units, Neonatal; Infant, Newborn; Systematic Reviews as Topic; Infant, Premature; Qualitative Research; Health Personnel; Family; Research Design
PubMed: 38908851
DOI: 10.1136/bmjopen-2024-084884 -
Urology Jun 2024
PubMed: 38908559
DOI: 10.1016/j.urology.2024.06.022 -
Spectrochimica Acta. Part A, Molecular... Jun 2024Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and...
Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm (low wavenumber range, LWR) and 2700-3700 cm (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.
PubMed: 38908360
DOI: 10.1016/j.saa.2024.124683 -
Current Problems in Cardiology Jun 2024Primary graft dysfunction (PGD) is the leading cause of death in the first year after heart transplant (HT), but pathophysiology and histology are not completely... (Review)
Review
BACKGROUND
Primary graft dysfunction (PGD) is the leading cause of death in the first year after heart transplant (HT), but pathophysiology and histology are not completely understood. This study describes and compares morphological findings of hearts of patients with and without PGD.
METHODS
We included adult patients submitted to HT in a single center who died within the first 14 days after HT and were submitted to necropsy. Clinical and histological data were recorded retrospectively. All heart slides were reviewed by a blinded pathologist. We categorized patients in two groups (PGD and non-PGD) and compared findings between them.
RESULTS
Among 322 HTs, 26 patients were included. Median age was 51.5 years, 57.7% were male, 46.1% had non-ischemic cardiomyopathy, 30.8% Chagas cardiomyopathy and 23% ischemic cardiomyopathy. Eleven patients presented PGD, while 15 patients did not. PGD was severe in 72.7% of cases and moderate in 27.3%. PGD group had longer ischemic time (p=0.08), higher incidence of mechanical circulatory support (p=0.004), lower post-transplant biventricular ejection fraction (p=0.005). However, necropsy findings were similar between groups. Necrosis was detected in 80.7% of all cases (p=0.907 comparing groups), taking ≥ 10% of myocardial area in 46.1% of them, and 4 types of necrosis were found either in patients with and without PGD.
CONCLUSION
Cardiac pathological findings were similar in HT patients with or without PGD who died within 14 days after the transplant and necrosis was frequent in both groups, raising the hypothesis necrosis is not the cause of cardiac dysfunction in PGD.
PubMed: 38908210
DOI: 10.1016/j.cpcardiol.2024.102694 -
Virchows Archiv : An International... Jun 2024The aim of this multicenter prospective survey called PIT-EASY was to assess the relevance of the European Pituitary Pathology Group (EPPG) diagnostic tools for...
The aim of this multicenter prospective survey called PIT-EASY was to assess the relevance of the European Pituitary Pathology Group (EPPG) diagnostic tools for pituitary neuroendocrine tumors (PitNETs) to improve the quality of their histological diagnosis. Each center performed at least 30 histological cases of PitNETs using the EPPG tools and assessed their value using a scorecard with 10 questions. For each center, the histological cases were carried out by pathologists with varying levels of expertise in pituitary pathology defined as junior, intermediate, and expert. Two hundred and ninety histological cases were collected from six French and Italian centers. The three EPPG tools were validated and regarded as helpful for a more accurate and time-efficient diagnosis. The usefulness of level 2 and level 3 of the "EPPG's multi-step approach for immunohistochemistry" including pituitary transcription factors (PIT1, TPIT, and SF1) and chromogranin, SSTRs, and P53 respectively was higher in "other non-functioning" (silent plurihormonal PIT1, silent corticotroph, and null cell): 88% vs 32%, p < 10-6 and 42% vs 14%, p = 0.002, respectively. The diagnostic algorithm proved more useful for junior pathologists (p = 0.0001) and those with intermediate experience. PIT-EASY survey confirmed the importance of a standardized approach to PitNETs for an accurate and reproducible diagnosis and served as validation of the EPPG proposal. The tool appeared to be of practical value to junior participants and staff with intermediate experience for safe routine diagnostic reporting.
PubMed: 38907774
DOI: 10.1007/s00428-024-03849-x -
Forensic Science, Medicine, and... Jun 2024Traumatic brain injury (TBI) is one of the major causes of morbidity and mortality among young people and is a matter of concern for forensic pathologists. Many authors...
BACKGROUND
Traumatic brain injury (TBI) is one of the major causes of morbidity and mortality among young people and is a matter of concern for forensic pathologists. Many authors have tried to estimate a person's survival time (ST) after TBI using different approaches.
OBJECTIVE
The present study aimed to present an innovative workflow to estimate the ST after TBI by observing the inflammatory reaction of the dura mater (DM).
METHODS
The authors collected DM samples from 36 cadavers (20 with TBI and 16 with no history or signs of TBI). Each sample was labelled via immunohistochemistry with three different primary antibodies, CD15, CD68, and CD3, yielding 108 slides in total. The slides were digitalized and analysed using QuPath software.
RESULTS
The DM is involved in the inflammatory response after TBI. CD15 immunoreactivity allowed us to distinguish between subjects who died immediately after TBI and those with an ST of minutes or hours. CD3 immunoreactivity can be used to differentiate subjects with an ST of days from those with other STs. Moreover, the DM samples showed an acceptable diagnostic yield even in samples with signs of putrefaction.
PubMed: 38907772
DOI: 10.1007/s12024-024-00834-3