Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.

This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.

One-class modelling applied to ATR-FTIR spectroscopy for determining document forgeries related to paper aging.

One of the great challenges of document analysis is determining document forgeries. The present work proposes a non-destructive approach to discriminate natural and artificially aged papers using infrared spectroscopy and soft independent modeling by class analogy (SIMCA) algorithms. This is of particular interest in cases of document falsifications made by artificial aging, for this study, SIMCA, and Data-Driven SIMCA (DD-SIMCA) classification models were built using naturally aged paper samples, taken from three time periods: 1st period from 1998 to 2003; 2nd period from 2004 to 2009; and 3rd period from 2010 to 2015. Artificially aged samples (exposed to high temperature or UV radiation) were used as test sets. Promising results in detecting document falsifications related to aging were obtained. Samples artificially aged at high temperature were correctly discriminated from the authentic samples (naturally aged) with 100% accuracy. In contrast, the samples under the photodegradation process showed a lower classification performance, with results above 90%.

Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Evaluation of functioning and associated factors in children and adolescents with osteogenesis imperfecta / Avaliação da funcionalidade e fatores associados de crianças e adolescentes com osteogênese imperfeita

ABSTRACT Objective: The aim of this study was to evaluate the functioning and associated factors in children and adolescents with osteogenesis imperfecta (OI). Methods: This is a cross-sectional study conducted on 30 children and adolescents with OI. Medical records, use of bisphosphonates, socioeconomic status, handgrip strength, balance, joint hypermobility, ambulatory level, and the Pediatric Evaluation of Disability Inventory—Computer Adaptative Test (PEDI-CAT) scores were assessed. Data is presented as mean and standard deviation and Student's t-test or Mann-Whitney U test. Categorical data is presented as frequency and analyzed using Fisher's exact test. Within-group analyses were conducted using ANCOVA or Wilcoxon signed-rank test. Correlations used Kendall's Tau-b test. Results: The participants involved in this study were 6-18 years old. The sample was separated into two groups according to disease severity. The moderate/severe OI group (n=10) presented a lower height and muscular strength than the mild group (n=20). Muscle weakness was observed in all participants with OI when compared with the normal population. No differences were observed between the groups in the PEDI-CAT scores except for the mobility domain. There were correlations between the PEDI-CAT mobility domain and the number of fractures, OI type, weight, and balance; there was also a correlation between the PEDI-CAT daily activities, mobility, responsibility, and social/cognitive domains. Conclusions: The findings suggest that children with moderate/severe forms of OI can achieve the same function levels as children with mild OI. Fractures can have a major influence on the functional level, and treatment should focus on the prevention and rehabilitation of these events when they occur.

RESUMO Objetivo: Avaliar a funcionalidade e fatores associados em crianças e adolescentes com osteogênese imperfeita (OI). Métodos: Estudo transversal com 30 crianças e adolescentes com OI. Foram avaliados prontuários médicos, uso de bisfosfonatos, características socioeconômicas, dinamometria de preensão palmar, equilíbrio, hipermobilidade articular, nível de deambulação e escores do Pediatric Evaluation of Disability Inventory - Computer Adaptative Test (PEDI-CAT). Os dados foram apresentados em média e desvio padrão e comparados por teste t por Mann-Whitney, enquanto os categóricos foram apresentados em frequência e comparados pelo teste exato de Fisher. Análises intragrupos foram realizadas por análise de covariância (ANCOVA) ou Teste de Wilcoxon para postos sinalizados. O teste Tau-b de Kendall foi usado para correlações. Resultados: A idade variou de 6 a 18 anos. A amostra foi dividida em dois grupos de acordo com a gravidade da doença. Casos moderados/graves (n=10) apresentaram menor estatura e força muscular comparadas às dos leves (n=20). Fraqueza muscular foi observada em todos os casos de OI quando comparados à população normal. Não houve diferença nos domínios do PEDI-CAT com exceção do domínio mobilidade. Houve correlação entre o número de fraturas, tipo de OI, peso e equilíbrio e o domínio mobilidade; e entre os domínios Atividades Diárias e Mobilidade e Responsabilidade e Social/cognitivo do PEDI-CAT. Conclusões: Nossos achados sugerem que crianças com OI moderada/severa podem atingir o mesmo nível de funcionalidade que crianças com a forma leve. Fraturas podem ter grande influência no nível de funcionalidade e o tratamento deve enfocar a prevenção e a reabilitação desses eventos.

Genetic testing and counseling for hypertrophic cardiomyopathy: An evidence-based practice resource of the National Society of Genetic Counselors.

Hypertrophic cardiomyopathy (HCM) is a common hereditary condition affecting approximately 1 in 500 adults. It is characterized by marked clinical heterogeneity with individuals experiencing minimal to no symptoms, while others may have more severe outcomes including heart failure and sudden cardiac death. Genetic testing for HCM is increasingly available due to advances in DNA sequencing technologies and reduced costs. While a diagnosis of HCM is a well-supported indication for genetic testing and genetic counseling, incorporation of genetic services into the clinical setting is often limited outside of expert centers. As genetic counseling and testing have become more accessible and convenient, optimal integration of genomic data into the clinical care of individuals with HCM should be instituted, including delivery via genetic counseling. Drawing on recommendations from recent disease guidelines and systematic evidence reviews, we highlight key recommendations for HCM genetic testing and counseling. This practice resource provides a comprehensive framework to guide healthcare providers in the process of genetic test selection, variant classification, and cascade testing for genetic evaluation of HCM.

A StarGAN and transformer-based hybrid classification-regression model for multi-institution VMAT patient-specific quality assurance.

BACKGROUND: The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis. PURPOSE: This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions. METHODS: A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation. RESULTS: For the 3%/3, 3%/2, and 2%/2 mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2 mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework. CONCLUSIONS: The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.

BACKGROUND: Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules. METHODS: Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules. RESULTS: Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones. CONCLUSIONS: Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic. CLINICALTRIALS: gov/ct2/show/NCT05432128 .

Classification of primary mitral regurgitation using extramitral cardiac involvement in patients undergoing transcatheter edge-to-edge repair.

BACKGROUND: An enhanced classification of primary mitral regurgitation (PMR) based on extramitral cardiac involvement may refine patient selection and optimize the timing of transcatheter edge-to-edge repair (TEER). AIMS: This study aimed to assess the prognostic significance of a recently established classification system that characterizes the extent of extramitral cardiac damage in patients undergoing TEER for PMR. METHODS: Consecutive PMR patients who received MitraClip implantation were categorized according to the presence of extramitral cardiac damage, determined through preprocedural echocardiography. The classifications included no damage or only left ventricular dilatation (group 0), left atrial involvement (group 1), right ventricular volume/pressure overload (group 2), right ventricular failure (group 3), or left ventricular failure (group 4). Cox-proportional hazard models were used to ascertain the impact of PMR groups on the primary composite outcome of all-cause mortality or rehospitalization for heart failure (HHF) over 2 years. RESULTS: In a cohort of 322 eligible PMR patients undergoing TEER (median age: 83 years; 41% female) between 2013 and 2020, the following distribution emerged: group 0 (10 patients, 3%), group 1 (96 patients, 30%), group 2 (117 patients, 36%), group 3 (56 patients, 18%), and group 4 (43 patients, 13%). Kaplan-Meier analysis demonstrated a significant decline in freedom from the primary outcome as group severity increased (log-rank p = 0.030). On multivariate analysis, the degree of extramitral cardiac involvement was significantly associated with the primary outcome (HR: 1.30; 95% CI: 1.02-1.67; p = 0.043), primarily driven by HHF. CONCLUSIONS: This innovative classification system for PMR, based on extramitral cardiac involvement, carries significant prognostic implications for clinical outcomes following TEER. Integrating this classification system into clinical decision-making could enhance risk stratification and optimize the timing of TEER in these patients.

Prescribing Patterns of Antibiotics in Pediatric Wards of Secondary Care Centers: A Retrospective Study Based on WHO AWaRe Classification.

Objectives. Antimicrobial resistance (AMR) poses a significant threat to global public health, with substantial mortality rates attributed to AMR-related infections. Pediatric populations face heightened vulnerability due to prevalent antimicrobial misuse. This study aimed at addressing the significant threat of antimicrobial resistance (AMR) and its associated mortality rates. Methods. This retrospective cross-sectional multicentric study investigated antibiotic prescribing patterns in pediatric wards of 4 secondary care hospitals affiliated with Aga Khan University Hospital. The study utilized the WHO Access, Watch, and Reserve (AWaRe) classification framework. Data from 6934 encounters were analyzed. Results. Antibiotics were prescribed in 78.1% of encounters, with intravenous administration being predominant (98.6%). Ceftriaxone was the most prescribed antibiotic agent (45.8%), and third-generation cephalosporins constituted the most prevalent antibiotic class (54.4%). Pneumonia exhibited the highest prescription rate (99.9%), with Watch group antibiotics being predominantly prescribed (>80%) across hospitals. Conclusion. These findings underscore the urgency for targeted interventions to optimize prescribing practices and mitigate resistance.

Novel ML-Based Algorithm for Detecting Seizures from Single-Channel EEG.

There is a need for seizure classification based on EEG signals that can be implemented with a portable device for in-home continuous minoring of epilepsy. In this study, we developed a novel machine learning algorithm for seizure detection suitable for wearable systems. Extreme gradient boosting (XGBoost) was implemented to classify seizures from single-channel EEG obtained from an open-source CHB-MIT database. The results of classifying 1-s EEG segments are shown to be sufficient to obtain the information needed for seizure detection and achieve a high seizure sensitivity of up to 89% with low computational cost. This algorithm can be impeded in single-channel EEG systems that use in- or around-the-ear electrodes for continuous seizure monitoring at home.

Classification of Infiltrative Heart Diseases MORAL-STAGE System.

Infiltrative heart disease (InHD) is a group of diseases characterized by the deposition of abnormal substances in the heart tissue, causing diastolic, less often systolic, dysfunction of the ventricle(s). Their classification still does not exist. In 2013, the MOGE(S) classification of cardiomyopathies was published, taking into account, along with the morphological and functional characteristics of the heart, damage to other organs, the presence of genetic mutations, acquired causes (e.g., myocardial inflammation, autoimmune diseases, storage diseases, amyloidosis), etc. By analogy with it we offer the MORAL-STAGE classification for InHD. It includes ten features: morphofunctional characteristics (M), organ damage (O), risk of cardiac death (R), age of clinical presentation, age of disease-specific therapy initiation (A), localization of the infiltrative process (inside or outside the cell, L), information about the functional class heart failure and stage of infiltrative heart disease (S), treatment (T), abnormal rhythm or conduction (A), genetic or familial nature of inheritance (G), etiology of the process (E). This article summarizes the cornerstones of the MORAL-STAGE classification and its clinical relevance. In addition, new issues are discussed that can be considered in future versions of the MORAL-STAGE classification.

The 2018 Classification of Periodontal Diseases: An Observational Study on Inter-examiner Agreement among Undergraduate Students.

PURPOSE: The objective of the present observational study was to assess the inter-examiner agreement for the diagnosis of periodontitis using the 2018 CPD among fourth and fifth year undergraduate students. It is hypothesised that there is no difference in the inter-examiner relaibility between fourth- and fifth-year undergraduate students regarding staging and grading periodontal disease using the 2018 Classification of Periodontal Diseases (CPD). MATERIALS AND METHODS: All participants received training on the 2018 CPD scheme through a mandatory periodontics course conducted by a periodontist. Documentation for seven deidentified periodontitis patients, comprising medical history, dental history including tooth loss, intra-oral photographs and radiographs, periodontal charts reporting probing depth, plaque and bleeding on probing scores, furcation involvement and clinical attachment loss, was sent via e-mail to undergraduate students. The cases consisted of one sextant, and the participants were instructed to assume the sextant to be a true representation of the entire dentition. Power analysis was done on pilot data, and the level of significance was set at p0.05. RESULTS: The percentage of undergraduate students in the fourth and fifth year that correctly identified the stage of periodontitis according to the 2018 CPD ranged between 28% and 72% and 18.5% and 77.8%, respectively. The percentage of undergraduate students in the fourth and fifth year that correctly identified the grade of periodontitis ranged between 40% and 88% and 51.8% and 92.5%, respectively. The overall staging and grading ranged between 22.8% and 74.1%, and 45.66% and 87.4%, respectively. There was no statistically significant difference between fourth- and fifth-year undergraduate students with regards to assigning the correct diagnoses to case documentation in terms of either stage or grade. CONCLUSION: Fourth- and fifth-year undergraduate students demonstrated high inter-examiner agreement using the 2018 CPD.

High-frequency episodic migraine: Time for its recognition as a migraine subtype?

BACKGROUND: High-frequency episodic migraine (HFEM) has gained attention in the field of headache research and clinical practice. In this narrative review, we analyzed the available literature to assess the evidence that could help decide whether HFEM may represent a distinct clinical and/or biological entity within the migraine spectrum. METHODS: The output of the literature search included 61 papers that were allocated to one of the following topics: (i) socio-demographic features and burden; (ii) clinical and therapeutic aspects; (iii) pathophysiology; and (iv) classification. RESULTS: Multiple features differentiate subjects with HFEM from low-frequency episodic migraine and from chronic migraine: education, employment rates, quality of life, disability and psychiatric comorbidities load. Some evidence also suggests that HFEM bears a specific profile of activation of cortical and spinal pain-related pathways, possibly related to maladaptive plasticity. CONCLUSIONS: Subjects with HFEM bear a distinctive clinical and socio-demographic profile within the episodic migraine group, with a higher disease burden and an increased risk of transitioning to chronic migraine. Recognizing HFEM as a distinct entity is an opportunity for the better understanding of migraine and the spectrum of frequency with which it can manifest, as well as for stimulating further research and more adequate public health approaches.

Ontology extension by online clustering with large language model agents.

An ontology is a structured framework that categorizes entities, concepts, and relationships within a domain to facilitate shared understanding, and it is important in computational linguistics and knowledge representation. In this paper, we propose a novel framework to automatically extend an existing ontology from streaming data in a zero-shot manner. Specifically, the zero-shot ontology extension framework uses online and hierarchical clustering to integrate new knowledge into existing ontologies without substantial annotated data or domain-specific expertise. Focusing on the medical field, this approach leverages Large Language Models (LLMs) for two key tasks: Symptom Typing and Symptom Taxonomy among breast and bladder cancer survivors. Symptom Typing involves identifying and classifying medical symptoms from unstructured online patient forum data, while Symptom Taxonomy organizes and integrates these symptoms into an existing ontology. The combined use of online and hierarchical clustering enables real-time and structured categorization and integration of symptoms. The dual-phase model employs multiple LLMs to ensure accurate classification and seamless integration of new symptoms with minimal human oversight. The paper details the framework's development, experiments, quantitative analyses, and data visualizations, demonstrating its effectiveness in enhancing medical ontologies and advancing knowledge-based systems in healthcare.

A NIRS-based recognition of coal and rock using convolution-multiview broad learning system.

Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.

Rethinking the classification of non-digestible carbohydrates: Perspectives from the gut microbiome.

Clarification is required when the term "carbohydrate" is used interchangeably with "saccharide" and "glycan." Carbohydrate classification based on human digestive enzyme activities brings clarity to the energy supply function of digestible sugars and starch. However, categorizing structurally diverse non-digestible carbohydrates (NDCs) to make dietary intake recommendations for health promotion remains elusive. In this review, we present a summary of the strengths and weaknesses of the traditional dichotomic classifications of carbohydrates, which were introduced by food chemists, nutritionists, and microbiologists. In parallel, we discuss the current consensus on commonly used terms for NDCs such as "dietary fiber," "prebiotics," and "fermentable glycans" and highlight their inherent differences from the perspectives of gut microbiome. Moreover, we provide a historical perspective on the development of novel concepts such as microbiota-accessible carbohydrates, microbiota-directed fiber, targeted prebiotics, and glycobiome. Crucially, these novel concepts proposed by multidisciplinary scholars help to distinguish the interactions between diverse NDCs and the gut microbiome. In summary, the term NDCs created based on the inability of human digestive enzymes fails to denote their interactions with gut microbiome. Considering that the gut microbiome possesses sophisticated enzyme systems to harvest diverse NDCs, the subclassification of NDCs should be realigned to their metabolism by various gut microbes, particularly health-promoting microbes. Such rigorous categorizations facilitate the development of microbiome-targeted therapeutic strategies by incorporating specific types of NDCs.

True Mitotic Count Prediction in Gastrointestinal Stromal Tumors: Bayesian Network Model and PROMETheus (Preoperative Mitosis Estimator Tool) Application Development.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy. OBJECTIVE: The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs. METHODS: Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed. RESULTS: Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model's accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model. CONCLUSIONS: The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

Cycle-ESM: Generation-assisted classification of antifungal peptides using ESM protein language model.

The rising prevalence of invasive fungal infections and the emergence of antifungal resistance highlight the urgent need for new antifungal medications. Antifungal peptides have emerged as promising alternatives to traditional antimicrobial agents. The identification of natural or synthetic antifungal peptides is crucial for advancing antifungal drug development. Typically, the availability of antifungal samples is limited, and significant sequence diversity exists among antifungal peptides, posing challenges for high-throughput screening. To address the identification challenge of antifungal peptides with limited sample availability, this study introduces the Cycle ESM method. Initially, the method utilises the ESM protein language model to generate additional data on antifungal peptides, serving as a data augmentation technique to enhance model training effectiveness. Subsequently, the ESM is employed in conjunction with a textCNN model to construct a classifier for peptide prediction, with a comprehensive exploration of peptide characteristics to improve prediction accuracy. Experimental results demonstrate that the performance of the Cycle ESM method surpasses that of existing methods across three distinct antifungal peptide datasets. This study presents a novel approach to antifungal peptide prediction and offers innovative insights for addressing classification problems with limited sample availability.

Accommodating Learners: An Adaptive Approach to Surgical Hand Preparation With Crutches.

PROBLEM: Accommodations for injured and disabled surgical providers have to balance an individual's needs with measures that ensure sterility requirements, patient and provider safety. The highly specialized nature of the surgical environment poses challenges when implementing changes in the operating room and literature is limited on adaptive surgical hand preparation techniques necessary to maximize a disabled medical student's active participation in their surgical clerkship. INTERVENTION: This paper presents a detailed account of the development and implementation of an adaptive surgical hand preparation designed to address mobility needs, enabling a student's active participation and education in the surgical curriculum. This offers a framework for adapting traditional surgical hand preparation techniques for crutches consisting of essential requirements in terms of equipment and personnel, step-by-step guide for implementation, discussion of potential risks related to contamination and safety, and a discussion on future directions for further innovation. CONTEXT: An adaptive surgical hand preparation technique was necessary to sterilize forearm crutches for a third-year medical student with a physical disability to ensure accessibility in the operating room and equity in surgical clerkship and medical education. Successful use of this protocol, in over 40 surgical cases throughout an 8-week surgical clerkship, created opportunity for a disabled medical student to access the sterile operating table through collaboration and innovation in the operating room. IMPACT: The adaptive hand preparation and sterile crutch cover solution was necessary for the student to assist in open, laparoscopic, and surgical procedures resulting in high clinical performance marks in the surgical clerkship. Beyond the individual benefit, this protocol promotes the importance of equity in medication education and encourages diversity through adaptive measures in the surgical field. LESSONS LEARNED: Designing an adaptive sterilization protocol for use of crutches in the operating room serves as an example of educational engineering and adaptable accessibility. The entire collaborative effort involving the medical student, university, surgical providers and operating room staff demonstrates the importance of teamwork in creating access in healthcare settings. Through learned experience, the authors provide insights for future directions for innovation, aiming to enhance access and inclusivity in medical education and surgical practice. This paper reflects on the broader implications of educational engineering and inclusive practices in healthcare.