Stand-alone Tool Research Articles

A comparison of assessment tools for childcare centers in high vs. low resource settings

Studies suggest issues may arise when using childcare setting assessment tools designed for high-resource settings in low-resource settings to assess and improve the quality of care, including placing disproportionate weight on features of the childcare environment that may not be available or culturally appropriate within the low-resource context. This study compares a novel assessment tool developed in and for low-income and low-resource settings with a standardized “gold standard” tool developed for use in high-resource settings. The study included a randomized sample of 34 childcare centers in a low-resource context that provided care for approximately 918. Results suggest that the WCI-QCUALS assessment tool performed better at differentiating among childcare settings that were consolidated into the lowest rating on the ECERS-R. Further, the WCI-QCUALS was found to be a feasible, appropriate stand-alone tool for assessing the quality of childcare centers in low-resource settings. Additional refinement and validity testing in other countries and contexts will improve the understanding of how the quality of childcare across different contexts can be measured, and improved assessment of childcare quality in low-resource settings will enhance the ability to identify low-quality care that can be remedied to ensure appropriate care for vulnerable children.

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray

PurposePneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists. MethodWe retrospectively ran the AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports. ResultsOverall, the AI system achieved an accuracy of 74 % (95%CI 68–79 %), with a sensitivity of 66 % (95%CI 59–73 %) and a specificity of 93 % (95%CI 85–97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71–82 %, p = 0.355), with higher sensitivity (73 %, 95%CI 66–79 %, p = 0.040), albeit lower specificity (85 %, 95%CI 75–91 %, p = 0.034). The performance of AI was influenced by patient positioning at CXR (p = 0.040). ConclusionsThe proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.

Frustration Gap Bridging: A Second Look at Vocabulary Proficiency with the Yes/No Test

This research advances the scrutiny of the Yes/No test's (Meara: 1990) efficacy in evaluating vocabulary proficiency among language learners of high, middle, and low proficiency levels, focusing on its capability to mitigate the frustration commonly encountered by this demographic. As highlighted in the recent investigation (Goetz, 2023), the Yes/No test prompted respondents to signify their word familiarity through binary choices. Yes, they know a word, and No, they don’t. Involving a paltry 56 English language learners of lower proficiency to determine if the test's impact on enhancing vocabulary skills had merit, it was found that contrary to the anticipated outcomes, the test did not yield significant advancements in students' performance on subsequent unit tests compared to their counterparts who were not subjected to the Yes/No test. This absence of tangible benefits, combined with the test's failure to deliver a contextualized and nuanced comprehension of vocabulary, raises questions regarding its efficacy as a pedagogical tool for vocabulary enhancement. Nevertheless, the results did not dismiss the Yes/No test's potential utility. For this study, a second look will address two critical shortcomings: the limited sample size and the absence of a diverse range of proficiency levels among participants. This inquiry reevaluates the Yes/No test's capacity to effectively navigate the intricacies of language acquisition, proposing a more in-depth investigation into whether this binary and straightforward approach could indeed be beneficial or whether it underscores the imperative for a more holistic and contextually aware method in language instruction by expanding the sample size and including more proficiency levels. Questions remain regarding the efficacy of the Yes/No test. Is it optimally utilized as a straightforward, standalone tool, or should it be considered within the broader spectrum of comprehensive, context-sensitive learning strategies? This inquiry invites a nuanced evaluation of the test's role in language acquisition. It challenges researchers and practitioners to contemplate whether its simplicity is a virtue in isolation or if its true value emerges when integrated with a holistic approach to learning.

How to auscultate for heart sounds in infants and children.

This article provides an introduction to performing cardiac auscultation in infants and children aged 0-16 years, with the aim of improving children's nurses' knowledge and confidence in this procedure. Nurses wishing to develop competence in cardiac auscultation can use this article as a guide to complement supervised practical experience; however, it is not intended to determine an individual's competence. • Cardiac auscultation is a component of a structured cardiac assessment rather than a standalone tool. • It is important to auscultate all four main heart valve areas, listening for normal heart sounds and any additional sounds. • Several differences should be considered when auscultating heart sounds in infants and children compared with adults, including heart rate ranges and the potential need to use distraction techniques for those who are distressed. REFLECTIVE ACTIVITY: 'How to' articles can help to update your practice and ensure it remains evidence based. Apply this article to your practice. Reflect on and write a short account of: • How you think this article might help improve your practice when undertaking cardiac auscultation in infants and children. • How you could use this information to develop your own knowledge and skills before educating others on the appropriate technique and evidence base behind auscultating heart sounds in infants and children.

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It’s an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2; https://pypi.org/project/hladr4pred2/).

ModelHamiltonian: A Python-scriptable library for generating 0-, 1-, and 2-electron integrals.

ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser-Parr-Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope.

How accurate is the QbTest for measuring symptoms of ADHD in children and adults?

This literature review aimed to assess the reliability and validity of the quantified behavioural test (QbTest) to measure the symptoms of attention deficit hyperactivity disorder (ADHD), determining the extent to which results of the selected studies were accurate and generalisable. A literature review was undertaken, with each paper appraised using the Critical Appraisal Skills Programme randomised controlled trial checklist. Thematic analysis was also implemented to identify key themes and relationships between data sets. Three papers concluded that the QbTest increases clinical efficiency without compromising diagnostic accuracy, while others identified limitations relating to its ability to identify symptoms of impulsivity correctly. Additional issues were identified relating to external validity, generalisability and the extent to which the QbTest could differentiate ADHD from other conditions. It is concluded that the QbTest is an unreliable means of assessing ADHD in both children and adults, particularly when used as a stand-alone assessment tool. Thematic analysis highlighted concerns around diagnostic accuracy and the QbTest's inability to differentiate symptoms of ADHD from other conditions – fundamental flaws affecting the overarching fidelity of QbTesting.

Real-world evaluation of Brainomix e-Stroke software

Background and purposeBrainomix e-Stroke is an artificial intelligence-based decision support tool that aids the interpretation of CT imaging in the context of acute stroke. While e-Stroke has the potential to...

Leveraging Digital Technologies for Carbon Footprint Tracking in Perennial Cultivations: A Case Study of Walnut Orchard Establishment in Central Greece

The present paper aims to quantify the carbon emissions associated with the establishment of 15 walnut orchards (“Juglans californica”) in the greater area of Magnisia, Greece, with the use of a carbon footprint tool interconnected to a Farm Management Information System. The data collection spanned the first five years following the planting of the trees, providing a comprehensive view of the emissions during this critical establishment phase. Over the five-year period examined (February 2019–December 2023), the results revealed net carbon emissions amounting to 13.71 tn CO2 eq ha−1, with the calculated emissions showing an increasing trend from the first year through the fifth year. Scope 1 (7.38 tn CO2 eq ha−1) and Scope 2 (3.71 tn CO2 eq ha−1) emissions emerged as the most significant, while irrigation (drip irrigation) and fertilizing practices were identified as the highest contributors to emissions. This study highlights the significance of using integrated digital tools for monitoring the performance of cultivations rather than standalone tools that are currently widely available. Integrated tools that incorporate various applications simplify data collection, encourage accurate record-keeping, and facilitate certification processes. By automating data entry and calculations, these tools reduce human error during agricultural carbon management and save time; thus, the integration of digital monitoring tools is vital in improving data accuracy, streamlining certification processes, and promoting eco-friendly practices, crucial for the evolving carbon market.

Developing an Individualized Patient Decision Aid for Chronic Coronary Disease Based on the ISCHEMIA Trial: A Mixed-Methods Study

BACKGROUND: Pursuing initial invasive or conservative management of chronic coronary disease (CCD) is a preference-sensitive decision that should include shared decision-making. Communicating the benefits of either approach is challenging, as individual patients rarely achieve the population-averaged outcomes reported in clinical trials. Our objective was to develop a patient decision aid (PDA) with patient-specific estimates of outcomes for initial invasive versus conservative management of CCD, based on the ISCHEMIA trial (International Study of Comparative Health Effectiveness With Medical and Invasive Approaches). METHODS: This was a multiphase mixed-methods study using focus groups of outpatients with CCD, caregivers, clinicians, and researchers. Focus groups were held in Kansas City, MO and New York City, NY between September 2021 and June 2022. Patients with CCD were included if they had a positive stress test within 1 year. Phase 1 focused on patient priorities for outcomes to guide treatment decisions. Phase 2 involved PDA development and refinement. Phase 3 involved further refinement and member checking. Key themes involving shared decision-making and treatment preferences were elicited from focus groups using a deductive approach to develop a PDA representing the outcomes most important to patients. RESULTS: Of 46 patient and caregiver participants, the mean age was 63.5 years, 53% were female, 61% were White, 24% were Black, and 9% were Hispanic. When deciding between treatments, participants valued shared decision-making but generally deferred decisions to clinicians. The outcomes most important to participants were survival and quality of life, followed by physical functioning and symptoms. To represent these outcomes, participants favored simple visualizations, such as a speedometer or health meter. When deciding between treatment options, participants preferred to use the PDA collaboratively with a clinician instead of as a stand-alone tool. CONCLUSIONS: Our novel, patient-centered approach to developing a PDA for CCD with patient-specific outcomes has the potential to rapidly translate clinical trial results to individual patients and support shared decision-making.

Influenza sequence validation and annotation using VADR

Abstract Tens of thousands of influenza sequences are deposited into the GenBank database each year. The software tool FLu ANnotation tool (FLAN) has been used by GenBank since 2007 to validate and annotate incoming influenza sequence submissions and has been publicly available as a webserver but not as a standalone tool. Viral Annotation DefineR (VADR) is a general sequence validation and annotation software package used by GenBank for norovirus, dengue virus and SARS-CoV-2 virus sequence processing that is available as a standalone tool. We have created VADR influenza models based on the FLAN reference sequences and adapted VADR to accurately annotate influenza sequences. VADR and FLAN show consistent results on the vast majority of influenza sequences, and when they disagree, VADR is usually correct. VADR can also accurately process influenza D sequences as well as influenza A H17, H18, H19, N10 and N11 subtype sequences, which FLAN cannot. VADR 1.6.3 and the associated influenza models are now freely available for users to download and use. Database URL: https://bitbucket.org/nawrockie/vadr-models-flu.

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Comparison of the GAD-7 and ImPACT symptom cluster scores in measuring anxiety among college athletes

Assessing mental health needs among college athletes is important given the frequency of symptoms of depression and anxiety and associated complications that may arise after a sports-related concussion. The purpose of this study was to explore the utility of the General Anxiety Disorder-7 (GAD-7) as an independent measure of anxiety symptoms among Division II collegiate athletes (n = 568) during baseline concussion assessments, which also included the post-concussion symptom scale (PCSS) and a measure of depression, the Patient Health Questionnaire-9 (PHQ-9). While only 31 athletes (5.5%) fell above the established GAD-7 cutoff score of 5 or more, a sizable proportion of them (13 or 41.9%) endorsed 0 items on the PCSS affective symptom cluster. Additionally, ∼2% of athletes reported elevated symptoms of anxiety but not depression. These findings support the incorporation of a brief stand-alone anxiety screening tool during concussion baseline evaluations to ensure better detection of student athletes with elevated anxiety symptoms who might otherwise be overlooked, so they may be referred for further diagnostic assessment.

Rethinking Thin-Layer Chromatography for Screening Technetium-99m Radiolabeled Polymer Nanoparticles.

Thin-layer chromatography (TLC) is commonly employed to screen technetium-99m labeled polymer nanoparticle batches for unreduced pertechnetate and radio-colloidal impurities. Although this method is widely accepted, our findings applying radiolabeled PLGA/PLA-PEG nanoparticles underscore its lack of transferability between different settings and its limitations as a standalone quality control tool. While TLC profiles may appear similar for purified and radiocolloid containing nanoparticle formulations, their in vivo behavior can vary significantly, as demonstrated by discrepancies between TLC results and single-photon emission computed tomography (SPECT) and biodistribution data. This highlights the urgent need for a case-by-case evaluation of TLC methods for each specific nanoparticle type. Our study revealed that polymeric nanoparticles cannot be considered analytically uniform entities in the context of TLC analysis, emphasizing the complex interplay between nanoparticle composition, radiolabeling conditions, and subsequent biological behavior.

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

Chest Pain in Primary Care: A Systematic Review of Risk Stratification Tools to Rule Out Acute Coronary Syndrome.

Chest pain frequently poses a diagnostic challenge for general practitioners (GPs). Utilizing risk stratification tools might help GPs to rule out acute coronary syndrome (ACS) and make appropriate referral decisions. We conducted a systematic review of studies evaluating risk stratification tools for chest pain in primary care settings, both with and without troponin assays. Our aims were to assess the performance of tools for ruling out ACS and to provide a comprehensive review of the current evidence. We searched PubMed and Embase for articles up to October 9, 2023 concerning adult patients with acute chest pain in primary care settings, for whom risk stratification tools (clinical decision rules [CDRs] and/or single biomarker tests) were used. To identify eligible studies, a combination of active learning and backward snowballing was applied. Screening, data extraction, and quality assessment (following the Quality Assessment of Diagnostic Accuracy Studies-2 tool) were performed independently by 2 researchers. Of the 1,204 studies screened, 14 were included in the final review. Nine studies validated 7 different CDRs without troponin. Sensitivities ranged from 75.0% to 97.0%, and negative predictive values (NPV) ranged from 82.4% to 99.7%. None of the CDRs outperformed the unaided judgment of GP's. Five studies reported on strategies using troponin measurements. Studies using high-sensitivity troponin showed highest diagnostic accuracy with sensitivity 83.3% to 100% and NPV 98.8% to 100%. Clinical decision rules without troponin and the use of conventional troponin showed insufficient sensitivity to rule out ACS in primary care and are not recommended as standalone tools. High-sensitivity troponin strategies are promising, but studies are limited. Further prospective validation in primary care is needed before implementation.

Assessing GPT-4 multimodal performance in radiological image analysis.

This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology. We analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V. Modalities included ultrasound (US), computerized tomography (CT), and X-ray images. The interpretations provided by GPT-4V were then compared with those of senior radiologists. This comparison aimed to evaluate the accuracy of GPT-4V in recognizing the imaging modality, anatomical region, and pathology present in the images. GPT-4V identified the imaging modality correctly in 100% of cases (221/221), the anatomical region in 87.1% (189/217), and the pathology in 35.2% (76/216). However, the model's performance varied significantly across different modalities, with anatomical region identification accuracy ranging from 60.9% (39/64) in US images to 97% (98/101) and 100% (52/52) in CT and X-ray images (p < 0.001). Similarly, pathology identification ranged from 9.1% (6/66) in US images to 36.4% (36/99) in CT and 66.7% (34/51) in X-ray images (p < 0.001). These variations indicate inconsistencies in GPT-4V's ability to interpret radiological images accurately. While the integration of AI in radiology, exemplified by multimodal GPT-4, offers promising avenues for diagnostic enhancement, the current capabilities of GPT-4V are not yet reliable for interpreting radiological images. This study underscores the necessity for ongoing development to achieve dependable performance in radiology diagnostics. Although GPT-4V shows promise in radiological image interpretation, its high diagnostic hallucination rate (> 40%) indicates it cannot be trusted for clinical use as a standalone tool. Improvements are necessary to enhance its reliability and ensure patient safety. GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications.

Ohnut vs waitlist control for the self-management of endometriosis-associated deep dyspareunia: a pilot randomized controlled trial.

Deep dyspareunia affects 50% of people with endometriosis. The Ohnut is a set of interlocking rings that fit over the penis/insertive object. One or more rings can be used to limit insertion depth and reduce deep dyspareunia. We conducted a pilot, parallel, open-label randomized controlled trial (RCT) to investigate the feasibility of the study design and the acceptability and preliminary efficacy of the Ohnut. Participants were recruited from a tertiary center for endometriosis. Eligibility criteria were surgically confirmed endometriosis, age 19-49years, monogamous sexual relationship with a partner willing to participate in the study, and no comorbid superficial dyspareunia, anxiety, or depression. Couples were randomized into an intervention group or a waitlist control group using a 1:1 allocation ratio. All couples had sex as normal during weeks 1 to 4 (baseline period), and couples in the intervention group used the Ohnut with sex during weeks 5 to 10 (intervention period) while controls had sex as normal. Patient participants used daily diaries to record sexual activity and deep dyspareunia score (0-10) for the 10-week study. Intervention group participants completed an acceptability questionnaire at the end of the study. The primary outcomes were feasibility of the study and acceptability of the Ohnut. We also assessed differences in deep dyspareunia scores in the participants who used the Ohnut compared to the control participants who did not. We recruited approximately 5 couples per month of active recruitment. Of 864 potentially eligible participants, we successfully contacted 44.7% (n = 386), of whom 8.0% (n = 31) consented, 64.8% (n = 250) were ineligible, and 27.2% (n = 105) declined. Thirty-one couples were randomly assigned to the intervention or control group, and 17 couples completed the study. Intervention group couples used the Ohnut for an average of 72.4% (32.7%) of sexual encounters during the intervention period. The mean acceptability index score for the Ohnut was 0.83 (0.078) among patients and 0.83 (0.049) among partners (index between 0 and 1). After controlling for baseline deep dyspareunia, there was a significant difference in the intervention period mean deep dyspareunia scores between the control and intervention group (4.69 (2.44) vs 2.46 (1.82), P = .012). We identified preliminary evidence for the acceptability and efficacy of the Ohnut among both patients and partners, suggesting that the Ohnut may be a useful stand-alone or adjuvant management tool for endometriosis-associated deep dyspareunia. Strengths of this study were the "real-world" use of the Ohnut and data collection from both patients and partners. Limitations of the study design included the strict eligibility criteria that affected feasibility and generalizability. This pilot RCT indicated that the Ohnut may be an acceptable and effective intervention to reduce endometriosis-associated deep dyspareunia. We identified opportunities to improve design for a larger RCT. This clinical trial was registered with clinicaltrials.gov (#NCT04370444).

No-code method for lithography template cell parameterization for faster design cycles

This work presents a Python-based architecture packaged as a standalone tool, to enable the parameterization of lithography structures without the need for scripting. By feeding a lithography template file obtained from an existing layout editor into the tool, a ‘scaffold’ shape is created and recognised. The tool allows for the parameterization of created geometries and the establishment of parameterized rules between geometric features, which can be conveniently modified in tabular format. This work facilitates no-code procedural generation of geometrically distinct instances, significantly reducing the time required for complex lithography template development compared to traditional scripting methods.

PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model's predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.