Artificial neural networks have been used in various fields to solve classification and prediction tasks. However, it is unclear if these may be adequate methods to predict psychological treatment outcomes. This study aimed to evaluate the prognostic accuracy of neural networks using psychological treatment outcomes data.

Three neural network models (TensorFlow, nnet, and monmlp) and a generalised linear regression model were compared in their ability to predict post-treatment remission of depression symptoms in a large naturalistic sample (n = 69,489) of patients accessing low intensity cognitive behavioural therapy. Prognostic accuracy was evaluated using the area under the curve (AUC) in an external cross-validation design.

The AUC of the neural networks in an external test sample ranged from 0.64 to 0.65 and the AUC of the linear regression model was 0.63.

Neural networks can help predict symptom remission in new samples with moderate accuracy, although these models were no more accurate than a simpler inferential statistical linear regression model.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.

In ophthalmology, artificial intelligence (AI) demonstrates potential in using ophthalmic imaging across diverse diseases, often matching ophthalmologists' performance. However, the range of machine learning models for epiretinal membrane (ERM) management, which differ in methodology, application, and performance, remains largely unsynthesized.

Epiretinal membrane management relies on clinical evaluation and imaging, with surgical intervention considered in cases of significant impairment. AI analysis of ophthalmic images and clinical features could enhance ERM detection, characterization, and prognostication, potentially improving clinical decision-making. This scoping review aims to evaluate the methodologies, applications, and reported performance of AI models in ERM diagnosis, characterization, and prognostication.

A comprehensive literature search was conducted across 5 electronic databases including Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science Core Collection from inception to November 14, 2024. Studies pertaining to AI algorithms in the context of ERM were included. The primary outcomes measured will be the reported design, application in ERM management, and performance of each AI model.

Three hundred ninety articles were retrieved, with 33 studies meeting inclusion criteria. There were 30 studies (91%) reporting their training and validation methods. Altogether, 61 distinct AI models were included. OCT scans and fundus photographs were used in 26 (79%) and 7 (21%) papers, respectively. Supervised learning and both supervised and unsupervised learning were used in 32 (97%) and 1 (3%) studies, respectively. Twenty-seven studies (82%) developed or adapted AI models using images, whereas 5 (15%) had models using both images and clinical features, and 1 (3%) used preoperative and postoperative clinical features without ophthalmic images. Study objectives were categorized into 3 stages of ERM care. Twenty-three studies (70%) implemented AI for diagnosis (stage 1), 1 (3%) identified ERM characteristics (stage 2), and 6 (18%) predicted vision impairment after diagnosis or postoperative vision outcomes (stage 3). No articles studied treatment planning. Three studies (9%) used AI in stages 1 and 2. Of the 16 studies comparing AI performance to human graders (i.e., retinal specialists, general ophthalmologists, and trainees), 10 (63%) reported equivalent or higher performance.

Artificial intelligence-driven assessments of ophthalmic images and clinical features demonstrated high performance in detecting ERM, identifying its morphological properties, and predicting visual outcomes following ERM surgery. Future research might consider the validation of algorithms for clinical applications in personal treatment plan development, ideally to identify patients who might benefit most from surgery.

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

This study investigated the development and validation of predictive models for estimating foliar nitrogen (N), phosphorus (P), and potassium (K) contents, along with shoot dry mass (SDM) of Brachiaria ruziziensis L. The approach utilized Vis-NIR-SWIR spectroscopy coupled with multivariate statistical techniques (PLS, PCR) and machine learning algorithms (SVM, RF). A triple-factorial, completely randomized design with ten replications per treatment was employed in a greenhouse setting. Treatments included type of input (limestone-mining coproducts), input particle size (filler and powder), and soil class (Arenosol and Ferralsol). Following input incubation, B. ruziziensis was sown. Forty days later, foliar spectra and leaves were collected. Chemical analysis determined NPK content, along with SDM. The study developed predictive models utilizing Vis-NIR-SWIR spectroscopy, Partial Least Squares (PLS), and machine learning algorithms like Support Vector Machine (SVM) and Random Forest (RF) to estimate foliar N, P, K, and biomass. Model adjustments achieved R2p > 0.70 and RPDp > 1.80 for PLS, SVM, and RF models across all variables (SDM, N, P, and K). These results highlight the effectiveness of specific spectral bands for nutrient and biomass discrimination and emphasize the potential of these techniques for rapid, non-destructive nutrient content estimation. The findings support the integration of advanced spectroscopic methods with machine learning algorithms for improved precision agriculture practices, providing a more sustainable alternative for nutrient and biomass analysis in forage crops. This approach optimizes forage production and minimizes atmospheric CO2 emissions.

This review aims to explore the integration of artificial intelligence (AI) in laryngology, with specific focus on the barriers preventing translation from pilot studies into routine clinical practice and strategies for successful implementation.

Laryngology has seen an increasing number of pilot and proof-of-concept studies demonstrating AI's ability to enhance diagnostics, treatment planning, and patient outcomes. Despite these advancements, few tools have been successfully adopted in clinical settings. Effective implementation requires the application of established implementation science frameworks early in the design phase. Additional factors required for the successful integration of AI applications include addressing specific clinical needs, fostering diverse and interdisciplinary teams, and ensuring scalability without compromising model performance. Governance, epistemic, and ethical considerations must also be continuously incorporated throughout the project lifecycle to ensure the safe, responsible, and equitable use of AI technologies.

While AI hold significant promise for advancing laryngology, its implementation in clinical practice remains limited. Achieving meaningful integration will require a shift toward practical solutions that prioritize clinicians' and patients' needs, usability, sustainability, and alignment with clinical workflows.

Clinical prediction models help inform health care professionals, patients, and their families about the risks of disease or a future outcome and guide decision-making aimed at mitigating these risks. In this article, we review the principles of clinical prediction model development and validation, including predictor selection, model performance measures, internal validation, translation of a model to a scoring rule for clinical application, and external validation. We illustrate each principle using a published example of a prediction model developed to predict the presence of invasive bacterial infection in well-appearing febrile infants aged no more than 60 days presenting to emergency departments.

This study aims to develop a deep learning model for a robust diagnosis of Carpal Tunnel Syndrome (CTS) based on comparative classification leveraging the ultrasound images of the thenar and hypothenar muscles.

We recruited 152 participants, both patients with varying severities of CTS and healthy individuals. The enrolled patients underwent ultrasonography, which provided ultrasound image data of the thenar and hypothenar muscles from the median and ulnar nerves. These images were used to train a deep learning model. We compared the performance of our model with previous comparative methods using echo intensity ratio or machine learning, and non-comparative methods based on deep learning. During the training process, comparative guidance based on cosine similarity was used so that the model learns to automatically identify the abnormal differences in echotexture between the ultrasound images of the thenar and hypothenar muscles.

The proposed deep learning model with comparative guidance showed the highest performance. The comparison of Receiver operating characteristic (ROC) curves between models demonstrated that the Comparative guidance was effective in autonomously identifying complex features within the CTS dataset.

The proposed deep learning model with comparative guidance was shown to be effective in automatically identifying important features for CTS diagnosis from the ultrasound images. The proposed comparative approach was found to be robust to the traditional problems in ultrasound image analysis such as different cut-off values and anatomical variation of patients.

Proposed deep learning methodology facilitates accurate and efficient diagnosis of CTS from ultrasound images.

Mycotoxins, toxic secondary metabolites produced by fungi, pose a significant threat to food safety and human health. The occurrence of mycotoxins in food commodities necessitates accurate and reliable detection methods. Advanced detection techniques, such as chromatographic techniques and immunochemical assays, have improved sensitivity and specificity. However, the lack of standardized reference material, particularly in less privileged countries, hinders method validation and proficiency testing, ultimately affecting mycotoxin testing and regulation. Moreover, these techniques are complex as they require specialized equipment, and well-trained personnel, thus limiting their practical applications. This comprehensive review provides an up-to-date overview of the occurrence of mycotoxins and recent advancements in detection methods. It examines the crucial role of mycotoxin standards as reference materials for ensuring reliable results in mycotoxins analysis in agriculture commodities. The review addresses emerging challenges, knowledge gaps, and future research directions in mycotoxin detection and reference material development. By synthesizing existing literature, this review aims to provide valuable resources for researchers, policymakers, and other stakeholders in food safety, highlighting the importance of integrated approaches to mitigate mycotoxin contamination and ensuring food safety.

The last decade has witnessed a surge in artificial intelligence (AI). With bone metastases becoming more prevalent, there is an increasing call for personalized treatment options, a domain where AI can greatly contribute. However, integrating AI into clinical settings has proven to be difficult. Therefore, we aimed to provide an overview of AI modalities for treating bone metastases and recommend implementation-worthy models based on TRIPOD, CLAIM, and UPM scores.

This systematic review included 92 studies on AI models in bone metastases between 2008 and 2024. Using three assessment tools we provided a reliable foundation for recommending AI modalities fit for clinical use (TRIPOD or CLAIM ≥ 70 % and UPM score ≥ 10).

Most models focused on survival prediction (44/92;48%), followed by imaging studies (37/92;40%). Median TRIPOD completeness was 70% (IQR 64-81%), CLAIM completeness was 57% (IQR 48-67%), and UPM score was 7 (IQR 5-9). In total, 10% (9/92) AI modalities were deemed fit for clinical use.

Transparent reporting, utilizing the aforementioned three evaluation tools, is essential for effectively integrating AI models into clinical practice, as currently, only 10% of AI models for bone metastases are deemed fit for clinical use. Such transparency ensures that both patients and clinicians can benefit from clinically useful AI models, potentially enhancing AI-driven personalized cancer treatment.

Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized.

This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions.

AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges.

AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.

SAPHO syndrome is an inflammatory disorder with bone and cutaneous manifestations, for which whole-body bone scintigraphy (WBBS) is frequently used in diagnosis. The WBBS findings of SAPHO syndromes and secondary bone tumors (SBT) have overlapping features, posing diagnostic challenges. In this multicenter study, we aim to identify different bone and joint involvement patterns between the two disease entities through multiple methods to build machine-learning models and explore interpretable variables. The study included 1,193 patients, of which 593 were diagnosed with SAPHO syndrome and 600 with SBT. LASSO regression, logistic regression, and random forest techniques were applied in the training set to identify significant risk factors. Manual management and other methods were evaluated in the validation set to identify the top-performing model and the most interpretable terms. The study developed a model using 15 manually selected terms and multiple machine learning techniques, which demonstrated high diagnostic accuracy in the G1 dataset for (training AUC 0.934, testing AUC 0.929, accuracy = 88.3%, precision = 88.7%, Recall = 88.3%, F1 score = 0.882). The model was compared with logistic regression and random forest models and showed consistent results in the G2 dataset for external validation (AUC 0.957, Youden index = 0.806, sensitivity = 0.820, specificity = 0.986). The pelvis, femur, and ribs (excluding anterior ribs 1st-5th) and thoracic vertebrae 1st-8th were significant predictors of SBT, whereas the sacroiliac joints, sternum, foot, anterior ribs 1st-5th, and clavicle were indicative of SAPHO. This study assesses the effectiveness of WBBS terms in identifying SBT from SAPHO syndrome and utilizes machine learning to help screen features for patients. The final model demonstrates its dependability, providing a valuable tool for accurate and timely diagnosis.

Weibo, one of the most widely used social media platforms in China, sees a vast number of users expressing their opinions and emotional tendencies. Conducting sentiment analysis on Weibo posts using natural language processing techniques is crucial for market research and public opinion observation, holding significant commercial and societal importance. However, Chinese expression is highly diverse, making sentiment polarity harder to discern. Traditional sentiment classification algorithms often struggle with insufficient semantic feature extraction and coarse-grained in sentiment classification for Chinese texts. To address these challenges, this paper proposes a Chinese sentiment multi-classification method based on ERNIE-MCBMA. The proposed model extracts the parallel local dependency features between words through the multi-channel CNN convolutional layer and then uses the collaborative architecture of bidirectional LSTM and multi-head attention mechanism to realize context-sensitive feature recalibration. Finally, the shallow syntactic features and deep semantic representation were fused through the cross-layer feature fusion layer to realize the complementary enhancement of more fine-grained semantic information. The SMP2020-EWECT public dataset is used to categorize texts into six classes: neutral, happy, angry, sad, fear, and surprise. Various comparative experiments were conducted on the dataset. The experimental results show that the ERNIE-MCBMA achieves an accuracy of 78.26% and an F1-score of 78.45% for the 6-class classification task, outperforming other baseline models.

Reliable and secure wireless communication is essential in Industry 4.0. This work presents an anomaly detection framework using Digital Twin (DT) technology to simulate and monitor dynamic radio environments. By modeling network conditions and attack scenarios, the DT enables accurate identification of anomalies, particularly security threats. This study integrates machine learning with anomaly detection frameworks to enhance wireless network security. The proposed approach creates a virtual representation of the wireless environment, enabling accurate identification of anomalies and security threats. To validate the effectiveness of this framework, multiple machine learning algorithms based on traditional classifiers which are compared for their ability to detect anomalies, particularly jamming attacks. XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81). These results highlight XGBoost as a reliable solution for wireless network security. This work contributes to ongoing research on the integration of DT for comprehensive wireless network monitoring, emphasizing their potential to improve anomaly detection and resilience in next-generation communication systems.

Hepatitis B virus (HBV) can be transmitted from mother to child either through transplacental infection or via blood-to-blood contact during or immediately after delivery. Early and accurate risk assessments are essential for guiding clinical decisions and implementing effective preventive measures. Data mining techniques are powerful tools for identifying key predictors in medical diagnostics.

This study aims to develop a robust predictive model for mother-to-child transmission (MTCT) of HBV using decision tree algorithms, specifically Iterative Dichotomiser 3 (ID3) and classification and regression trees (CART). The study identifies clinically and paraclinically relevant predictors, particularly hepatitis B e antigen (HBeAg) status and peripheral blood mononuclear cell (PBMC) concentration, for effective risk stratification and prevention. Additionally, we will assess the model's reliability and generalizability through cross-validation with various training-test split ratios, aiming to enhance its applicability in clinical settings and inform improved preventive strategies against HBV MTCT.

This study used decision tree algorithms-ID3 and CART-on a data set of 60 hepatitis B surface antigen (HBsAg)-positive pregnant women. Samples were collected either before or at the time of delivery, enabling the inclusion of patients who were undiagnosed or had limited access to treatment. We analyzed both clinical and paraclinical parameters, with a particular focus on HBeAg status and PBMC concentration. Additional biochemical markers were evaluated for their potential contributory or inhibitory effects on MTCT risk. The predictive models were validated using multiple training-test split ratios to ensure robustness and generalizability.

Our analysis showed that 20 out of 48 (based on a split ratio of 0.8 from a total of 60 cases, 42%) to 27 out of 57 (based on a split ratio of 0.95 from a total of 60 cases, 47%) training cases with HBeAg-positive status were associated with a significant risk of MTCT of HBV (χ28=21.16, P=.007, df=8). Among HBeAg-negative women, those with PBMC concentrations ≥8 × 106 cells/mL exhibited a low risk of MTCT, whereas individuals with PBMC concentrations <8 × 106 cells/mL demonstrated a negligible risk. Across all training-test split ratios, the decision tree models consistently identified HBeAg status and PBMC concentration as the most influential predictors, underscoring their robustness and critical role in MTCT risk stratification.

This study demonstrates that decision tree models are effective tools for stratifying the risk of MTCT of HBV by integrating key clinical and paraclinical markers. Among these, HBeAg status and PBMC concentration emerged as the most critical predictors. While the analysis focused on untreated patients, it provides a strong foundation for future investigations involving treated populations. These findings offer actionable insights to support the development of more targeted and effective HBV MTCT prevention strategies.

Psoriasis is an immune-mediated skin disease where Th17 cell differentiation and IL-17 secretion play critical roles. This study investigates key exosomal ncRNAs regulating the Th17/IL-17 axis in psoriasis and their mechanisms.

We integrated bulk RNA sequencing datasets from the GEO database to construct and evaluate exosome-related patterns. Subsequently, exosome-related ncRNAs in psoriasis lesions were identified primarily through weighted gene co-expression network analysis and five machine learning algorithms. Additionally, large-scale integrated single-cell RNA sequencing data and genome-wide association study (GWAS) data were included to investigate the mechanisms of key ncRNA, primarily through immune infiltration analysis, gene set enrichment analysis (GSEA), co-expression analysis, and Mendelian randomization. Finally, the mechanisms of key ncRNA were confirmed primarily through cell co-culture and lentiviral transfection, assessed by immunofluorescence, qRT-PCR, and Western blot.

We identified 10 exosome-related ncRNAs, including PRKCQ-AS1, and constructed five machine learning models with excellent diagnostic performance, emphasizing PRKCQ-AS1's significance. Mendelian randomization demonstrated a causal relationship between PRKCQ-AS1 and psoriasis. Immune infiltration analysis and GSEA indicated that PRKCQ-AS1 influences the infiltration pattern of CD4+T cells, promotes Th17 differentiation, and is related to STAT3. The expression distribution in single-cell RNA sequencing data suggested that exosomal PRKCQ-AS1 may originate from keratinocytes, and co-expression analysis supported its role in STAT3 activation within lymphocytes. Co-culture experiments confirmed that keratinocytes in psoriasis models, as well as keratinocytes overexpressing PRKCQ-AS1, can upregulate PRKCQ-AS1 levels in CD4+T cells via exosomes, promoting Th17 cell differentiation and IL-17 secretion. Consistent results and STAT3 signaling pathway activation were detected in CD4+T cells overexpressing PRKCQ-AS1.

PRKCQ-AS1 is an exosomal lncRNA from keratinocytes in psoriasis, promoting Th17 differentiation and IL-17 secretion through STAT3 activation. This finding deepens the understanding of psoriasis pathogenesis and provides a basis for targeted therapies.

An advanced Artificial Intelligence (AI) model that leverages cutting-edge computer vision techniques to analyse embryo images and clinical data, enabling accurate prediction of clinical pregnancy outcomes in single embryo transfer procedures. Three AI models were developed, trained, and tested using a database comprised of a total of 1503 international treatment cycles (Thailand, Malaysia, and India): 1) A Clinical Multi-Layer Perceptron (MLP) for patient clinical data. 2) An Image Convolutional Neural Network (CNN) AI model using blastocyst images. 3) A fused model using a combination of both models. All three models were evaluated against their ability to predict clinical pregnancy and live birth. Each of the models were further assessed through a visualisation process where the importance of each data point clarified which clinical and embryonic features contributed the most to the prediction. The MLP model achieved a strong performance of 81.76% accuracy, 90% average precision and 0.91 AUC (Area Under the Curve). The CNN model achieved a performance of 66.89% accuracy, 74% average precision and 0.73 AUC. The Fusion model achieved 82.42% accuracy, 91% average precision and 0.91 AUC. From the visualisation process we found that female and male age to be the most clinical factors, whilst Trophectoderm to be the most important blastocyst feature. There is a gap in performance between the Clinical and Images model, which is expected due to the difficulty in predicting clinical pregnancy from just the blastocyst images. However, the Fusion AI model made more informed predictions, achieving better performance than separate models alone. This study demonstrates that AI for IVF application can increase prediction performance by integrating blastocyst images with patient clinical information.

Digital health technologies (DHTs) have rapidly advanced in the past two decades, through developments in mobile and wearable devices and most recently with the explosion of artificial intelligence (AI) capabilities and subsequent extension into the health space. DHT has myriad potential applications to deglutology, many of which have undergone promising investigations and developments in recent years. We present the first literature review on applications of DHT in swallowing health, from screening to therapeutics. Public health interventions for swallowing care are increasingly needed in the setting of aging populations in the West and East Asia, and DHT may offer a scalable and low-cost solution.

A narrative review was performed using PubMed and Google Scholar to identify recent research on applications of AI and digital health in swallow practice. Database searches, conducted in September 2024, included terms such as "digital," "AI," "machine learning," "tools" in combination with "deglutition," "Otolaryngology," "Head and Neck," "speech language pathology," "swallow," and "dysphagia." Primary literature pertaining to digital health in deglutology was included for review.

We review the various applications of DHT in swallowing care, including prevention, screening, diagnosis, treatment planning and rehabilitation.

DHT may offer innovative and scalable solutions for swallowing care as public health needs grow and in the setting of limited specialized healthcare workforce. These technological advances are also being explored as time and resource saving solutions at many points of care in swallow practice. DHT could bring affordable and accurate information for self-management of dysphagia to broader patient populations that otherwise lack access to expert providers.

Motors, as the core carriers of pollution-free power, realize efficient electric energy conversion in clean energy systems such as electric vehicles and wind power generation, and are widely used in industrial automation, smart home appliances, and rail transit fields with their low-noise and zero-emission operating characteristics, significantly reducing the dependence on fossil energy. As the requirements of various application scenarios become increasingly complex, it becomes particularly important to accurately and quickly design the sealing structure of motors. However, traditional design methods show many limitations when facing such challenges. To solve this problem, this paper proposes hybrid models of machine learning that contain polynomial regression and optimization XGBOOST models to rapidly and accurately predict the sealing performance of motors. Then, the hybrid model is combined with the simulated annealing algorithm and multi-objective particle swarm optimization algorithm for optimization. The reliability of the results is verified by the mutual verification of the results of the simulated annealing algorithm and the particle swarm optimization algorithm. The prediction accuracy of the hybrid model for data outside the training set is within 2.881%. Regarding the prediction speed of this model, the computing time of ML is less than 1 s, while the computing time of FEA is approximately 9 h, with an efficiency improvement of 32,400 times. Through the cross-validation of single-objective optimization and multi-objective optimization algorithms, the optimal design scheme is a groove depth of 0.8-0.85 mm and a pre-tightening force of 80 N. The new method proposed in this paper solves the limitations in the design of motor sealing structures, and this method can be extended to other fields for application.

Clinical Document Classification (CDC) is crucial in healthcare for organizing and categorizing large volumes of medical information, leading to improved patient care, streamlined research, and enhanced administrative efficiency. With the advancement of artificial intelligence, automatic CDC is now achievable through deep learning techniques. While existing research has shown promising results, more effective and accurate classification of long clinical documents is still desired. To address this, we propose a new model called the Enhanced Effective Convolutional Attention Network (EECAN), which incorporates a Squeeze-and-Excitation (SE) Inception module to improve feature representation by adaptively recalibrating channel-wise feature responses. This architecture introduces an Encoder and Attention-Based Clinical Document Classification (EAB-CDC) strategy, which utilizes sum-pooling and multi-layer attention mechanisms to extract salient features from clinical document representations. This study proposes EECAN (Enhanced Effective Convolutional Attention Network) as the overall model architecture and EAB-CDC (Encoder and Attention-Based Clinical Document Classification) as a core strategy conducted in EECAN. EAB-CDC is not a standalone model but a functional part applied to the architecture for discriminative feature extraction by sum-pooling and multi-layer attention mechanisms. With this integrated design, EECAN can transform multi-label clinical texts' general and label-specific contexts without losing information. Our empirical study, conducted on benchmark datasets such as MIMIC-III and MIMIC-III-50, demonstrates that the proposed EECAN model outperforms several existing deep learning approaches, achieving AUC scores of 99.70% and 99.80% using sum-pooling and multi-layer attention, respectively. These results highlight the model's substantial potential for integration into clinical systems, such as Electronic Health Record (EHR) platforms, for the automated classification of clinical texts and improved healthcare decision-making support.

This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular, it is especially helpful for managing cardiovascular disease, tracking stress, improving athletic performance, and providing health care. The device was tested with several machine learning models, such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and Stochastic Gradient Descent (SGD) to identify irregular heart rhythms. Using the SVM model, the system detects problems with an average accuracy of 98%. In the future, MyWear-designed as a wearable T-shirt-will seamlessly integrate with mobile applications for real-time data visualization, enhancing patient outcomes and fostering greater user engagement.

The capabilities of large language models (LLMs) to self-assess their own confidence in answering questions within the biomedical realm remain underexplored.

This study evaluates the confidence levels of 12 LLMs across 5 medical specialties to assess LLMs' ability to accurately judge their own responses.

We used 1965 multiple-choice questions that assessed clinical knowledge in the following areas: internal medicine, obstetrics and gynecology, psychiatry, pediatrics, and general surgery. Models were prompted to provide answers and to also provide their confidence for the correct answers (score: range 0%-100%). We calculated the correlation between each model's mean confidence score for correct answers and the overall accuracy of each model across all questions. The confidence scores for correct and incorrect answers were also analyzed to determine the mean difference in confidence, using 2-sample, 2-tailed t tests.

The correlation between the mean confidence scores for correct answers and model accuracy was inverse and statistically significant (r=-0.40; P=.001), indicating that worse-performing models exhibited paradoxically higher confidence. For instance, a top-performing model-GPT-4o-had a mean accuracy of 74% (SD 9.4%), with a mean confidence of 63% (SD 8.3%), whereas a low-performing model-Qwen2-7B-showed a mean accuracy of 46% (SD 10.5%) but a mean confidence of 76% (SD 11.7%). The mean difference in confidence between correct and incorrect responses was low for all models, ranging from 0.6% to 5.4%, with GPT-4o having the highest mean difference (5.4%, SD 2.3%; P=.003).

Better-performing LLMs show more aligned overall confidence levels. However, even the most accurate models still show minimal variation in confidence between right and wrong answers. This may limit their safe use in clinical settings. Addressing overconfidence could involve refining calibration methods, performing domain-specific fine-tuning, and involving human oversight when decisions carry high risks. Further research is needed to improve these strategies before broader clinical adoption of LLMs.

In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques to enhance the process of prioritising patients.

Developing a prediction model.

Our data were retrieved from the United Network for Organ Sharing (UNOS) open-source database of transplant patients between 2005 and 2023.

After the preprocessing process, a feature engineering technique was employed to select the most relevant features. Then, six ML models with optimised hyperparameters including multiple linear regression, random forest regressor (RF), support vector machine regressor, XGBoost regressor, a multilayer perceptron model and a deep learning model were developed based on the UNOS dataset.

The performance of each model was evaluated using R-squared (R2) and other error rate metrics. Next, the Shapley Additive Explanations (SHAP) technique was used to identify the most important features in the prediction.

The raw dataset contains 196 270 records with 545 features in all organs. After preprocessing, 32 966 records with 15 features remain. Among various models, the RF model achieved a high R2 score. Additionally, the RF model exhibited the lowest error values, indicating its superior precision compared with other regression models. The SHAP technique in conjunction with the RF model revealed the 11 most important features for priority allocation. Subsequently, we developed a web-based decision support tool using Python and the Streamlit framework based on the best-fine-tuned model.

The deployment of the ML model has the potential to act as an automated tool to aid physicians in assessing the priority of lung transplants and identifying significant factors that play a role in patient survival.

This study addresses the challenge of optimizing seal structure design through a novel two-stage interpretable optimization framework. Focusing on O-ring waterproof performance under hyperelastic material behavior, this study proposes a double-layer optimization method integrating explainable machine learning with hierarchical clustering algorithms. The key innovation lies in employing modified hierarchical clustering to categorize design parameters into two interpretable groups: bolt preload and groove depth. This clustering enables dimensionality reduction while maintaining the physical interpretability of critical parameters. In the first layer, systematic parameter screening and optimization are applied to the preload variable to reduce the database, with six remaining data points that constitute one-seventh of the original data. The second layer subsequently refines configurations using E-TOPSIS (Entropy Weight-Technique for Order Preference by Similarity to Ideal Solution) optimization. All evaluations are performed through FEA (finite element analysis) considering nonlinear material responses. The optimal design is a groove depth of 0.8 mm and a preload of 80 N. The experimental validation demonstrates that this method efficiently identifies optimal designs meeting IPX8 waterproof requirements, with zero leakage observed in both O-ring surfaces and motor interiors. The proposed methodology provides physically meaningful design guidelines.

Plant-based proteins are becoming essential resources for sustainable food systems, pharmaceutical innovations, and functional materials. This review examines the complex structure-function relationships of plant proteins, emphasising their crucial role in defining functional properties and applications. The primary structure, consisting of amino acid sequences, along with secondary, tertiary, and quaternary structures, profoundly affects protein behaviour. External factors such as pH, ionic strength, temperature, and processing techniques like extrusion and enzymatic modification can influence protein structure, consequently modifying their functional properties. Consider rewording to "Advanced processing techniques, such as high-pressure and non-thermal methods, effectively refine protein structures while preserving their functionality.Computational modelling, employing molecular dynamics and artificial intelligence, is proposed as a revolutionary instrument for forecasting and enhancing structure-function relationships. An emerging application of plant proteins is targeted drug delivery, whose structural characteristics facilitate accurate encapsulation and release of therapeutic agents. Case studies highlight the importance of protein surface characteristics in attaining precise cellular or tissue targeting, especially for conditions related to cancer and inflammation. This review concludes by highlighting strategic avenues for harnessing the complete potential of plant proteins, placing them at the cutting edge of innovation in food science, biotechnology, and drug delivery.

Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally favorable prognosis. Accurate recurrence prediction is critical for guiding post-treatment strategies. This study aimed to enhance predictive performance by refining feature engineering and evaluating a diverse ensemble of machine learning models using the UCI DTC dataset.

Unsupervised data engineering-specifically dimensionality reduction and clustering-was used to improve feature quality. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (t-SVD) were selected based on superior clustering metrics: adjusted Rand Index (ARI > 0.55) and V-measure (> 0.45). These were integrated into classification pipelines using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Feedforward Neural Network (FNN), and Gradient Boosting (GB). Model performance was evaluated through bootstrapping on an independent test set, stratified 10-fold cross-validation (CV), and subgroup analyses. Metrics included balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision, each reported with 95% confidence intervals (CIs). SHAP analysis supported model interpretability.

The PCA-based LR pipeline achieved the best test set performance: balanced accuracy of 0.95 (95% CI: 0.90-0.99), AUC of 0.99 (95% CI: 0.97-1.00), and sensitivity of 0.94 (95% CI: 0.84-1.00). In stratified CV, it maintained strong results (balanced accuracy: 0.86; AUC: 0.97; sensitivity: 0.80), with consistent performance across clinically relevant subgroups. The t-SVD-based LR pipeline showed comparable performance on both test and CV sets. SVM and FNN pipelines also performed robustly (test AUCs > 0.99; CV AUCs > 0.96). RF and KNN had high specificity but slightly lower sensitivity (test: ~0.87; CV: 0.77-0.80). GB pipelines showed the lowest overall performance (test balanced accuracy: 0.86-0.88; CV: 0.85-0.88).

Dimensionality reduction via PCA and t-SVD significantly improved model performance, particularly for LR, SVM, FNN, RF and KNN classifiers. The PCA-based LR pipeline showed the best generalizability, supporting its potential integration into clinical decision-support tools for personalized DTC management.

Not applicable.

An effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction.

The study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent-child pairs, providing a robust dataset for examining the research questions.

In the current study, the predictors named, "mother's education" (β1: 0.423; p < 0.25), "parent's knowledge of bottle-feeding habit during sleep can cause tooth decay" (β2: -1.264; p < 0.25), "attitude towards the importance of oral health as general health" (β4: -1.052; p < 0.25) and "parent's self-reported oral pain among their children" (β5: -2.107; p < 0.25) showed significant association with ECC. For this model, the Mean Absolute Deviation (MAD) was 0.02211, Predictive Mean Squared Error (PMSE) was 0.07909, and the accuracy level was 99.98%. No significant difference was observed from the t-test between the actual values and the predicted values of the model (p > 0.05).

It has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention.

Radiomics and artificial intelligence (AI) are rapidly evolving, significantly transforming the field of medical imaging. Despite their growing adoption, these technologies remain challenging to approach due to their technical complexity. This review serves as a practical guide for early-career radiologists and researchers seeking to integrate radiomics into their studies. It provides practical insights for clinical and research applications, addressing common challenges, limitations, and future directions in the field. This work offers a structured overview of the essential steps in the radiomics workflow, focusing on concrete aspects of each step, including indicative and practical examples. It covers the main steps such as dataset definition, image acquisition and preprocessing, segmentation, feature extraction and selection, and AI model training and validation. Different methods to be considered are discussed, accompanied by summary diagrams. This review equips readers with the knowledge necessary to approach radiomics and AI in medical imaging from a hands-on research perspective.

Dental caries is one of the most prevalent chronic diseases among both children and adults, despite being largely preventable. This condition has significant negative impacts on human health and imposes a substantial economic burden. In recent years, scientists and dentists have increasingly started to utilize artificial intelligence (AI), particularly machine learning, to improve the efficiency of dental caries management. This study aims to provide an overview of the current knowledge about the AI-enabled approaches for dental caries management within the framework of personalized patient care. Generally, AI works as a promising tool that can be used by both dental professionals and patients. For dental professionals, it predicts the risk of dental caries by analyzing dental caries risk and protective factors, enabling to formulate personalized preventive measures. AI, especially those based on machine learning and deep learning, can also analyze images to detect signs of dental caries, assist in developing treatment plans, and help to make a risk assessment for pulp exposure during treatment. AI-powered tools can also be used to train dental students through simulations and virtual case studies, allowing them to practice and refine their clinical skills in a risk-free environment. Additionally, AI tracks brushing patterns and provides feedback to improve oral hygiene practices of the patients and the general population, thereby improving their understanding and compliance. This capability of AI can inform future research and the development of new strategies for dental caries management and control.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting in diverse forms such as memory loss and movement disorders, pose significant challenges due to their complex molecular mechanisms and heterogeneous patient presentations. Diagnosis often relies heavily on clinical assessments and neuroimaging, with definitive confirmation frequently requiring post-mortem autopsy. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative potential. These technologies can enable the development of non-invasive tools for early diagnosis, biomarker identification, personalized treatment strategies, patient subtyping and stratification, and disease risk prediction. This review aims to provide a starting point for researchers, both with and without clinical backgrounds, who are interested in applying ML to NDs. We will discuss available data resources for key diseases like Alzheimer's and Parkinson's, explore how ML can revolutionize neurodegenerative care, and emphasize the importance of integrating multiple high-dimensional data sources to gain deeper insights and inform effective therapeutic strategies.

Lynch syndrome (LS) screening methods include multistep molecular somatic tumor testing to distinguish likely-LS patients from sporadic cases, which can be costly and complex. Also, direct germline testing for LS for every diagnosed solid cancer patient is a challenge in resource limited settings. We developed a unique machine learning scoring model to ascertain likely-LS cases from a cohort of colorectal cancer (CRC) patients.

We used CRC patients from the cBioPortal database (TCGA studies) with complete clinicopathologic and somatic genomics data. We determined the rate of pathogenic/likely pathogenic variants in five (5) LS genes (MLH1, MSH2, MSH6, PMS2, EPCAM), and the BRAF mutations using a pre-designed bioinformatic annotation pipeline. Annovar, Intervar, Variant Effect Predictor (VEP), and OncoKB software tools were used to functionally annotate and interpret somatic variants detected. The OncoKB precision oncology knowledge base was used to provide information on the effects of the identified variants. We scored the clinicopathologic and somatic genomics data automatically using a machine learning model to discriminate between likely-LS and sporadic CRC cases. The training and testing datasets comprised of 80% and 20% of the total CRC patients, respectively. Group regularisation methods in combination with 10-fold cross-validation were performed for feature selection on the training data.

Out of 4800 CRC patients frorm the TCGA datasets with clinicopathological and somatic genomics data, we ascertained 524 patients with complete data. The scoring model using both clinicopathological and genetic characteristics for likely-LS showed a sensitivity and specificity of 100%, and both had the maximum accuracy, area under the curve (AUC) and AUC for precision-recall (AUCPR) of 1. In a similar analysis, the training and testing models that only relied on clinical or pathological characteristics had a sensitivity of 0.88 and 0.50, specificity of 0.55 and 0.51, accuracy of 0.58 and 0.51, AUC of 0.74 and 0.61, and AUCPR of 0.21 and 0.19, respectively.

Simultaneous scoring of LS clinicopathological and somatic genomics data can improve prediction and ascertainment for likely-LS from all CRC cases. This approach can increase accuracy while reducing the reliance on expensive direct germline testing for all CRC patients, making LS screening more accessible and cost-effective, especially in resource-limited settings.

Large-scale land cover changes leading to land degradation and deforestation in fragile ecosystems such as the Western Ghats have impaired ecosystem services, evident from the conversion of perennial water bodies to seasonal, which necessitates an understanding of forest structure dynamics with ecosystem services to evolve appropriate location-specific mitigation measures to arrest land degradation. The current study evaluates the extent and condition of forest ecosystems in Goa of the Central Western Ghats, a biodiversity hotspot. Land use dynamics is assessed through a supervised hierarchical classifier based on the Random Forest Machine Learning Algorithm, revealing that total forest cover declined by 3.75% during the post-1990s due to market forces associated with globalization. Likely land uses predicated through the CA-Markov-based Analytic Hierarchy Process (AHP) highlight a decline in evergreen forest cover of 10.98%. The carbon sequestration potential of forests in Goa assessed through the InVEST model highlights the storage of 56,131.16 Gg of carbon, which accounts for 373.47 billion INR (4.49 billion USD). The total ecosystem supply value (TESV) for forest ecosystems was computed by aggregating the provisioning, regulating, and cultural services, which accounts for 481.76 billion INR per year. TESV helps in accounting for the degradation cost of ecosystems towards the development of green GDP (Gross Domestic Product). Prioritization of Ecologically Sensitive Regions (ESR) considering bio-geo-climatic, ecological, and social characteristics at disaggregated levels reveals that 54.41% of the region is highly sensitive (ESR1 and ESR2). The outcome of the research offers invaluable insights for the formulation of strategic natural resource management approaches.

This study aimed to explore the relationships between gender, anthropometrics, and spinal movement patterns (SMP) during lifting and lowering tasks. Thirty adults lifted and lowered a 15 kg-box using a freestyle, squat, and stoop technique. A stepwise segmentation approach, along with the timing of main inflection points of relative angles, was used to distinguish various spinal movement patterns. Temporal multi-segmental interactions were categorised, and their frequencies were analysed based on segments and lifting techniques. SMP's demonstrated varying associations with gender and anthropometric factors during lifting and lowering phases. Notably, during stoop lifting, females tended towards a bottom-up pattern, contrasting with males' preference for a simultaneous pattern. Cluster analysis highlighted the bottom-up pattern in the thoracic spine as the most prominent discriminating factor among females. This SMP categorisation method holds potential for designing tailored manual material handling strategies and re-evaluating therapeutic and exercise programs in occupational, clinical, and sport contexts.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potential to enhance disease diagnosis through data-driven approaches relying on non-invasive techniques. However, no studies have explored the application of fully non-invasive methods for predicting thyroid-stimulating hormone (TSH) levels. Consequently, this study introduces a novel, fully non-invasive framework for predicting TSH levels by developing an innovative hybrid machine learning model that balances performance, complexity, and interpretability. Seven ML models were evaluated, and the best-performing models were integrated into a hybrid approach to balance performance, complexity, and interpretability. A dataset of 6190 instances from Jordan was used for model development. Four-dimensional non-invasive factors, including demographics, symptoms, family history, and newly engineered symptom scores, were incorporated into the model. The hybrid model achieved an R2 of 94.2 % and RMSE of 0.015, demonstrating superior predictive performance. Model interpretability was ensured using LIME and SHAP explainers, confirming aggregated symptom scores' critical role in enhancing prediction accuracy. A robust feature selection technique was implemented, reducing model complexity and enhancing performance. Among the top ten features for predicting TSH levels were hypothyroidism and hyperthyroidism symptom scores, family history, cold intolerance, itchy-dry skin, sweating, hand tremors, and palpitations. The model can be employed to develop cost-effective diagnostic tools for thyroid disorders. It also offers a robust framework that can be generalized to predict other biomarkers and applied in diverse contexts.

A lack of standardization exists for functional tasks in return-to-activity (RTA) guidelines for adolescents with anterior cruciate ligament injury (ACLi). Identifying the variables that discern ACLi status among adolescents is a first step in the creation of such guidelines following surgical reconstruction. This study investigated the use of classification models to discern ACLi status of adolescents with and without injury using spatiotemporal variables from functional tasks typically used in RTA guidelines for adults.

Sixty-four adolescents with ACLi and 70 uninjured adolescents completed single-limb hops, lunges, squats, countermovement jumps and drop-vertical jumps. Jumping distances, heights, and depths were collected. Decision trees (DTs) were used to classify ACLi status and were evaluated using the F-measure (F1), kappa statistic (ĸ) and area under the precision-recall curve (PRC). Independent t tests and effect sizes were calculated for each important classifier of the DT models.

A five-variable model classified ACLi status with an accuracy of 67.5% (F1 = 0.6842; ĸ = 0.350; PRC = 0.491) with sex as a classifier. Significant differences were found in three of the four spatiotemporal variables (p ≤ 0.002). Separate models then classified ACLi status in males and females with an accuracy of 53.3% (F1 = 0.5882; ĸ = 0.0541; PRC = 0.476) and 76.9% (F1 = 0.7692; ĸ = 0.541; PRC = 0.528), respectively, with significant differences for all variables (p ≤ 0.013).

Among the DT models, females were better able to classify ACLi status compared to males, highlighting the importance of sex-specific rehabilitation guidelines for adolescents.

Level III.

Biological sex estimation in forensic anthropology is a crucial topic, and the patella has shown promise in this regard due to its sexual dimorphism. This study uses 12 machine learning models for sex estimation based on three patellar measurements (maximum height, breadth, and thickness). Data was collected from 180 skeletons of a contemporary Italian population (83 males and 97 females) as well as from an independent sample of 21 forensic cases (13 males and 8 females). Statistical analyses indicated that each of the variables exhibited significant sexual dimorphism. To predict biological sex, the classifiers were built using 70% of a reference sample, then tested on the remaining 30% of the original sample and then tested again on the independent sample. The different classifiers generated accuracies varied between 0.85 and 0.91 on the reference sample and between 0.71 and 0.95 for the validation sample. SVM classifier stood out with the highest accuracy and seemed the best model for our study.This study contributes to the growing application of machine learning in forensic anthropology by being the first to apply such techniques to patellar measurements in an Italian population. It aims to enhance the accuracy and efficiency of biological sex estimation from the patella, building on promising results observed with other skeletal elements.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threatening condition and a major cause of cancer-related mortality. Patients with neutropenic sepsis are generally treated with Broad Spectrum Antibiotics (BSA) as a first-line medication that destroys common causative organisms but may either miss the true pathogen or be overly broad leading to an increased risk of development of Antimicrobial Resistance (AMR). Physicians resort to using BSA due to a typical delay of 2-5 days for specific organism identification by blood cultures. We report the development and validation of an explainable AI powered system to predict bacterial growth in blood cultures (N=110) using readily available hematological parameters, enabling predictions 2-5 days ahead of actual culture results. Our best performing models yielded an accuracy and F1 score of 78%. In predicting gram-negative bacteria (GNB), the models demonstrated an accuracy and F1 score of 63%. To our knowledge, this is the first study to explore AI-powered early prediction of bacteremia in neutropenic sepsis patients in a South Asian population.

Missed hospital appointments are common among outpatients and have significant clinical and economic consequences. The purpose of this study is to develop a predictive model of missed hospital appointments and to evaluate different overbooking strategies.

Retrospective cross-sectional analysis.

Outpatient clinics of the Royal Hospital in Muscat, Oman.

All outpatient clinic appointments scheduled between January 2014 and February 2021 (n=947 364).

Predictive models were created using logistic regression for the entire cohort and individual practices to predict missed hospital appointments. The performance of the models was evaluated using a holdout set. Simulations were performed to compare the effectiveness of predictive model-based overbooking and organisational overbooking in optimising appointment utilisation.

Of the 947 364 outpatient appointments booked, 201 877 (21.3%) were missed. The proportion of missed appointments varied by clinic, ranging from 13.8% in oncology to 28.3% in urology. The area under the receiver operating characteristic curve (AUC) for the overall predictive model was 0.771 (95% CI: 0.768 to 0.775), while the AUC for the clinic-specific predictive model was 0.845 (95% CI: 0.836 to 0.855) for oncology and 0.738 (95% CI: 0.732 to 0.744) for paediatrics. The overbooking strategy based on the predictive model outperformed systematic overbooking, with shortages of available appointments at 10.4% in oncology and 25.0% in gastroenterology.

Predictive models can effectively estimate the probability of missing a hospital appointment with high accuracy. Using these models to guide overbooking strategies can enable better appointment scheduling without burdening clinics and reduce the impact of missed appointments.

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

Understanding of complex biological systems remains a significant challenge due to their high dimensionality, nonlinearity, and context-specific behavior. Artificial intelligence (AI) and mechanistic modeling are becoming essential tools for studying such complex systems. Mechanistic modeling can facilitate the construction of simulatable models that are interpretable but often struggle with scalability and parameters estimation. AI can integrate multi-omics data to create predictive models, but it lacks interpretability. The gap between these two modeling methods limits our ability to develop comprehensive and predictive models for biomedical applications. This article reviews the most recent advancements in the integration of AI and mechanistic modeling to fill this gap. Recently, with omics availability, AI has led to new discoveries in mechanistic computational modeling. The mechanistic models can also help in getting insight into the mechanism for prediction made by AI models. This integration is helpful in modeling complex systems, estimating the parameters that are hard to capture in experiments, and creating surrogate models to reduce computational costs because of expensive mechanistic model simulations. This article focuses on advancements in mechanistic computational models and AI models and their integration for scientific discoveries in biology, pharmacology, drug discovery and diseases. The mechanistic models with AI integration can facilitate biological discoveries to advance our understanding of disease mechanisms, drug development, and personalized medicine. The article also highlights the role of AI and mechanistic model integration in the development of more advanced models in the biomedical domain, such as medical digital twins and virtual patients for pharmacological discoveries.

The worldwide ongoing trend of synthetic drug production is also of increasing concern due to enormous amounts of chemical waste produced in clandestine laboratories. Typically, several tons of different types of production waste are stored in numerous containers and need to be characterized after dismantling a laboratory to assess production features, e.g., synthesis route and production scale, and to draw conclusions on the minimum number of batches produced. This forensic assessment is commonly done by a rather laborious gas chromatography - mass spectrometry approach. The aim of this work is to evaluate the suitability of the SICRIT (soft ionization by chemical reaction in transfer) ion source, which is based on the dielectric barrier discharge ionization principle, combined with high-resolution mass spectrometry (HRMS), for the rapid classification of liquid samples from amphetamine production in a seized large-scale clandestine drug laboratory. Among the different sample introduction methods tested, headspace analysis directly into the SICRIT ion source in conjunction with a heated inlet proved to be optimal. Identification of expected target substances (reaction educts, intermediates, byproducts, products) was possible as well as grouping related samples and assigning them to specific synthesis steps by multivariate data analysis in an unsupervised approach. In addition, supervised machine learning algorithms were evaluated to obtain a classification model for the assessment of production waste samples from one dismantled synthetic drug laboratory, and a random forest classifier showed the best performance with an accuracy of 97%. The potential of the novel SICRIT-HRMS approach for the assessment of synthetic drug laboratories was demonstrated.

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate. Different techniques are available to address the class imbalance in classification models and can be categorized as data-level, algorithm-level, and hybrid methods. But to the best of our knowledge, an in-depth analysis of the performance of these techniques against the class ratio is not available in the literature. We have addressed these shortcomings in this study and have performed a detailed analysis of the performance of four different techniques to address imbalanced class distribution using machine learning (ML) methods and AutoML tools. To carry out our study, we have selected four such techniques─(a) threshold optimization using (i) GHOST and (ii) the area under the precision-recall curve (AUPR) curve, (b) internal balancing method of AutoML and class-weight of machine learning methods, and (c) data balancing using SMOTETomek─and generated 27 data sets considering nine different class ratios (i.e., the ratio of the positive class and total samples) from three data sets that belong to the drug discovery and development field. We have employed random forest (RF) and support vector machine (SVM) as representatives of ML classifier and AutoGluon-Tabular (version 0.6.1) and H2O AutoML (version 3.40.0.4) as representatives of AutoML tools. The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. In summary, exploration of multiple data balancing techniques is recommended for classifying imbalanced data sets to achieve optimal performance as neither of the external techniques nor the internal techniques outperform others significantly. The results are specific to the ML methods and AutoML libraries used in this study, and for generalization, a study can be carried out considering a sizable number of ML methods and AutoML libraries.

Organophosphorus pesticides (OPPs) are widely used in agriculture but pose significant ecological and human health risks due to their persistence and toxicity in the environment. While microbial degradation offers a promising solution, gaps remain in understanding the enzymatic mechanisms, degradation pathways, and ecological impacts of OPP transformation products. This review aims to bridge these gaps by integrating traditional microbial degradation research with emerging machine learning (ML) technologies. We hypothesize that ML can enhance OPP degradation studies by improving the efficiency of enzyme discovery, pathway prediction, and ecological risk assessment. Through a comprehensive analysis of microbial degradation mechanisms, environmental factors, and ML applications, we propose a novel framework that combines biochemical insights with data-driven approaches. Our review highlights the potential of ML to optimize microbial strain screening, predict degradation pathways, and identify key active sites, offering innovative strategies for sustainable pesticide management. By integrating traditional research with cutting-edge ML technologies, this work contributes to the journal's scope by promoting eco-friendly solutions for environmental protection and pesticide pollution control.

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.