UNASSIGNED: Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy for hematological malignancies, it can be associated with relevant post-transplant complications. Several reports have shown that polymorphisms in immune system genes are correlated with the development of post-transplant complications. Within this context, this work focuses on identifying novel polymorphisms in cytokine genes and developing predictive models to anticipate the risk of developing graft-versus-host disease (GVHD), transplantation-related mortality (TRM), relapse and overall survival (OS).
UNASSIGNED: Our group developed a 132-cytokine gene panel which was tested in 90 patients who underwent an HLA-identical sibling-donor allo-HSCT. Bayesian logistic regression (BLR) models were used to select the most relevant variables. Based on the cut-off points selected for each model, patients were classified as being at high or low-risk for each of the post-transplant complications (aGVHD II-IV, aGVHD III-IV, cGVHD, mod-sev cGVHD, TRM, relapse and OS).
UNASSIGNED: A total of 737 polymorphisms were selected from the custom panel genes. Of these, 41 polymorphisms were included in the predictive models in 30 cytokine genes were selected (17 interleukins and 13 chemokines). Of these polymorphisms, 5 (12.2%) were located in coding regions, and 36 (87.8%) in non-coding regions. All models had a statistical significance of p<0.0001.
UNASSIGNED: Overall, genomic polymorphisms in cytokine genes make it possible to anticipate the development all complications studied following allo-HSCT and, consequently, to optimize the clinical management of patients.

Acute hyperglycemia is a common endocrine and metabolic disorder that seriously threatens the health and life of patients. Exploring effective diagnostic methods and treatment strategies for acute hyperglycemia to improve treatment quality and patient satisfaction is currently one of the hotspots and difficulties in medical research. This article introduced a method for diagnosing acute hyperglycemia based on data-driven prediction models. In the experiment, clinical data from 1000 patients with acute hyperglycemia were collected. Through data cleaning and feature engineering, 10 features related to acute hyperglycemia were selected, including BMI (Body Mass Index), TG (triacylglycerol), HDL-C (High-density lipoprotein cholesterol), etc. The support vector machine (SVM) model was used for training and testing. The experimental results showed that the SVM model can effectively predict the occurrence of acute hyperglycemia, with an average accuracy of 96 %, a recall rate of 84 %, and an F1 value of 89 %. The diagnostic method for acute hyperglycemia based on data-driven prediction models has a certain reference value, which can be used as a clinical auxiliary diagnostic tool to improve the early diagnosis and treatment success rate of acute hyperglycemia patients.

BACKGROUND: The global burden of the opportunistic fungal disease Pneumocystis jirovecii pneumonia (PJP) remains substantial. Polymerase chain reaction (PCR) on nasopharyngeal swabs (NPS) has high specificity and may be a viable alternative to the gold standard diagnostic of PCR on invasively collected lower respiratory tract specimens, but has low sensitivity. Sensitivity may be improved by incorporating NPS PCR results into machine learning models.
METHODS: Three supervised multivariable diagnostic models (random forest, logistic regression and extreme gradient boosting) were constructed and validated using a 111-person Australian dataset. The predictors were age, gender, immunosuppression type and NPS PCR result. Model performance metrics such as accuracy, sensitivity, specificity and predictive values were compared to select the best-performing model.
RESULTS: The logistic regression model performed best, with 80% accuracy, improving sensitivity to 86% and maintaining acceptable specificity of 70%. Using this model, positive and negative NPS PCR results indicated post-test probabilities of 84% (likely PJP) and 26% (unlikely PJP), respectively.
CONCLUSIONS: The logistic regression model should be externally validated in a wider range of settings. As the predictors are simple, routinely collected patient variables, this model may represent a diagnostic advance suitable for settings where collection of lower respiratory tract specimens is difficult but PCR is available.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

Biochar is crucial for agricultural output and plays a significant role in effectively eliminating heavy metals (HMs) from the soil, which is essential for maintaining a soil-plant environment. This work aimed to assess machine learning models to analyze the impact of soil parameters on the transformation of HMs in biochar-soil-plant environments, considering the intricate non-linear relationships involved. A total of 211 datasets from pot or field experiments were evaluated. Fourteen factors were taken into account to assess the efficiency and bioavailability of HM-biochar amendment immobilization. Four predictive models, namely linear regression (LR), partial least squares (PLS), support vector regression (SVR), and random forest (RF), were compared to predict the immobilization efficiency of biochar-HM. The findings revealed that the RF model was created using 5-fold cross-validation, which exhibited a more reliable prediction performance. The results indicated that soil features accounted for 79.7% of the absorption of HM by crops, followed by biochar properties at 17.1% and crop properties at 3.2%. The main elements that influenced the result have been determined as the characteristics of the soil (including the presence of different HM species and the amount of clay) and the quantity and attributes of the biochar (such as the temperature at which it was produced by pyrolysis). Furthermore, the RF model was further developed to predict bioaccumulation factors (BAF) and variations in crop uptake (CCU). The R2 values were found to be 0.7338 and 0.6997, respectively. Thus, machine learning (ML) models could be useful in understanding the behavior of HMs in soil-plant ecosystems by employing biochar additions.

OBJECTIVE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resonance spectroscopy (WB-MRS) protocol that delves into the intricacies of tumor microenvironments, offering a comprehensive understanding of glioma progression to inform expectant surgical and adjuvant intervention.
METHODS: We investigated five locoregional tumor metabolites in a post-treatment population and applied machine learning (ML) techniques to analyze key relationships within seven regions of interest: contralateral normal-appearing white matter (NAWM), fluid-attenuated inversion recovery (FLAIR), contrast-enhancing tumor at time of WB-MRS (Tumor), areas of future recurrence (AFR), whole-brain healthy (WBH), non-progressive FLAIR (NPF), and progressive FLAIR (PF). Five supervised ML classification models and a neural network were developed, optimized, trained, tested, and validated. Lastly, a web application was developed to host our novel calculator, the Miami Glioma Prediction Map (MGPM), for open-source interaction.
RESULTS: Sixteen patients with histopathological confirmation of high-grade glioma prior to WB-MRS were included in this study, totaling 118,922 whole-brain voxels. ML models successfully differentiated normal-appearing white matter from tumor and future progression. Notably, the highest performing ML model predicted glioma progression within fluid-attenuated inversion recovery (FLAIR) signal in the post-treatment setting (mean AUC = 0.86), with Cho/Cr as the most important feature.
CONCLUSIONS: This study marks a significant milestone as the first of its kind to unveil radiographic occult glioma progression in post-treatment gliomas within 8 months of discovery. These findings underscore the utility of ML-based WB-MRS growth predictions, presenting a promising avenue for the guidance of early treatment decision-making. This research represents a crucial advancement in predicting the timing and location of glioblastoma recurrence, which can inform treatment decisions to improve patient outcomes.

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes.

The worldwide health crisis triggered by the novel coronavirus (COVID-19) epidemic has resulted in an extensive variety of symptoms in people who have been infected, the most prevalent disorders of which are loss of smell and taste senses. In some patients, these disorders might occasionally last for several months and can strongly affect patients\' quality of life. The COVID-19-related loss of taste and smell does not presently have a particular therapy. However, with the help of an early prediction of these disorders, healthcare providers can direct the patients to control these symptoms and prevent complications by following special procedures. The purpose of this research is to develop a machine learning (ML) model that can predict the occurrence and persistence of post-COVID-19-related loss of smell and taste abnormalities. In this study, we used our dataset to describe the symptoms, functioning, and disability of 413 verified COVID-19 patients. In order to prepare accurate classification models, we combined several ML algorithms, including logistic regression, k-nearest neighbors, support vector machine, random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The accuracy of the loss of taste model was 91.5 % with an area-under-cure (AUC) of 0.94, and the accuracy of the loss of smell model was 95 % with an AUC of 0.97. Our proposed modelling framework can be utilized by hospitals experts to assess these post-COVID-19 disorders in the early stages, which supports the development of treatment strategies.

BACKGROUND: With the rapid evolution of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT-4 (OpenAI), there is an increasing interest in their potential to assist in scholarly tasks, including conducting literature reviews. However, the efficacy of AI-generated reviews compared with traditional human-led approaches remains underexplored.
OBJECTIVE: This study aims to compare the quality of literature reviews conducted by the ChatGPT-4 model with those conducted by human researchers, focusing on the relational dynamics between physicians and patients.
METHODS: We included 2 literature reviews in the study on the same topic, namely, exploring factors affecting relational dynamics between physicians and patients in medicolegal contexts. One review used GPT-4, last updated in September 2021, and the other was conducted by human researchers. The human review involved a comprehensive literature search using medical subject headings and keywords in Ovid MEDLINE, followed by a thematic analysis of the literature to synthesize information from selected articles. The AI-generated review used a new prompt engineering approach, using iterative and sequential prompts to generate results. Comparative analysis was based on qualitative measures such as accuracy, response time, consistency, breadth and depth of knowledge, contextual understanding, and transparency.
RESULTS: GPT-4 produced an extensive list of relational factors rapidly. The AI model demonstrated an impressive breadth of knowledge but exhibited limitations in in-depth and contextual understanding, occasionally producing irrelevant or incorrect information. In comparison, human researchers provided a more nuanced and contextually relevant review. The comparative analysis assessed the reviews based on criteria including accuracy, response time, consistency, breadth and depth of knowledge, contextual understanding, and transparency. While GPT-4 showed advantages in response time and breadth of knowledge, human-led reviews excelled in accuracy, depth of knowledge, and contextual understanding.
CONCLUSIONS: The study suggests that GPT-4, with structured prompt engineering, can be a valuable tool for conducting preliminary literature reviews by providing a broad overview of topics quickly. However, its limitations necessitate careful expert evaluation and refinement, making it an assistant rather than a substitute for human expertise in comprehensive literature reviews. Moreover, this research highlights the potential and limitations of using AI tools like GPT-4 in academic research, particularly in the fields of health services and medical research. It underscores the necessity of combining AI\'s rapid information retrieval capabilities with human expertise for more accurate and contextually rich scholarly outputs.

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.