Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy.

CAR-T cell therapy is a revolutionary new treatment for hematological malignancies, but it can also result in significant adverse effects, with cytokine release syndrome (CRS) being the most common and potentially life-threatening. The identification of biomarkers to predict the severity of CRS is crucial to ensure the safety and efficacy of CAR-T therapy. To achieve this goal, we characterized the expression profiles of seven cytokines, four conventional biochemical markers, and five hematological markers prior to and following CAR-T cell infusion. Our results revealed that IL-2, IFN-γ, IL-6, and IL-10 are the key cytokines for predicting severe CRS (sCRS). Notably, IL-2 levels rise at an earlier stage of sCRS and have the potential to serve as the most effective cytokine for promptly detecting the condition's onset. Furthermore, combining these cytokine biomarkers with hematological factors such as lymphocyte counts can further enhance their predictive performance. Finally, a predictive tree model including lymphocyte counts, IL-2, and IL-6 achieved an accuracy of 85.11% (95% CI = 0.763-0.916) for early prediction of sCRS. The model was validated in an independent cohort and achieved an accuracy of 74.47% (95% CI = 0.597-0.861). This new prediction model has the potential to become an effective tool for assessing the risk of CRS in clinical practice.

Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.

This study investigates the use of computational frameworks for sepsis. We consider two dimensions for investigation - early diagnosis of sepsis (EDS) and mortality prediction rate for sepsis patients (MPS). We concentrate on the clinical parameters on which sepsis diagnosis and prognosis are currently done, including customized treatment plans based on historical data of the patient. We identify the most notable literature that uses computational models to address EDS and MPS based on those clinical parameters. In addition to the review of the computational models built upon the clinical parameters, we also provide details regarding the popular publicly available data sources. We provide brief reviews for each model in terms of prior art and present an analysis of their results, as claimed by the respective authors. With respect to the use of machine learning models, we have provided avenues for model analysis in terms of model selection, model validation, model interpretation, and model comparison. We further present the challenges and limitations of the use of computational models, providing future research directions. This study intends to serve as a benchmark for first-hand impressions on the use of computational models for EDS and MPS of sepsis, along with the details regarding which model has been the most promising to date. We have provided details regarding all the ML models that have been used to date for EDS and MPS of sepsis.

Thermographic assessment of mastitis progression in sahiwal cattle: Insights into the patterns in the natural course of infection.

Mastitis is a global concern in the dairy sector, demanding innovative solutions for effective management for quality lifetime milk production. In this study, infrared thermography (IRT) as a non-invasive technology was integrated into routine farm activities for continuous health monitoring of animals. For 30 days, we systematically monitored the udder health status in 40 Sahiwal cows (160 quarters), employing IRT along with the California Mastitis Test (CMT). We also assessed somatic cell count (SCC), microbial identification, and milk quality parameters of representative samples. The thermal imaging data was analyzed, considering both backward propagation from the 0th day to the -10th day and forward propagation from the 0th day to the +10th day. Our findings revealed that on the 0th day, the mean temperatures of the udder surface skin temperature (USST) and teat skin surface temperature (TSST) exhibited differences (p < 0.05) between the quarters affected by sub-clinical mastitis (SCM) and clinical mastitis (CM) in comparison to the healthy quarters, with the highest degree of difference observed. The observed temperature differences between CM and SCM quarters compared to healthy ranged from 1.8 to 3.62 °C and 0.98 to 3.23 °C for USST, and from 1.68 to 3.16 °C and 0.56 to 2.32 °C for TSST, respectively. Furthermore, our observations indicated that both udder and teat quarters responded differently to mastitis. A temperature rise of 1.37 °C in SCM quarters and 1.75 °C in CM quarters was observed between the -10th and -8th day relative to day 0, with the increase being more pronounced in the morning hours. Also, a notable temperature surge occurred during the -2nd and -1st days relative to the 0th day. The log10SCC values and milk quality parameters significantly differed (p < 0.05) between mastitis-affected and healthy samples. In addition, Staphylococcus spp. was identified as the predominant mastitis-causing pathogen in the bacteriological identification conducted in this study. Therefore, IRT efficiently assesses the initiation point of udder infection in Sahiwal cows, aiding in effective udder health management.

A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images.

Background: Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC). Methods: A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan-Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers. Findings: The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752-0.849), 0.748 (95% CI, 0.660-0.830), 0.788 (95% CI, 0.735-0.835), and 0.766 (95% CI, 0.717-0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P < 0.001; Center 2:HR, 1.776, P = 0.013]. Interpretation: The transformer-based DLN provides early and effective prediction of LNM and survival outcomes in LAGC patients receiving NAC, with promise to guide individualized therapy. Future prospective multicenter studies are warranted to further validate our model. Funding: National Natural Science Foundation of China (NO. 82373432, 82171923, 82202142), Project Funded by China Postdoctoral Science Foundation (NO. 2022M720857), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (NO. U22A20345), National Science Fund for Distinguished Young Scholars of China (NO. 81925023), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (NO. 2022B1212010011), High-level Hospital Construction Project (NO. DFJHBF202105), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (NO. 2024B1515020091).

A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis.

BACKGROUND: This study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients. METHODS: Eligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA). RESULTS: A total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities. CONCLUSIONS: The dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.

A Review of Stage 0 Biomarkers in Type 1 Diabetes: The Holy Grail of Early Detection and Prevention?

Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease characterized by the destruction of pancreatic islet cells, resulting in lifelong dependency on insulin treatment. There is an abundance of review articles addressing the prediction of T1D; however, most focus on the presymptomatic phases, specifically stages 1 and 2. These stages occur after seroconversion, where therapeutic interventions primarily aim to delay the onset of T1D rather than prevent it. This raises a critical question: what happens before stage 1 in individuals who will eventually develop T1D? Is there a "stage 0" of the disease, and if so, how can we detect it to increase our chances of truly preventing T1D? In pursuit of answers to these questions, this narrative review aimed to highlight recent research in the field of early detection and prediction of T1D, specifically focusing on biomarkers that can predict T1D before the onset of islet autoimmunity. Here, we have compiled influential research from the fields of epigenetics, omics, and microbiota. These studies have identified candidate biomarkers capable of predicting seroconversion from very early stages to several months prior, suggesting that the prophylactic window begins at birth. As the therapeutic landscape evolves from treatment to delay, and ideally from delay to prevention, it is crucial to both identify and validate such "stage 0" biomarkers predictive of islet autoimmunity. In the era of precision medicine, this knowledge will enable early intervention with the potential for delaying, modifying, or completely preventing autoimmunity and T1D in at-risk children.

In perspective of specific symptoms of major depressive disorder: Functional connectivity analysis of electroencephalography and potential biomarkers of treatment response.

BACKGROUND: The symptom variability in major depressive disorder (MDD) complicates treatment assessment, necessitating a thorough understanding of MDD symptoms and potential biomarkers. METHODS: In this prospective study, we enrolled 54 MDD patients and 39 controls. Over the course of weeks 1, 2, and 4 participants underwent evaluations, with electroencephalograms (EEG) recorded at baseline and week 1. Our investigation considered five previously identified syndromal factors derived from the 17-item Hamilton Depression Rating Scale (17-item HAMD) for assessing depression: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. We assessed treatment response and EEG characteristics across all syndromal factors and total scores, all of which are based on the 17-item HAMD. To analyze the topology of brain networks, we employed functional connectivity (FC) and a graph theory-based method across various frequency bands. RESULTS: The healthy control group had notably higher values in delta band EEG FC compared to the MDD patient group. Similar distinctions were observed between the responder and non-responder patient groups. Further exploration of baseline FC values across distinct syndromal factors revealed significant variations among the core, psychomotor-insight, and anorexia subgroups when using a specific graph theory-based approach, focusing on global efficiency and average clustering coefficient. LIMITATIONS: Different antidepressants were included in this study. Therefore, the results should be interpreted with caution. CONCLUSIONS: Our findings suggest that delta band EEG FC holds promise as a valuable predictor of antidepressant efficacy. It demonstrates an ability to adapt to individual variations in depressive symptomatology, offering insights into personalized treatment for patients with depression.

Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.

OBJECTIVE: Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CTEs) in elderly patients with atrial fibrillation(AF). These events encompass newly diagnosed cerebral ischemia events, cardiovascular events, pulmonary embolism, and lower extremity arterial embolism. METHODS: This retrospective study included 6,079 elderly hospitalized patients (≥ 75 years old) with AF admitted to the People's Liberation Army General Hospital in China from January 2010 to June 2022. Random forest imputation was used for handling missing data. In the descriptive statistics section, patients were divided into two groups based on the occurrence of CTEs, and differences between the two groups were analyzed using chi-square tests for categorical variables and rank-sum tests for continuous variables. In the machine learning section, the patients were randomly divided into a training dataset (n = 4,225) and a validation dataset (n = 1,824) in a 7:3 ratio. Four machine learning models (logistic regression, decision tree, random forest, XGBoost) were trained on the training dataset and validated on the validation dataset. RESULTS: The incidence of composite thromboembolic events was 19.53%. The Least Absolute Shrinkage and Selection Operator (LASSO) method, using 5-fold cross-validation, was applied to the training dataset and identified a total of 18 features that exhibited a significant association with the occurrence of CTEs. The random forest model outperformed other models in terms of area under the curve (ACC: 0.9144, SEN: 0.7725, SPE: 0.9489, AUC: 0.927, 95% CI: 0.9105-0.9443). The random forest model also showed good clinical validity based on the clinical decision curve. The Shapley Additive exPlanations (SHAP) showed that the top five features associated with the model were history of ischemic stroke, high triglyceride (TG), high total cholesterol (TC), high plasma D-dimer, age. CONCLUSIONS: This study proposes an accurate model to stratify patients with a high risk of CTEs. The random forest model has good performance. History of ischemic stroke, age, high TG, high TC and high plasma D-Dimer may be correlated with CTEs.

Mendelian randomization supports causal effects of inflammatory biomarkers on myopic refractive errors.

AIMS: To determine whether inflammatory biomarkers are causal risk factors for more myopic refractive errors. METHODS: Northern Sweden Population Health Study (NSPHS), providing inflammatory biomarkers data; UK Biobank, providing refractive errors data. 95,619 European men and women aged 40 to 69 years with available information of refractive errors and inflammatory biomakers. Inflammatory biomarkers including ADA, CCL23, CCL25, CD6, CD40, CDCP-1, CST5, CXCL-5, CXCL-6, CXCL-10, IL-10RB, IL-12B, IL-15RA, IL-18R1, MCP-2, MMP-1, TGF-ß1, TNF-ß, TWEAK and VEGF-A were exposures, and spherical equivalent (SE) using the formula SE = sphere + (cylinder/2) was outcome. RESULTS: Mendelian randomization analyses showed that each unit increase in VEGF-A, CD6, MCP-2 were causally related to a more myopic refractive errors of 0.040 D/pg.mL-1 (95% confidence interval 0.019 to 0.062; P = 2.031 × 10-4), 0.042 D/pg.mL-1 (0.027 to 0.057; P = 7.361 × 10-8) and 0.016 D/pg.mL-1 (0.004 to 0.028; P = 0.009), and each unit increase in TWEAK was causally related to a less myopic refractive errors of 0.104 D/pg.mL-1 (-0.152 to -0.055; P = 2.878 × 10-5). Tested by the MR-Egger, weighted median, MR-PRESSO, Leave-one-out methods, our results were robust to horizontal pleiotropy and heterogeneity in VEGF-A, MCP-2, CD6, but not in TWEAK. CONCLUSIONS: Our Mendelian Randomization analysis supported the causal effects of VEGF-A, MCP-2, CD6 and TWEAK on myopic refractive errors. These findings are important for providing new indicators for early intervention of myopia to make myopic eyesight threatening consequences less inevitable.

Application of CD25 and CTLA4 gene transcription levels in early prediction of acute graft-versus-host disease.

Introduction: Our study investigated the potential of peripheral blood T cell CD25, CD28, and CTLA-4 gene transcription levels as predictive biomarkers for acute graft-versus-host disease (aGVHD) following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods: Real-time reverse transcription fluorescent quantitative PCR (RT-qPCR) analysis was conducted on day +7, +14, and +21 post-transplantation in patients undergoing allo-HSCT. Results: Elevated levels of CD25 and CTLA-4 mRNA were found to be associated with the occurrence of aGVHD, as well as severe and gastrointestinal aGVHD. Receiver operating characteristic (ROC) curve analysis was utilized to assess the predictive value of each biomarker. Combined analysis of CD25 and CTLA-4 mRNA levels demonstrated promising predictive potential for aGVHD. Conclusion: Our results confirmed that the transcription levels of CD25 and CTLA-4 genes could be used as early predictive biomarkers for aGVHD post-allo-HSCT.

Association of cord blood Ang-1 and sCD105 levels with bronchopulmonary dysplasia in preterm infants.

BACKGROUND: To investigate the relationship between cord blood levels of Angiopoietin-1 (Ang-1) and S-endoglin (sCD105) and bronchopulmonary dysplasia (BPD) in preterm infants. METHODS: Sixty-one preterm infants admitted to the neonatal intensive care unit of the study hospital between July 2021 and September 2022 were included. Cord blood was collected after the birth of premature infants. Ang-1 and sCD105 levels were quantified using the vascular endothelial growth factor enzyme-linked immunosorbent assay. Preterm infants were divided into BPD and non-BPD groups, and differences in Ang-1 and sCD105 levels between the two groups were compared. A binary logistic model was used to assess the association between low and high levels Ang-1 and BPD in preterm infants. RESULTS: In the study, there were 20 preterm infants with BPD (32.8%) and 41 preterm infants with non-BPD (67.2%). Ang-1 concentration levels were lower in the BPD group than in the non-BPD group (7105.43 (5617.01-8523.00) pg/ml vs. 10488.03 (7946.19-15962.77) pg/ml, P = 0.027). However, the sCD105 concentration levels were not significantly different between the BPD and non-BPD groups (P = 0.246). A median Ang-1 concentration of 8800.40 pg/ml was calculated. Logistic regression analysis showed that after adjusting for gestational age, birth weight, and maternal prenatal steroid hormone application, the odds ratio (OR) was 8.577 for the risk of BPD in preterm infants with Ang-1 concentrations of ≤ 8800.40 pg/ml compared to those with Ang-1 concentrations of > 8800.40 pg/ml (OR: 8.577, 95% confidence interval: 1.265-58.155, P = 0.028). CONCLUSION: Our study indicated that Ang-1 levels in the cord blood of preterm infants may be associated the risk of BPD. In the future, we will continue to conduct study with large samples.

Bowtie Nanoantenna LSPR Biosensor for Early Prediction of Preeclampsia.

OBJECTIVE: The concentration of the placental circulating factor in early pregnancy is often extremely low, and the traditional prediction method cannot meet the clinical demand for early detection preeclampsia in high-risk gravida. It is of prime importance to seek an ultra-sensitive early prediction method. METHODS: In this study, finite-different time-domain (FDTD) and Discrete Dipole Approximation (DDA) simulation, and electron beam lithography (EBL) methods were used to develop a bowtie nanoantenna (BNA) with the best field enhancement and maximum coupling efficiency. Bio-modification of the placental circulating factor (sFlt-1, PLGF) to the noble nanoparticles based on the amino coupling method were explored. A BNA LSPR biosensor which can specifically identify the placental circulating factor in preeclampsia was constructed. RESULTS: The BNA LSPR biosensor can detect serum placental circulating factors without toxic labeling. Serum sFlt-1 extinction signal (Δλmax) in the preeclampsia group was higher than that in the normal pregnancy group (14.37 ± 2.56 nm vs. 4.21 ± 1.36 nm), p = 0.008, while the serum PLGF extinction signal in the preeclampsia group was lower than that in the normal pregnancy group (5.36 ± 3.15 nm vs. 11.47 ± 4.92 nm), p = 0.013. The LSPR biosensor detection results were linearly consistent with the ELISA kit. CONCLUSIONS: LSPR biosensor based on BNA can identify the serum placental circulating factor of preeclampsia with high sensitivity, without toxic labeling and with simple operation, and it is expected to be an early detection method for preeclampsia.

Early Detection of Sub-clinical Mastitis in Murrah Buffaloes through Udder Thermogram Analysis during the Natural Progression of Infection.

Mastitis is a global production disease that needs an intelligent solution to tackle effectively. Infrared Thermography (IRT) is a non-invasive technology that could be incorporated into routine day-to-day farm activities to monitor the health status of the animals. In this study, the udder health status was routinely monitored for 30 days among 41 Murrah buffaloes via IRT and the California Mastitis Test (CMT). Further, somatic cell count (SCC), microbial identification, and milk quality parameters were also estimated for representative samples. The thermal imaging data obtained was tabulated and back propagated from the 0th day to the -10th day and front propagated from the 0th day to +10th day for all the udder quarters. Results revealed that on the 0th day, the mean of udder skin surface temperature (USST) and teat skin surface temperature (TSST) showed a difference (p < 0.05) in the sub-clinical mastitis (SCM) and clinical mastitis (CM) affected quarters to the healthy quarters, and their degree of difference was the highest. The indication of infection was signaled during the -9th to -5th day to the 0th day in SCM and CM cases. There was a steep increment in the temperature from -2nd and -1st day to the 0th day of infection. Sometimes, some quarters show an increment in temperature due to mastitis during morning hours but recover by evening milking due to the animal's innate immune system. Thus, the initiation period in which the udder gets assaulted is crucial in the early assessment of SCM by monitoring temperature change using IRT.

Ultra-early prediction of the process parameters of coal chemical production.

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence.

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Development and Validation of a Nomogram for Predicting the Severity of the First Episode of Hyperlipidemic Acute Pancreatitis.

Purpose: Early detection of hyperlipidemic acute pancreatitis (HLAP) with exacerbation tendency is crucial for clinical decision-making and improving prognosis. The aim of this study was to establish a reliable model for the early prediction of HLAP severity. Patients and Methods: A total of 225 patients with first-episode HLAP who were admitted to Fujian Medical University Union Hospital from June 2012 to June 2023 were included. Patients were divided into mild acute pancreatitis (MAP) or moderate-severe acute pancreatitis and severe acute pancreatitis (MSAP+SAP) groups. Independent predictors for progression to MSAP or SAP were identified through univariate analysis and least absolute shrinkage and selection operator regression. A nomogram was established through multivariate logistic regression analysis to predict this progression. The calibration, receiver operating characteristic(ROC), and clinical decision curves were employed to evaluate the model's consistency, differentiation, and clinical applicability. Clinical data of 93 patients with first-episode HLAP who were admitted to the First Affiliated Hospital of Fujian Medical University from October 2015 to October 2022 were collected for external validation. Results: White blood cell count, lactate dehydrogenase, albumin, serum creatinine, serum calcium, D-Dimer were identified as independent predictors for progression to MSAP or SAP in patients with HLAP and used to establish a predictive nomogram. The internally verified Harrell consistency index (C-index) was 0.908 (95% CI 0.867-0.948) and the externally verified C-index was 0.950 (95% CI 0.910-0.990). The calibration, ROC, and clinical decision curves showed this nomogram's good predictive ability. Conclusion: We have established a nomogram that can help identify HLAP patients who are likely to develop MSAP or SAP at an early stage, with high discrimination and accuracy.

Risk factors and early prediction of cardiorenal syndrome type 3 among acute kidney injury patients: a cohort study.

BACKGROUND: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram. METHODS: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort. RESULTS: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility. CONCLUSIONS: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors.

OBJECTIVE: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors. METHODS: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset. RESULTS: An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program.

The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.

Integrative Multimodal Metabolomics to Early Predict Cognitive Decline Among Amyloid Positive Community-Dwelling Older Adults.

Alzheimer's disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions.