Search Results (5)

Background: Takotsubo Syndrome (TTS) is a transient left ventricular systolic dysfunction typically characterized by anteroseptal-apical dyskinetic ballooning of the left ventricle with a hyperkinetic base, without significant obstructive coronary artery disease. The interplay between systemic inflammation and hemodynamic stress in sepsis exacerbates susceptibility to TTS. We aim to investigate the characteristics and factors associated with TTS in critically ill patients with sepsis admitted to the intensive care unit. Methods: A retrospective cohort study was conducted on 361 patients admitted to the medical ICU at a tertiary care hospital in New York City. All patients underwent transthoracic echocardiography (TTE) within 72 h of sepsis diagnosis. Patients were divided into TTS and non-TTS groups. Clinical data, comorbidities, and hemodynamic parameters were extracted from electronic medical records and analysed using multivariate logistic regression to determine independent predictors of TTS. Results: Among 361 patients, 24 (6.65%) were diagnosed with TTS. Female sex (OR 3.145, 95% CI 1.099–9.003, p = 0.033) and higher shock index (OR 4.454, 95% CI 1.426–13.910, p = 0.010) were significant predictors of TTS. Individuals with ≥ 25 kg/m² had a lower odds of developing TTS as compared to their obese counterparts (OR 0.889, 95% CI 0.815–0.969, p = 0.007). Conclusions: The findings highlight that Female sex, higher shock index and a BMI < 25 kg/m² emerge as possible predictors for development of TTS in patients with sepsis. Further research is needed to unravel the mechanisms behind the “obesity paradox” in TTS and optimize clinical strategies for high-risk patients. Full article

Cardiotoxicity is a well-known adverse effect of cancer-related therapy that has a significant influence on patient outcomes and quality of life. The use of antineoplastic drugs to treat colorectal cancers (CRCs) is associated with a number of undesirable side effects including cardiac complications. For both sexes, CRC ranks second and accounts for four out of every ten cancer deaths. According to the reports, almost 39% of patients with colorectal cancer who underwent first-line chemotherapy suffered cardiovascular impairment. Although 5-fluorouracil is still the backbone of chemotherapy regimen for colorectal, gastric, and breast cancers, cardiotoxicity caused by 5-fluorouracil might affect anywhere from 1.5% to 18% of patients. The precise mechanisms underlying cardiotoxicity associated with CRC treatment are complex and may involve the modulation of various signaling pathways crucial for maintaining cardiac health including TKI ErbB2 or NRG-1, VEGF, PDGF, BRAF/Ras/Raf/MEK/ERK, and the PI3/ERK/AMPK/mTOR pathway, resulting in oxidative stress, mitochondrial dysfunction, inflammation, and apoptosis, ultimately damaging cardiac tissue. Thus, the identification and management of cardiotoxicity associated with CRC drug therapy while minimizing the negative impact have become increasingly important. The purpose of this review is to catalog the potential cardiotoxicities caused by anticancer drugs and targeted therapy used to treat colorectal cancer as well as strategies focused on early diagnosing, prevention, and treatment of cardiotoxicity associated with anticancer drugs used in CRC therapy. Full article

A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test–retest pairs scanned on consecutive days) was split into approximately 70% training, 10% validation, and 20% test subsets. Two expert annotators (EA1 and EA2) performed manual segmentations of the mouse tibia (EA1: all data; EA2: test and validation). Attention U-net (A-U-net) model performance was assessed for accuracy with respect to EA1 reference using the average Jaccard index (AJI), volume intersection ratio (AVI), volume error (AVE), and Hausdorff distance (AHD) for four training scenarios: full training, two half-splits, and a single-mouse subsets. The repeatability of computer versus expert segmentations for tibia volume of test–retest pairs was assessed by within-subject coefficient of variance (%wCV). A-U-net models trained on full and half-split training sets achieved similar average accuracy (with respect to EA1 annotations) for test set: AJI = 83–84%, AVI = 89–90%, AVE = 2–3%, and AHD = 0.5 mm–0.7 mm, exceeding EA2 accuracy: AJ = 81%, AVI = 83%, AVE = 14%, and AHD = 0.3 mm. The A-U-net model repeatability wCV [95% CI]: 3 [2, 5]% was notably better than that of expert annotators EA1: 5 [4, 9]% and EA2: 8 [6, 13]%. The developed deep learning model effectively automates murine bone marrow segmentation with accuracy comparable to human annotators and substantially improved repeatability. Full article

Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and maize. Climate datasets were collected over the period from 1969 to 2019, including: mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (H), solar radiation (SR), sunshine hours (Ssh), wind speed (WS), and day length (DL). Five machine learning (ML) methods were applied, including random forest (RF), support vector regression (SVM), bagged trees (BT), boosted trees (BoT), and matern 5/2 Gaussian process (MG). Models implemented by MG, including Tmean, SR, WS, and DL (Model 3); Tmax, Tmin, Tmean, SR, Ssh, WS, H, and DL (Model 8); Tmean, and SR (Model 9), were found optimal (r² = 0.85) for forecasting CWP for wheat. Moreover, results of CWP for maize showed that the BT model, a combination of SR, WS, H, and Tmin data, achieved a high correlation coefficient of 0.82 compared to others. The outcomes demonstrated several high performance ML-based alternative CWP estimation methods in case of limited climatic data supporting decision making for designers, developers, and managers of water resources. Full article

Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R² and Adjusted R² while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations. Full article