Potential prognostic value of CSF-targeted proteomics across the Alzheimer's disease continuum.

BACKGROUND: Core biomarkers for Alzheimer's disease (AD), such as Aß42 and tau, have demonstrated high prognostic accuracy but do not fully capture the complex pathophysiology of AD. In this study, our objective was to identify novel cerebrospinal fluid (CSF) biomarkers using proteomics across the entire AD continuum to predict conversion to AD and explore their involvement in AD pathogenesis. METHODS: A cohort of 186 cognitively normal (CN), 127 subjective memory complaint (SMC), 79 early mild cognitive impairment (EMCI), 249 late MCI (LMCI), and 132 AD individuals was analyzed, with a follow-up period of over 3 years for non-AD participants. CSF 65 peptides, as well as hippocampal and entorhinal volumes were analyzed, and cognitive function was evaluated using the 13-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog 13). Cox proportional hazards models and mediation analysis were performed to investigate associations and causal relationships. RESULTS: During the follow-up, approximately one-fourth (146/580) of the non-AD participants progressed to AD. After adjusting for baseline diagnosis (CN to LMCI) and other variables, multivariable Cox regression analysis identified three peptides (VAELEDEK, VSFELFADK, and VVSSIEQK) as significant predictors of conversion to AD. Incorporating these three peptides into the initial model significantly improved the C-statistic from 0.82 to 0.85 for predicting AD conversion, surpassing the predictive ability of Aß42 and P-tau. Moreover, hippocampal and entorhinal volumes mediated 30.3-53.8% of the association between the three peptides and ADAS-Cog 13 scores. CONCLUSIONS: These findings underscore the potential of these three peptides as robust prognostic biomarker candidates for AD conversion across the entire AD continuum, with a mechanism involving the mediation of hippocampal and entorhinal volumes.

Effect of smoking on prostate cancer: Results from the National Health and Nutrition Examination Survey 2003-2018 and Mendelian randomization analyses.

INTRODUCTION: The controversial relationship between smoking and prostate cancer (PCa) risk prompted us to conduct a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database and apply Mendelian randomization (MR) analyses in order to clarify the possible causal effect of smoking on PCa risk. METHODS: Using univariate and multivariate logistic regression methods, a secondary analysis of the pooled 2003-2018 NHANES dataset was performed to explore the association between smoking and PCa risk. Propensity-score matching was used to reduce selection bias. Then, we conducted subsequent MR analysis study to investigate the potential causal effect of smoking on PCa risk, with genetic variants of four exposure factors including the lifetime smoking index, light smoking, smoking initiation, and the amount of smoking per day obtained from genome-wide association studies, and PCa summary statistics obtained from three database populations. Inverse-variance weighting was the primary analytical method, and weighted median and MR-Egger regression were used for sensitivity analyses. The MR results for the three PCa databases were combined using meta-analysis. RESULTS: The study included 16073 NHANES subjects, comprising 554 with PCa and 15519 without PCa. Logistic regression before and after matching did not reveal any significant association. Meta-analysis of the MR results also did not support an association of PCa risk with lifetime smoking index (OR=0.95; 95% CI: 0.83-1.09), light smoking (OR=1.00; 95% CI: 0.95-1.06), smoking initiation (OR=0.99, 95% CI=0.99-1.00), or the amount of smoking per day (OR=1.00; 95% CI: 0.99-1.00) and PCa risk. CONCLUSIONS: There was no evidence for an association between smoking and the risk of PCa. Further studies are needed to determine if there are any associations of other forms of smoking with the risk of PCa at different stages.

Competing risk nomogram predicting cause-specific mortality in older patients with testicular germ cell tumors.

Background: Testicular germ cell tumor (TGCT) is the most common type of malignancy in young men, but rarely in older adults. We aimed to construct a competing risk model to predict the prognosis for older patients with TGCT. Methods: We collected TGCT patients aged 50 years or older diagnosed between 2004 and 2015 from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. We estimated the cumulative incidences of cause-specific death (CSD) and other causes of death and established a nomogram predicting cause-specific mortality in older patients with TGCT by Fine-Gray competing risk regression. The concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC), and decision analysis curves (DCA) were used to evaluate the differentiation, accuracy, and clinical significance of the nomogram. Results: A total of 2,751 older TGCT patients were included in the study. The 3-, 5-, and 10-year cumulative incidences were 4.4, 5.0 and 6.1%, respectively, for cause-specific death, and 3.8, 6.2, 13.1%, respectively, for other causes of death. Predictors of cause-specific mortality in older TGCT included age, marital status, annual household income, histology, tumor size, stage and surgery. In the training and validation sets, the C-indexes were greater than 0.8, indicating that the nomogram had good discrimination. The AUC revealed the same result. The calibration curves showed good agreement between the predicted and observed results of the nomogram. DCA curves indicated that the nomogram had more clinical significance than the conventional American Joint Committee on Cancer (AJCC) staging. Based on the total nomogram score of each case, all patients were categorized into low-risk and high-risk groups, and risk categorization allowed the identification of cases with a high risk of death. Conclusion: We established a competing risk nomogram with good performance that may help clinicians accurately predict the prognosis of older TGCT patients.

Braden score can independently predict 90-day mortality in critically ill patients with dementia.

BACKGROUND: Dementia is a significant cause of death in the older population and is becoming an important public health issue as the population ages and the prevalence of dementia increases. The Braden score is one of the most commonly used clinical tools to assess the risk of skin pressure injury in patients, and some studies have reported that it may reflect the state of frailty of patients. The present study attempted to explore the association between Braden score and 90-day mortality, pressure injury, and aspiration pneumonia in older patients with dementia in the intensive care unit (ICU). METHODS: The study involved extracting crucial data from the Medical Information Market for Intensive Care IV (MIMIC-IV) database using Structured Query Language, with a license certificate obtained after completing the necessary training and examination available on the MIMIC-IV website. A retrospective analysis was performed on older patients with dementia, aged 65 or older, who were first admitted to the ICU. Ninth and tenth revision International Classification of Diseases codes were used to identify patients with dementia. The primary outcome was 90-day mortality. Cox proportional hazards models were used to determine the association between Braden score and death, and hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Propensity score matching and E-value assessments were employed for sensitivity analysis. RESULTS: A total of 2892 patients with a median age of approximately 85 years (interquartile range 78.74-89.59) were included, of whom 1625 were female (56.2%). Patients had a median Braden score of 14 (interquartile range 12-15) at ICU admission. Braden score at ICU admission was inversely associated with 90-day mortality risk after adjustment for demographics, severity of illness, treatment and medications, delirium, and sepsis (adjusted HR: 0.92, 95% CI: 0.87-0.98, p = 0.006). Patients were divided into two groups with a cut-off value of 15: high-risk group and low-risk group. Compared to the low-risk group (Braden score >15), the risk of 90-day mortality was significantly increased in the high-risk group (Braden score ≤15) (adjusted HR: 1.52, 95% CI: 1.10-2.09, p = 0.011, E-value: 2.01), the risk of pressure injury (adjusted OR: 2.62, 95% CI: 2.02-3.43, E-value: 2.62) and aspiration pneumonia (adjusted OR: 2.55, 95% CI: 1.84-3.61, E-value: 2.57) was also significantly higher. CONCLUSIONS: The Braden score may be a quick and simple screening tool to identify the risk of adverse outcomes in critically ill older adults with dementia.

Effect of a fall within three months of admission on delirium in critically Ill elderly patients: a population-based cohort study.

BACKGROUND: Delirium is common among elderly patients in the intensive care unit (ICU) and is associated with prolonged hospitalization, increased healthcare costs, and increased risk of death. Understanding the potential risk factors and early prevention of delirium is critical to facilitate timely intervention that may reverse or mitigate the harmful consequences of delirium. AIM: To clarify the effects of pre-admission falls on ICU outcomes, primarily delirium, and secondarily pressure injuries and urinary tract infections. METHODS: The study relied on data sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Statistical tests (Wilcoxon rank-sum or chi-squared) compared cohort characteristics. Logistic regression was employed to investigate the association between a history of falls and delirium, as well as secondary outcomes, while Kaplan-Meier survival curves were used to assess short-term survival in delirium and non-delirium patients. RESULTS: Study encompassed 22,547 participants. Delirium incidence was 40%, significantly higher in patients with a history of falls (54.4% vs. 34.5%, p < 0.001). Logistic regression, controlling for confounders, not only confirmed that a history of falls elevates the odds of delirium (OR: 2.11; 95% CI: 1.97-2.26; p < 0.001) but also showed it increases the incidence of urinary tract infections (OR:1.50; 95% CI:1.40-1.62; p < 0.001) and pressure injuries (OR:1.36; 95% CI:1.26-1.47; p < 0.001). Elderly delirium patients exhibited lower 30-, 180-, and 360-day survival rates than non-delirium counterparts (all p < 0.001). CONCLUSIONS: The study reveals that history of falls significantly heighten the risk of delirium and other adverse outcomes in elderly ICU patients, leading to decreased short-term survival rates. This emphasizes the critical need for early interventions and could inform future strategies to manage and prevent these conditions in ICU settings.

Association between secondhand smoke exposure and serum sex hormone concentrations among US female adults: a cross-sectional analysis using data from the National Health and Nutrition Examination Survey, 2013-2016.

OBJECTIVE: To estimate the association between secondhand smoke (SHS) exposure and serum sex hormone concentrations in female adults (never smokers and former smokers). DESIGN: Cross-sectional analysis. SETTING: US National Health and Nutrition Examination Survey, 2013-2016. OUTCOME MEASURES: Serum sex hormone measures included total testosterone (TT) and oestradiol (E2), sex hormone-binding globulin (SHBG), the ratio of TT and E2 and free androgen index (FAI). Isotope dilution-liquid chromatography tandem mass spectrometry was used to measure serum TT and E2. SHBG was measured using immunoassay. The ratio of TT and E2 and FAI were calculated. SHS exposure was defined as serum cotinine concentration of 0.05-10 ng/mL. PARTICIPANTS: A total of 622 female participants aged ≥20 years were included in the analysis. RESULTS: For never smokers, a doubling of serum cotinine concentration was associated with a 2.85% (95% CI 0.29% to 5.47%) increase in TT concentration and a 6.29% (95% CI 0.68% to 12.23%) increase in E2 in fully adjusted models. The never smokers in the highest quartile (Q4) of serum cotinine level exhibited a 10.30% (95% CI 0.78% to 20.72%) increase in TT concentration and a 27.75% (95% CI 5.17% to 55.17%) increase in E2 compared with those in the lowest quartile (Q1). For former smokers, SHBG was reduced by 4.36% (95% CI -8.47% to -0.07%, p for trend=0.049) when the serum cotinine level was doubled, and the SHBG of those in Q4 was reduced by 17.58% (95% CI -31.33% to -1.07%, p for trend=0.018) compared with those in Q1. CONCLUSION: SHS was associated with serum sex hormone concentrations among female adults. In never smokers, SHS was associated with increased levels of TT and E2. In former smokers, SHS was associated with decreased SHBG levels.

A prognostic nomogram for the cancer-specific survival rate of choroidal melanoma using the Surveillance, Epidemiology, and End Results database.

Objective: This study was conducted to develop a comprehensive nomogram for individuals with choroidal melanoma (CM) to determine their cancer-specific survival (CSS). Methods: Data of individuals with CM, diagnosed between 2004 and 2015, were accessed at the Surveillance, Epidemiology, and End Results (SEER) database. The selected individuals were randomly categorized into a training and validation cohort. Multivariate Cox regression analysis was applied to screen the relevant variables. Followed by the development of a nomogram based on independent variables. Ultimately, the net reclassification index (NRI), concordance index (C-index), calibration charts, integrated discrimination improvement (IDI), receiver operating characteristic curves (ROC), area under the curve (AUC), and decision-curve analysis (DCA), were utilized to evaluate the discrimination, accuracy, and effectiveness of the model. Results: This study enrolled 3,782 patients. Seven independent factors linked to prognosis were screened via multivariate Cox regression analysis, encompassing age at diagnosis; race; AJCC (American Joint Committee on Cancer) stage; histologic type; and therapy method of radiotherapy, surgery, and chemotherapy. The respective C-indexes of the training and validation cohorts were 0.709 and 0.726, indicative of the excellent accuracy of the nomogram. Furthermore, the AUCs of the training and validation cohorts across 3, 5, and 8 years were 0.767, 0.744, and 0.722 as well as 0.772, 0.770, and 0.753, respectively. Evident of the superiority of the established nomogram over the AJCC staging, both the NRI and IDI values exhibited improvement. The favorable clinical impact and good performance of the nomogram were evident via decision curve analyses (DCAs) and calibration plots, respectively. Conclusion: This research dealt with establishing and validating a nomogram as a prognostic tool for assessing the prognosis of adult patients with CM utilizing the SEER database. A comprehensive assessment of the nomogram via diverse variables demonstrated its accuracy in predicting the CSS probabilities of CM patients across 3, 5, and 8 years in clinical settings. Notably, its performance surpassed that of the AJCC staging system.

Spraying double-stranded RNA targets UDP-N-acetylglucosamine pyrophosphorylase in the control of Nilaparvata lugens.

The rice pest Nilaparvata lugens (the brown planthopper, BPH) has developed different levels of resistance to at least 11 chemical pesticides. RNAi technology has contributed to the development of environmentally friendly RNA biopesticides designed to reduce chemical use. Consequently, more precise targets need to be identified and characterized, and efficient dsRNA delivery methods are necessary for effective field pest control. In this study, a low off-target risk dsNlUAP fragment (166 bp) was designed in silico to minimize the potential adverse effects on non-target organisms. Knockdown of NlUAP via microinjection significantly decreased the content of UDP-N-acetylglucosamine and chitin, causing chitinous structural disorder and abnormal phenotypes in wing and body wall, reduced fertility, and resulted in pest mortality up to 100 %. Furthermore, dsNlUAP was loaded with ROPE@C, a chitosan-modified nanomaterial for spray application, which significantly downregulated the expression of NlUAP, led to 48.9 % pest mortality, and was confirmed to have no adverse effects on Cyrtorhinus lividipennis, an important natural enemy of BPH. These findings will contribute to the development of safer biopesticides for the control of N. lugens.

Joinpoint regression analysis of recent trends in desmoplastic malignant melanoma incidence and mortality: 15-year multicentre retrospective study.

BACKGROUND: Recent data reveal a marked rise in the detection and mortality rates of Desmoplastic Malignant Melanoma (DMM). This trend underscores the imperative for an in-depth analysis of DMM's epidemiology, which is crucial for the formulation of precise medical and public health strategies. This investigation seeks to elucidate the variations in the incidence and mortality of DMM over a 15-year period (2005-2019). METHODS: Data on DMM patients was sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Both incidence and incidence-based mortality rates (IBM) were directly extracted from the SEER database. Joinpoint regression was used to analyze and calculate the average annual percent change (AAPC) and its 95% confidence interval (CI). RESULTS: Between 2005 and 2019, 3,384 DMM cases were identified, boasting an age-adjusted incidence rate of 36.3 cases per 1000,000 person-years (95% CI 3.51-3.76) and an IBM of 1.65cases per 1000,000 person-years (95% CI 1.57-1.74). Of these, 2,353 were males (69.53%) and 1,031 were females (30.47%). There were 1894 patients (55.97%) who were over 70 years old. Predominantly, DMM lesions manifested in exposed areas: Limbs (955, 28.22%), Face (906, 26.77%), and Scalp and Neck (865, 25.56%). The incidence of DMM increased significantly at a rate of APC = 0.9% during 2005-2019, while the incidence-based mortality showed a significant upward trend (APC = 7%) during 2005-2012, and slowly increasing trend (APC = 0.6%) during 2012-2019. In contrast to the modest upward trajectory in female incidence and mortality, male incidence initially surged, later declining, while male mortality peaked and stabilized post-2012. The primary sites for incidence and mortality were chronically sun-exposed areas: Face, Scalp and Neck, and Limbs. CONCLUSIONS: In recent years, the incidence and incidence-based mortality of DMM have significantly increased. Each subgroup analysis has different trends, and these trends can provide better support for our exploration of DMM.

Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study.

Background: There are few research findings on the survival prognosis of spindle cell melanoma (SCM), which is an unusual kind of melanoma. The purpose of this study was to develop a thorough nomogram for predicting the overall survival (OS) of patients with SCM and to assess its validity by comparing it with the conventional American Joint Committee on Cancer (AJCC) staging system. Methods: The Surveillance, Epidemiology, and End Results database was searched, and 2,015 patients with SCM were selected for the analysis. The patients were randomly divided into training (n = 1,410) and validation (n = 605) cohorts by using R software. Multivariate Cox regression was performed to identify predictive factors. A nomogram was established based on these characteristics to predict OS in SCM. The calibration curve, concordance index (C-index), area under the receiver operating characteristic curve, and decision-curve analysis were utilized to assess the accuracy and reliability of the model. The net reclassification improvement and integrated discrimination improvement were also applied in this model to evaluate its differences with the AJCC model. Results: The developed nomogram suggests that race, AJCC stage, chemotherapy status, regional node examination status, marital status, and sex have the greatest effects on OS in SCM. The nomogram had a higher C-index than the AJCC staging system (0.751 versus 0.633 in the training cohort and 0.747 versus 0.650 in the validation cohort). Calibration plots illustrated that the model was capable of being calibrated. These criteria demonstrated that the nomogram outperforms the AJCC staging system alone. Conclusion: The nomogram developed in this study is sufficiently reliable for forecasting the risk and prognosis of SCM, which may facilitate personalized treatment recommendations in upcoming clinical trials.

Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis.

BACKGROUND: Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations. METHODS: Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis. RESULTS: This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram. CONCLUSIONS: The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

BACKGROUND: The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS: 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS: Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS: In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Dual-space high-frequency learning for transformer-based MRI super-resolution.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing works prioritize the design of transformer attention blocks to capture global information. The local high-frequency details, which are pivotal to faithful MRI restoration, are unfortunately neglected. METHODS: In this work, we propose a high-frequency enhanced learning scheme to effectively improve the awareness of high frequency information in current Transformer-based MRI single image super-resolution methods. Specifically, we present two entirely plug-and-play modules designed to equip Transformer-based networks with the ability to recover high-frequency details from dual spaces: 1) in the feature space, we design a high-frequency block (Hi-Fe block) paralleled with Transformer-based attention layers to extract rich high-frequency features; while 2) in the image intensity space, we tailor a high-frequency amplification module (HFA) to further refine the high-frequency details. By fully exploiting the merits of the two modules, our framework can recover abundant and diverse high-frequency information, rendering faithful MRI super-resolved results with fine details. RESULTS: We integrated our modules with six Transformer-based models and conducted experiments across three datasets. The results indicate that our plug-and-play modules can enhance the super-resolution performance of all foundational models to varying degrees, surpassing the capabilities of existing state-of-the-art single image super-resolution networks. CONCLUSION: Comprehensive comparison of super-resolution images and high-frequency maps from various methods, clearly demonstrating that our module possesses the capability to restore high-frequency information, showing huge potential in clinical practice for accelerated MRI reconstruction.

Specialized spatially-arranged non-circular fibers enhance filtration performance of African shrimp (Atya gabonensis).

African shrimp (Atya gabonensis) inhabit clear freshwaters, where the notably low concentration of food may pose a challenge to the efficacy of filter fibers on the chela for filter-feeding. Here, we investigate how the distinctive cross-sectional characteristics and spatial arrangement of the African shrimp's non-circular fibers contribute to the enhanced filtration performance of these specialized fibers. The unilateral thickening of the wall along the long axis of the elliptical cross-section of African shrimp fibers markedly enhances the filtration performance. The staggered and twisted arrangement of the fibers optimizes the surrounding flow field, achieving a favorable balance between pressure drop and collection efficiency, consequently improving their filtration performance in collecting fine particles (diameter: 2-10µm). Moreover, the arrangement of the fibers substantially increases the effective flow-facing filtering area of the fiber bundles, thus facilitating their efficiency in collecting larger particles (diameter > 10µm). The unique fiber properties of the African shrimp offer novel insights for the design and optimization of new fiber-filtering robots, presenting a wide range of potential applications, such as marine in-situ resource extraction, medical filtration, and industrial filtration.

Variations in the Prevalence of Anemia of Varying Severity Among Urban Non-Pregnant Women - China, 2021.

What is already known about this topic?: Anemia is a significant public health issue affecting women globally. Prior studies in China predominantly concentrated on anemia in pregnant or reproductive-age women, leaving a gap in available data concerning anemia in non-pregnant women of all age groups in China. What is added by this report?: In 2021, the prevalence of anemia and moderate to severe anemia among women aged 18 years and older in urban China was 14.8% and 5.7%, respectively. Anemia prevalence exhibited significant variations based on factors such as age, body mass index (BMI), geographic location, and socioeconomic status. What are the implications for public health practice?: The strategy for addressing anemia should account for non-pregnant women aged 30-49 years and those aged 70 years and older, taking into consideration differences related to socioeconomic development and geography.

Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis.

BACKGROUND: Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis. METHODS: Retrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer-Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted. RESULTS: The research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems. CONCLUSIONS: The nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.