Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model.

PURPOSE: Heart failure (HF) is a widespread ailment and is a primary contributor to hospital admissions. The focus of this study was to identify factors affecting the extended-term survival of patients with HF, anticipate patient outcomes through cause-of-death analysis, and identify risk elements for preventive measures. METHODS: A total of 435 HF patients were enrolled from the medical records of the Rajaie Cardiovascular Medical and Research Center, covering data collected between March and August 2018. After a five-year follow-up (July 2023), patient outcomes were assessed based on the cause of death. The survival analysis was performed with the AFT method with the Bayesian approach in the presence of competing risks. RESULTS: Based on the results of the best model for HF-related mortality, age [time ratio = 0.98, confidence interval 95%: 0.96-0.99] and ADHF [TR = 0.11, 95% (CI): 0.01-0.44] were associated with a lower survival time. Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10-0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12-0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13-0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37-0.90]. CAD [TR = 0.65, 95% (CI): 0.38-0.98], CKD [TR = 0.52, 95% (CI): 0.28-0.87], and AF [TR = 0.53, 95% (CI): 0.32-0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality. CONCLUSION: The study identified distinct predictive factors for overall survival among patients with HF-related mortality or non-HF-related mortality. This differentiated approach based on the cause of death contributes to the estimation of patient survival time and provides valuable insights for clinical decision-making.

Red-emissive carbon nanostructure-anchored molecularly imprinted Er-BTC MOF: a biosensor for visual anthrax monitoring.

Investigating effective fluorescence strategies for real-time monitoring of dipicolinic acid (DPA) is of paramount importance in safeguarding human health. Herein, we present the design of a desirable red-emissive carbon nanostructure anchoring a molecularly imprinted Er-BTC MOF as a fluorescence biosensor for the visual determination of DPA. DPA is a biomarker of Bacillus anthracis, a subcategory of serious infectious diseases and bioweapons. We introduce a paper test strip sensitized with the aforementioned nanostructure, which is integrated with online UV excitation and smartphone digital imaging, resulting in a DPA signal-off sensing platform. The proposed fluorometric visual paper-based biosensor demonstrates wide linear ranges for DPA (10-125 µM) with a LOQ and LOD of 4.32 and 1.28 µM, respectively. The designed platform exhibits impressive emission properties and adaptable surface functional groups, which confirm its desirable selective sensing capabilities against other biological molecules and DPA isomers. As a proof of concept, DPA monitoring is successfully applied to real samples of tap water and urine. This integrated selective paper-based nano-biosensor, coupled with smartphone signal recording, holds great promise for state-of-the-art practical applications including fluorometric/colorimetric detection in healthcare and environmental monitoring, food safety analysis, and point-of-care testing.

Paper-based microfluidic system and chiroptical functionalized gold nano-oval for colorimetric detection of L-Tryptophan.

"The development and deployment of a practical and portable technology for on-site chiral identification of enantiomers hold immense significance in the fields of medical and biological sciences. Among the essential amino acids, Tryptophan (Trp) plays a crucial role in human metabolism and serves as a diagnostic marker for various metabolic disorders. In this study, we introduce an innovative approach that combines an enantio-selective ZIF-8-His MOF-MIPs packed-bed centrifugal microfluidic system with an enantioselective colorimetric sensor probe. This system is further integrated with smartphone-based on-site data recording. The basis of this colorimetric sensor's operation lies in the controlled morphology and surface passivation of gold nano-ovals (Au-NOs) through DL-Alanine. To confirm the successful synthesis of the chiral recognition elements, we employed various characterization techniques, including FE-SEM, TEM, FTIR, CD, UV-Vis, zeta potential, DLS, and XRD. Our focus was on optimizing operational parameters for the effective separation and determination of L-chiral tryptophan on-site. The sensor exhibited two linear ranges for L-Trp detection: 0-5.42 and 5.42-80.47 mM, with a detection limit of 0.5 mM. The integrated system possesses advantages such as ease of availability, preparation, high stability, desirable selectivity even in the presence of similar biomolecules, and rapid detection capabilities. Furthermore, our method demonstrated successful enantioselective sensing of L-Trp in various biological samples, including human blood plasma, urine, milk, and bovine serum albumin (BSA), yielding promising results. The integrated microfluidic platform follows a "sample-in and answer-out" approach, making it highly applicable in healthcare, environmental monitoring, food safety analysis, and point-of-care testing. The chiral recognition pretreatment assay and self-contained, automated colorimetric detection on the microfluidic disc represent a promising avenue for cutting-edge research in these domains".

Major dietary patterns and sleep quality in relation to overweight/obesity among school children: A case-control study.

Background: Childhood overweight/obesity is increasing worldwide. There is evidence on the role of dietary patterns (DPs) and sleep quality on body weight in adults, but studies on the association of major DPs, sleep quality and overweight/obesity among school-age children are scarce, so the present study was done to shade a light on the subject. Methods: This study was a case-control study, conducted on school-age (7-13 years) children. Cases were healthy children who had a body mass index (BMI) percentile of≥85th for age and sex (n=102). Sex-matched children with a BMI percentile between 5th and 85th were considered as control group (n=102). Dietary data were collected using a validated 168-item food frequency questionnaire. Sleep quality was assessed by Pittsburgh sleep quality index. Binary logistic regression was used to assess the association between DPs, sleep quality, and overweight/obesity. Results: Three DPs were identified: "Low-energy healthy", "High-energy healthy" and "Unhealthy diet". Adherence to the first and second DPs was associated with 51%-62% lower odds of overweight/obesity (Odds ratio [OR]: 0.49, 95% CI: 0.24-0.97, and 0.38, 95% CI: 0.15-0.94, respectively, P<0.050). However, we found no significant association for the third DP with overweight/obesity. Furthermore, there was no significant association between sleep quality/duration and overweight/obesity. The interactions of DPs and sleep quality/duration with overweight/obesity were not significant. Conclusion: Eating a diet high in white meats, eggs, vegetables, fruits and juices, nuts, dairy products, whole grains, and low in refined grains and snacks is associated with a lower likelihood of overweight/obesity in children. This inverse association does not depend on sleep quality/duration.

Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.

Medical research frequently relies on Cox regression to analyze the survival distribution of cancer patients. Nonetheless, in specific scenarios, neural networks hold the potential to serve as a robust alternative. In this study, we aim to scrutinize the effectiveness of Cox regression and neural network models in assessing the survival outcomes of patients who have undergone treatment for colorectal cancer. We conducted a retrospective study on 284 colorectal cancer patients who underwent surgery at Imam Khomeini clinic in Hamadan between 2001 and 2017. The data was used to train both Cox regression and neural network models, and their predictive accuracy was compared using diagnostic measures such as sensitivity, specificity, positive predictive value, accuracy, negative predictive value, and area under the receiver operating characteristic curve. The analyses were performed using STATA 17 and R4.0.4 software. The study revealed that the best neural network model had a sensitivity of 74.5% (95% CI 61.0-85.0), specificity of 83.3% (65.3-94.4), positive predictive value of 89.1% (76.4-96.4), negative predictive value of 64.1% (47.2-78.8), AUC of 0.79 (0.70-0.88), and accuracy of 0.776 for death prediction. For recurrence, the best neural network model had a sensitivity of 88.1% (74.4-96.0%), specificity of 83.7% (69.3-93.2%), positive predictive value of 84.1% (69.9-93.4%), negative predictive value of 87.8% (73.8-95.9%), AUC of 0.86 (0.78-0.93), and accuracy of 0.859. The Cox model had comparable results, with a sensitivity of 73.6% (64.8-81.2) and 85.5% (78.3-91.0), specificity of 89.6% (83.8-93.8) and 98.0% (94.4-99.6), positive predictive value of 84.0% (75.6-90.4) and 97.4% (92.6-99.5), negative predictive value of 82.0% (75.6-90.4) and 88.8% (0.83-93.1), AUC of 0.82 (0.77-0.86) and 0.92 (0.89-0.95), and accuracy of 0.88 and 0.92 for death and recurrence prediction, respectively. In conclusion, the study found that both Cox regression and neural network models are effective in predicting early recurrence and death in patients with colorectal cancer after curative surgery. The neural network model showed slightly better sensitivity and negative predictive value for death, while the Cox model had better specificity and positive predictive value for recurrence. Overall, both models demonstrated high accuracy and AUC, indicating their usefulness in predicting these outcomes.

Changes in sexual activities, function, and satisfaction during the COVID-19 pandemic era: a systematic review and meta-analysis.

Introduction: Little is known about the impact of the coronavirus on sexual behavior, function, and satisfaction. Aim: The aim of the present study was to systematically review people's sexual function and behaviors and their changes in sexual activities during the COVID-19 pandemic. Methods: Comprehensive searches in PubMed, Web of Science, and Scopus were conducted with keywords in accordance with MeSH terms: COVID-19, SARS-CoV-2, coronavirus, sexual health, sexual function, sexual dysfunctions, sexuality, sexual orientation, sexual activities, and premarital sex. Two reviewers independently assessed full-text articles according to predefined criteria: original design, English studies, and investigating either the general population or sexual minorities. Results: Risk of bias in the studies was assessed by the Newcastle-Ottawa Scale, and data were pooled via random effects meta-analyses. We utilized the standardized mean difference to evaluate the effects of the COVID-19 pandemic on sexual activity, functioning, and satisfaction. We included 19 studies in the analysis and 11 studies in the meta-analysis, with a sample size of 12 350. To investigate sexual activity changes, a sample size of 8838 was entered into the subgroup analysis, which showed a significant decrease in both genders (5821 women, P < .033; 3017 men, P < .008). A subgroup meta-analysis showed that the sexual function of men and women during the COVID-19 pandemic significantly declined (3974 women, P < .001; 1427 men, P < .001). Sexual desire and arousal decreased in both genders, though mainly in women. In investigating sexual satisfaction changes during the COVID-19 pandemic, a meta-analysis with a sample size of 2711 showed a significant decrease (P < .001). The most indicative changes in sexual behaviors during the pandemic were the increase in masturbating and usage of sex toys. Greater COVID-19 knowledge was associated with lower masturbation, oral sex, and vaginal sex. The more protective behaviors were associated with less hugging, kissing, cuddling, genital touching, watching porn with a partner, and vaginal sex. Conclusion: The COVID-19 pandemic led to increased challenges and changes for individuals' sexual behaviors. Efforts for preventive strategies should therefore be concentrated between pandemics, while ensuring that there is information available to the population during a pandemic for help in times of psychological distress or crisis.

Marriage survival in new married couples: A competing risks survival analysis.

BACKGROUND AND AIM: Marriage is one of the most important phenomena in human life. The survival of the marriage and the impact of various competing factors on the survival is of high importance. This study aimed at utilizing competing risks survival analysis to investigate the marriage survival of new couples in Tabriz. METHODS: In this longitudinal study, a number of 386 individuals who were married and divorced from 1991 to 2017, were selected by random sampling. The registered information was attained from the general registry office of Tabriz. Data analysis was carried out using the Lunn-McNeil procedure and the results were presented using an adjusted hazard ratio (AHR). RESULTS: The average age of marriage was about 23.9 (SD 6.6) years. The results of multivariate Lunn-McNeil models indicated that for the competing cause of having a relationship with another person (AHRs range: 1.12 to 2.03), the traditional mode of being familiar (AHRs range: 1.55 to 3.39), family weak role in choosing a wife/spouse (AHRs range: 0.25 to 3.25) and the role of moral-religious commitment (AHRs range: 0.37 to 0.47), along with other causes severed the risk of marriage survival reduction. CONCLUSION: According to the results of this study in assessing competing risks, we conclude that the decline in marriage survival is a multifactorial phenomenon. Examining the survival of marriage in order to better understand all the dimensions and factors affecting this phenomenon and providing information to counselors and officials can play an important role in increasing marriage survival.

Modeling Survival in Patients With Brain Stroke in the Presence of Competing Risks.

OBJECTIVES: After heart disease, brain stroke (BS) is the second most common cause of death worldwide, underscoring the importance of understanding preventable and treatable risk factors for the outcomes of BS. This study aimed to model the survival of patients with BS in the presence of competing risks. METHODS: This longitudinal study was conducted on 332 patients with a definitive diagnosis of BS. Demographic characteristics and risk factors were collected by a validated checklist. Patients' mortality status was investigated by telephone follow-up to identify deaths that may be have been caused by stroke or other factors (heart disease, diabetes, high cholesterol, etc.). Data were analyzed by the Lunn-McNeil approach at alpha=0.1. RESULTS: Older age at diagnosis (59-68 years: adjusted hazard ratio [aHR], 2.19; 90% confidence interval [CI], 1.38 to 3.48; 69-75 years: aHR, 5.04; 90% CI, 3.25 to 7.80; ≥76 years: aHR, 5.30; 90% CI, 3.40 to 8.44), having heart disease (aHR, 1.65; 90% CI, 1.23 to 2.23), oral contraceptive pill use (women only) (aHR, 0.44; 90% CI, 0.24 to 0.78) and ischemic stroke (aHR, 0.52; 90% CI, 0.36 to 0.74) were directly related to death from BS. Older age at diagnosis (59-68 years: aHR, 21.42; 90% CI, 3.52 to 130.39; 75-69 years: aHR, 16.48; 90% CI, 2.75 to 98.69; ≥76 years: aHR, 26.03; 90% CI, 4.06 to 166.93) and rural residence (aHR, 2.30; 90% CI, 1.15 to 4.60) were directly related to death from other causes. Significant risk factors were found for both causes of death. CONCLUSIONS: BS-specific and non-BS-specific mortality had different risk factors. These findings could be utilized to prescribe optimal and specific treatment.