Category: Nevin Manimala

Med Sci Sports Exerc. 2023 Jan 30. doi: 10.1249/MSS.0000000000003135. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study is to determine the criterion validity of Activities Completed over Time (ACT24), an automated previous day recall tool designed for mobile devices for 1) estimating sedentary vs active time compared to an activPAL; and 2) estimating time spent in activity domains (e.g., work, household, leisure) compared to direct observation (DO).

METHODS: Over a 7-day study period, 53 participants were sent invitations to complete three automated ACT24 recalls and to wear an activPAL device. A subset (N = 24) consented to two, 3-hour video recorded DO sessions. activPAL and ACT24 data were matched by date, and agreement for sedentary versus active time was compared between methods using paired t-tests for mean differences and spearman correlations. We compared DO and ACT24 results by domain for overall time-use and separately for sedentary and active time using Kappa statistics and tested mean differences with linear mixed models.

RESULTS: Compared to the activPAL, the mean difference in ACT24 sedentary time was 1.9% (mean [95%CI] -0.17 [-0.75,0.40] hrs/day) and the mean difference for ACT24 active time was 2.2% (0.14 [-0.32,0.60] hrs/day). Correlations were R = 0.61 (95% CI: [0.39, 0.76]) and R = 0.65 (0.44, 0.78) for sedentary and active time, respectively. Domain-specific agreement was substantial for leisure-time, work, and shopping/errands (Kappa range: 0.63-0.79), moderate for transportation (Kappa = 0.49) and fair for personal care and household activities (Kappa: 0.24 and 0.33). ACT24 estimates of average time within each domain were not significantly different than DO.

CONCLUSIONS: The present study confirms that ACT24 is accurate for group-level estimation of active and sedentary time. Domain-specific agreement tended to be higher for more commonly reported activities and those that were of longer duration.

PMID:36719650 | DOI:10.1249/MSS.0000000000003135

J Perinat Neonatal Nurs. 2023 Jan 30. doi: 10.1097/JPN.0000000000000686. Online ahead of print.

ABSTRACT

OBJECTIVE: Under-5 mortality has declined globally; however, proportion of under-5 deaths occurring within the first 28 days after birth has increased significantly. This study aims to determine the impact of an educational intervention on neonatal care and survival rates in Nigeria.

METHODS: This was a sequential exploratory mixed-methods design involving 21 health workers in the preintervention phase, while 15 health workers and 30 mother-baby dyads participated in the postintervention phase. Data were collected using semistructured interviews and nonparticipatory observation. Qualitative data were analyzed using thematic analysis, while quantitative data were analyzed using descriptive and inferential statistics.

RESULTS: Healthy newborns were routinely separated from their mothers in the preintervention period. During this time, non-evidence-based practices, such as routine nasal and oral suctioning, were performed. Skin-to-skin contact and early initiation of breastfeeding were frequently interrupted. After the intervention, 80.6% were placed in skin-to-skin contact with their mothers, and 20 of these babies maintained contact with the mother until breastfeeding was established. There was decline in neonatal deaths post-intervention. Independent t-test analysis of the day of neonatal death demonstrates a significant difference in mean (P = .00, 95% confidence interval -5.629; -7.447 to -4.779).

CONCLUSION: Newborn survival can be improved through regular training of maternity health workers in evidence-based newborn care.

PMID:36719649 | DOI:10.1097/JPN.0000000000000686

J Relig Health. 2023 Jan 31. doi: 10.1007/s10943-023-01749-z. Online ahead of print.

ABSTRACT

This study was conducted to determine the compassion fatigue level of nurses and to review several variables believed to be associated with it; in addition, an assessment is made of empathy levels in the same group. This is a cross-sectional study conducted from December 2021 to May 2022 on nurses working at a city hospital linked to the Turkish Ministry of Health. The study group consisted of 616 nurses. A Personal Information Form, the Compassion Fatigue-Short Scale (CF-SS), and the Jefferson Scale of Empathy were used to collect data. Data were collected through face-to-face interviews. Student’s t-test, One-Way Analysis of Variance, and Multiple Linear Regression Analysis were used for data analysis. The statistical significance value was accepted as p < 0.05. The study group consisted of 499 (81.0%) females and 117 (19.0%) males, and their ages ranged from 20 to 51, with a mean age of 29.2 ± 6.9 years. The scores obtained from the CF-SS ranged from 16 to 130, with a mean score of 70.96 ± 25.04. The level of compassion fatigue was found to be higher in participants with a low family income, those who work more than 40 h a week, those who chose their profession unwillingly, those who are not satisfied with their profession, and those with a history of contact with a COVID-19 patient (p < 0.05 for each group). There was a significant association between levels of compassion fatigue and empathy (r = 0.92; p = 0.220). The level of compassion fatigue was found to be moderate in the nurses observed. The factors affecting the level of compassion fatigue included gender, family income, reasons for choosing nursing as a profession, the number of patients given daily care by the nurses, satisfaction with their profession, and history of contact with a COVID-19 patient. More extensive studies focusing on the association between compassion fatigue and empathy in nurses are needed.

PMID:36719601 | DOI:10.1007/s10943-023-01749-z

Environ Sci Pollut Res Int. 2023 Jan 31. doi: 10.1007/s11356-023-25344-7. Online ahead of print.

ABSTRACT

Statistical analysis of streamflow and sediment is very important for integrated watershed management and the design of water infrastructure, especially in silt-rich rivers. Here, we propose a bivariate joint distribution framework based on nonparametric kernel density estimation (KDE) and a hybrid copula function to describe the complex streamflow-sediment dependent structure. In this framework, the non-parametric KDE is used to fit the marginal distribution function of streamflow and sediment variables, and then the hybrid copula function is constructed by using the linear combination of Clayton, Frank, and Gumbel copulas, and compared with five commonly used single copulas (Clayton, Frank, Gumbel, Gaussian, and t). We use the Jinsha River Basin (JRB) in the Yangtze River’s (JR) upper reaches to verify the proposed method. The results show the following: (1) Compared with the gamma distribution (Gamma) and generalized extreme value (GEV) distribution of parameters, the marginal distribution function of streamflow and sediment variables can be effectively obtained based on nonparametric KDE. (2) Compared with the single copula, the hybrid copula function more fully reflects the complex dependent structure of streamflow and sediment variables. (3) Compared with the best single copula, the precision of return period based on hybrid copula can be increased by 7.41%. In addition, the synchronous probability of streamflow and sediment in JRB is 0.553, and the asynchronous probability of streamflow and sediment is 0.447. This study can not only improve the accuracy of streamflow and sediment statistical analysis in JRB, but also provide a useful framework for other bivariate joint probability analysis.

PMID:36719583 | DOI:10.1007/s11356-023-25344-7

Ann Surg Oncol. 2023 Jan 31. doi: 10.1245/s10434-023-13147-6. Online ahead of print.

ABSTRACT

BACKGROUND: Current success in transplant oncology for select liver tumors, such as hepatocellular carcinoma, has ignited international interest in liver transplantation (LT) as a therapeutic option for nonresectable colorectal liver metastases (CRLM). In the United States, the CRLM LT experience is limited to reports from a handful of centers. This study was designed to summarize donor, recipient, and transplant center characteristics and posttransplant outcomes for the indication of CRLM.

METHODS: Adult, primary LT patients listed between December 2017 and March 2022 were identified by using United Network Organ Sharing database. LT for CRLM was identified from variables: “DIAG_OSTXT”; “DGN_OSTXT_TCR”; “DGN2_OSTXT_TCR”; and “MALIG_TY_OSTXT.”

RESULTS: During this study period, 64 patients were listed, and 46 received LT for CRLM in 15 centers. Of 46 patients who underwent LT for CRLM, 26 patients (56.5%) received LTs using living donor LT (LDLT), and 20 patients received LT using deceased donor (DDLT) (43.5%). The median laboratory MELD-Na score at the time of listing was statistically similar between the LDLT and DDLT groups (8 vs. 9, P = 0.14). This persisted at the time of LT (8 vs. 12, P = 0.06). The 1-, 2-, and 3-year, disease-free, survival rates were 75.1, 53.7, and 53.7%. Overall survival rates were 89.0, 60.4, and 60.4%, respectively.

CONCLUSIONS: This first comprehensive U.S. analysis of LT for CRLM suggests a burgeoning interest in high-volume U.S. transplant centers. Strategies to optimize patient selection are limited by the scarce oncologic history provided in UNOS data, warranting a separate registry to study LT in CRLM.

PMID:36719568 | DOI:10.1245/s10434-023-13147-6

Med Biol Eng Comput. 2023 Jan 31. doi: 10.1007/s11517-023-02770-w. Online ahead of print.

ABSTRACT

In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.

PMID:36719563 | DOI:10.1007/s11517-023-02770-w

J Racial Ethn Health Disparities. 2023 Jan 31. doi: 10.1007/s40615-023-01523-7. Online ahead of print.

ABSTRACT

INTRODUCTION: Despite increased attention to the societal consequences of aggressive policing, the focus on rarer instances of deaths/severe injuries fails to fully capture the day-to-day experiences that racially minoritized groups face during police encounters (PEs). We explored differential vulnerability by race/ethnicity in the relationship between PEs and cardiovascular disease (CVD) risk.

METHODS: Using data from the National Longitudinal Study of Adolescent to Adult Health, we regressed the Framingham 30-Year CVD risk score on a high number of lifetime PEs (6 + among men and 2 + among women). To explore differential vulnerability by race, we added an interaction between PEs and race/ethnicity. We also examined sex- and race and sex-stratified models.

RESULTS: We observed no association between PEs and CVD risk in the sample overall, but the interaction between PEs and race/ethnicity was statistically significant. In race stratified models, we found that higher PEs were associated with a lower CVD risk among Black respondents, whereas among White respondents there was no relationship. In the sex-stratified analysis, reporting higher PEs was associated with lower CVD risk among men, while among women there was no relationship. In sex- and race-stratified models, higher PEs was associated with lower CVD risk among Black men and higher CVD risk among White women, while there was no association among Black women and White men.

CONCLUSION: The association between PEs and CVD risk depends on race/ethnicity and sex. More work is needed to understand the counterintuitive finding that high PEs are associated with lower CVD risk among Black men.

PMID:36719543 | DOI:10.1007/s40615-023-01523-7

Int Urol Nephrol. 2023 Jan 31. doi: 10.1007/s11255-023-03487-3. Online ahead of print.

ABSTRACT

CONTEXT: Prostate cancer (PCa) is the second largest male tumor in the world and one of the most common malignant tumors in the urinary system. In recent years, the incidence rate of PCa in China has been increasing year by year. Meanwhile, refractory hormone resistance and adverse drug reactions of advanced PCa cause serious harm to patients.

OBJECTIVE: The present study aims to systematically review the recent advances in molecularly targeted drugs for prostate cancer and to use the retrieval and analysis of the literature library to summarize the adverse effects of different drugs so as to maximize the treatment benefits of targeted therapies.

EVIDENCE ACQUISITION: We performed a systematic literature search of the Medline, EMBASE, PubMed, and Cochrane databases up to March 2022 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Medical Subject Heading (MeSH) terms and keywords such as (prostate cancer) AND (molecular target drugs) AND (side effect) were used. No language restrictions were set on the search process, and all these results were processed independently by two authors. Consensus was reached through discussion once met with any disagreements. The primary endpoint was differential features between different molecular targeted drugs. Secondary endpoints were side effects of different drugs on the body and corresponding prognostic values.

EVIDENCE SYNTHESIS: The Cochrane Collaboration risk of bias tool was used to assess the study quality in terms of sequence generation, allocation concealment, blinding, the completeness of outcome data, selective reporting and other biases. We retrieved 332 articles, of which 49 met the criteria for inclusion. Included studies show that prostatic tumor cells, tumor neovascularization and immune checkpoints are the main means for targeted therapy. Common drugs include 177 Lu-PSMA, Olaparib, Rucaparib, Bevacizumab, Pazopanib, Sorafenib, Cabozantinib, Aflibercept, Ipilimumab, Atezolizumab, Avelumab, Durvalumab. A series of publicly available data suitable for further analysis of side effects. An over-representation analysis of these datasets revealed reasonable dosage and usage is the key to controlling the side effects of targeted drugs. Important information such as the publication year, the first author, location and outcome observation of adverse effects was extracted from the original article. If the study data has some insufficient data, contacting the corresponding authors is necessary. All the studies included prospective nonrandomized and randomized research. Retrospective reviews were also screened according to the relevant to the purpose of this study. Meeting abstracts as well as letters to the editor and editorials were excluded.

STATISTICAL ANALYSIS: Data analysis was based on Cochrane’s risk of bias tools to obtain the quality assessment. The included randomized studies used RoB2 and non-randomized ones corresponded to ROBINS-I. Standardized mean differences (SMD) were used to determine relative risk (RR) and side effects between groups. The eggers’ test was used to check the publication bias from variable information in the included studies. All p < 0.05 were considered to be significant, and 95% was set as the confidence interval.

CONCLUSIONS: With the approval of a variety of targeted drugs, targeted therapy will be widely used in the treatment of advanced or metastatic prostate cancer. Despite the existence of adverse reactions related to targeted drug treatment, it is still meaningful to adjust the drug dosage or treatment cycle to reduce the occurrence of adverse reactions, improving the treatment benefits of patients.

PMID:36719528 | DOI:10.1007/s11255-023-03487-3

Int J Biometeorol. 2023 Jan 31. doi: 10.1007/s00484-022-02422-y. Online ahead of print.

ABSTRACT

Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use of a random forest classifier was explored to analyze the relative importance of hydrometeorological indices in developing the leptospirosis model and to evaluate the performance of models based on the type of indices used, using case data from three districts in Kelantan, Malaysia, that experience annual monsoonal rainfall and flooding. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity, and temperature were transformed into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, weekly case occurrences were classified into binary classes “high” and “low” based on an average threshold. Seventeen models based on “average,” “extreme,” and “mixed” indices were trained by optimizing the feature subsets based on the model computed mean decrease Gini (MDG) scores. The variable importance was assessed through cross-correlation analysis and the MDG score. The average and extreme models showed similar prediction accuracy ranges (61.5-76.1% and 72.3-77.0%) while the mixed models showed an improvement (71.7-82.6% prediction accuracy). An extreme model was the most sensitive while an average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The rainfall variable (extreme) was the most important in classifying the leptospirosis occurrence while streamflow was the least important despite showing higher correlations with leptospirosis.

PMID:36719482 | DOI:10.1007/s00484-022-02422-y

Discov Oncol. 2023 Jan 31;14(1):13. doi: 10.1007/s12672-023-00622-5.

ABSTRACT

BACKGROUND: Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival.

METHODS: CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool.

RESULTS: The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction.

CONCLUSIONS: Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented ( unipd.link/melanomaprediction ). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model’s accuracy beyond the original research context.

PMID:36719475 | DOI:10.1007/s12672-023-00622-5