Category: Nevin Manimala

F1000Res. 2021 Oct 12;10:1037. doi: 10.12688/f1000research.73497.1. eCollection 2021.

ABSTRACT

Background: Lack of reproducibility in preclinical research is a problem posing ethical and economic challenges for biomedical science. Various institutional activities from society stakeholders of leading industry nations are currently underway to improve the situation. Such initiatives usually attempt to tackle high-level organisational issues and do not typically focus on improving experimental approaches per se. Addressing these is necessary in order to increase consistency and success rates of lab-to-lab repetitions. Methods: In this project, we statistically evaluated repetitive data of a very basic and widely applied lab procedure, namely quantifying the number of viable cells. The purpose of this was to appreciate the impact of different parameters and instrumentations that may constitute sources of variance in this procedure. Conclusion: By comparing the variations of data acquired under two different procedures, featuring improved stringency of protocol adherence, our project attempts to propose guidelines on how to reduce such variations. We believe our work can contribute to tackling the repeatability crisis in biomedical research.

PMID:35035893 | PMC:PMC8749900 | DOI:10.12688/f1000research.73497.1

J Healthc Eng. 2022 Jan 7;2022:9654919. doi: 10.1155/2022/9654919. eCollection 2022.

ABSTRACT

To analyze the causes of muscle soreness and injury during precompetition training in university sports meet and taking the DOMS mechanism as the main line to find a reasonable way to deal with the muscle pain and prevent the injury, 125 college students participating in stadium games training were randomly selected. The muscle pain and injury during the training were obtained through interviews, mathematical statistics, and literature review. The information of exercise load, pain and injury type, exercise ability, pain degree, and recovery time was comprehensively analyzed to study the mechanism of pain and injury formation. Muscle pain and injury occurred in precompetition training, especially in freshmen. After heavy load, muscle soreness occurred, causing DOMS and developing into muscle injury. Affected by the external climate environment, sudden muscle soreness and injury are a gradual transformation process with DOMS as the boundary, which is the comprehensive result of exercise load, water, energy, and material metabolism; control load intensity, water supplement, and energy and material supplement can effectively prevent the occurrence of DOMS, and timely recovery after DOMS symptoms can effectively avoid the occurrence of sports injury. According to the different intensity of exercise, it is of great significance to clarify the mechanism of DOMS and explore effective prevention methods for physical education and sports training.

PMID:35035865 | PMC:PMC8759844 | DOI:10.1155/2022/9654919

J Healthc Eng. 2022 Jan 6;2022:9506915. doi: 10.1155/2022/9506915. eCollection 2022.

ABSTRACT

This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients’ compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group (P < 0.05). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment (P < 0.05), and the scores of the quality of life in the intervention group were higher than those in the control group (P < 0.05). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group (P < 0.05). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group (P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group (P < 0.05).

PMID:35035864 | PMC:PMC8758290 | DOI:10.1155/2022/9506915

J Healthc Eng. 2022 Jan 7;2022:9048123. doi: 10.1155/2022/9048123. eCollection 2022.

ABSTRACT

The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference (P < 0.05). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference (P < 0.05). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor’s disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.

PMID:35035863 | PMC:PMC8759862 | DOI:10.1155/2022/9048123

J Healthc Eng. 2022 Jan 7;2022:8414135. doi: 10.1155/2022/8414135. eCollection 2022.

ABSTRACT

The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance (P < 0.05). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance (P < 0.05). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.

PMID:35035861 | PMC:PMC8759879 | DOI:10.1155/2022/8414135

J Healthc Eng. 2022 Jan 5;2022:7840852. doi: 10.1155/2022/7840852. eCollection 2022.

ABSTRACT

Patients who develop heart failure (HF) after an acute myocardial infarction (AMI) are at higher risk of adverse fatal and nonfatal outcomes. Studies have shown sacubitril/valsartan can further reduce the risk of cardiovascular death or hospitalization for heart failure by 20% compared with enalapril. At the same time, its tolerance and safety are better. However, the current evidence regarding the efficacy of sacubitril/valsartan in patients with heart failure after acute myocardial infarction is controversial. To assess the effect of sacubitril/valsartan on heart failure after acute myocardial infarction, we conducted a systematic review of the literature and a meta-analysis of existing randomized clinical trials. Meta-analysis of randomized controlled trails is used where data are collected from PubMed, the Cochrane library, Embase, and Web of Science. Data about sacubitril/valsartan were available from 5 studies. Forest plots showed that the sacubitril/valsartan group had a 299% higher value of sacubitril/valsartan to the control group (MD = 2.99%, 95% CI: 2.01, 3.96, I ² = 78%, P < 0.00001, Figure 2), and the difference was statistically significant. Forest plots showed that the sacubitril/valsartan group had a 531% lower value of LVEF to the control group (MD = -5.31%, 95% CI: -7.36, -3.26, I ² = 91%, P < 0.00001, Figure 2), and the difference was statistically significant. Forest plots showed that the sacubitril/valsartan group had a 133% lower value of NT-proBNP to the control group (MD = -1.33%, 95% CI: -1.54, -1.12, I ² = 96%, P < 0.00001, Figure 3). Forest plots showed that the sacubitril/valsartan group had a 49% lower risk of heart failure to the control group (MD = 0.49, 95% CI: 0.27, 0.89, I ² = 0%, P=0.02, Figure 3). The patients in experimental showed an obviously lower OR of MACE (OR = 0.47, 95% CI: 0.27, 0.82, P=0.007, Figure 3). The data were statistically significant. We have observed that for patients with heart failure after acute myocardial infarction, early administration of sacubitril/valsartan can significantly reduce the incidence of heart rate, left ventricular ejection fraction, NT-proBNP, and MACE. Our meta-analysis suggests that taking sacubitril/valsartan is relatively safe and effective, especially if started early after acute myocardial infarction.

PMID:35035857 | PMC:PMC8754592 | DOI:10.1155/2022/7840852

J Cosmet Dermatol. 2022 Jan 16. doi: 10.1111/jocd.14777. Online ahead of print.

ABSTRACT

UV absorption properties of bioactive agents has lead to their screening and development to provide photo protection. Safranal is one such secondary plant metabolite obtained from Saffron (Crocus sativus) and reported to possess antioxidant and antisolar properties. The objective of this research was to design a suitable delivery system for the topical delivery of Safranal and to develop broad spectrum polyherbal sun protection cream with mild photoprotection. Safranal loaded Solid Lipid Nanoparticles (SLN) were formulated using probe sonication technique. The effect of variables like lipid concentration, surfactant concentration and stirring time were studied using central composite design using Design Expert 7.0 (Stat-Ease, Inc, USA). Particle size analysis of prepared SLN revealed the particles in the range of 460nm-980nm (F9). Entrapment efficiency was found between 88% and 99% . SLN was further characterized by techniques like DSC, FTIR and TEM. These SLN were combined with zinc oxide, pearl powder, Pterocarpus santalinus; a natural colorant with skin whitening effect and dispersed in a dermatological acceptable carrier with excellent skin nourishing properties. Other natural ingredients namely Almond oil, Hen egg oil and Aloevera gel were also incorporated due to their Sun protection properties. Evaluation of sunscreen cream by transmittane method showed good texture, excellent rheological properties, optimum pH and stability. The developed product showed broad spectrum of sunscreen protection with SPF 9.22, UV-A ++ and *** Boot Star Rating. The significant inhibitory activity of Safranal on matrix metalloproteinases (MMPs) analyzed by biochemical Investigation method and a higher SPF established that this bioorganic molecule is a strong photoprotective agent. Designing of Solid lipid nanoparticles and incorporation of other traditional ingredients in the recipe augmented its antisolar property and provided an all natural sunscreen.

PMID:35034408 | DOI:10.1111/jocd.14777

Echocardiography. 2022 Jan 15. doi: 10.1111/echo.15292. Online ahead of print.

ABSTRACT

AIMS: This study aimed to explore the validation and the diagnostic value of multiple right ventricle (RV) volumes and functional parameters derived from a novel artificial intelligence (AI)-based three-dimensional echocardiography (3DE) algorithm compared to cardiac magnetic resonance (CMR).

METHODS AND RESULTS: A total of 51 patients with a broad spectrum of clinical diagnoses were finally included in this study. AI-based RV 3DE was performed in a single-beat HeartModel mode within 24 hours after CMR. In the entire population, RV volumes and right ventricular ejection fraction (RVEF) measured by AI-based 3DE showed statistically significant correlations with the corresponding CMR analysis (p < 0.05 for all). However, the Bland-Altman plots indicated that these parameters were slightly underestimated by AI-based 3DE. Based on CMR derived RVEF < 45% as RV dysfunction, end-systolic volume (ESV), end-systolic volume index (ESVi), stroke volume (SV), and RVEF showed great diagnostic performance in identifying RV dysfunction, as well as some non-volumetric parameters, including tricuspid annular systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strains (LS) (p < 0.05 for all). The cutoff value was 43% for RVEF with a sensitivity of 94% and specificity of 67%.

CONCLUSION: AI-based 3DE could provide rapid and accurate quantitation of the RV volumes and function with multiple parameters. Both volumetric and non-volumetric measurements derived from AI-based 3DE contributed to the identification of the RV dysfunction.

PMID:35034377 | DOI:10.1111/echo.15292

Hepatology. 2022 Jan 16. doi: 10.1002/hep.32347. Online ahead of print.

ABSTRACT

We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with hepatitis C virus (HCV) infection. We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pre-treatment predictors (n=179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression (MLR) as well as elastic net (EN), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) machine learning algorithms to predict DAA treatment failure. Training (n=4,894) and validation (n=1,631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57 years; 60% male; 66% White; 36% with cirrhosis). Of 6525 HCV-infected adults, 95.3% achieved sustained virologic response, whereas 4.7% experienced DAA treatment failure. In the validation sample, machine learning approaches performed similarly in predicting DAA treatment failure (C statistic [95% CI]: GBM, 0.69 [0.64-0.74]; RF, 0.68 [0.63-0.73]; FNN, 0.66 [0.60-0.71]; EN, 0.64 [0.59-0.70]), and all four outperformed MLR (0.51 [0.46-0.57]). Using the Youden index to identify the balanced risk score threshold, GBM had 66.2% sensitivity and 65.1% specificity, and 12 individuals were needed to evaluate to identify one DAA treatment failure. Over 55% of patients with treatment failure were classified by the GBM in the top three risk decile subgroups (positive predictive value: 6% to 14%). The top 10 GBM-identified predictors included albumin, liver enzymes (aspartate aminotransferase, alkaline phosphatase), total bilirubin levels, sex, HCV viral loads, sodium level, hepatocellular carcinoma, platelet levels, and tobacco use. CONCLUSIONS: Machine learning algorithms performed effectively for risk prediction and stratification of DAA treatment failure.

PMID:35034373 | DOI:10.1002/hep.32347

Biometrics. 2022 Jan 16. doi: 10.1111/biom.13622. Online ahead of print.

ABSTRACT

The restricted mean survival time (RMST) evaluates the expectation of survival time truncated by a prespecified time point, because the mean survival time in presence of censoring is typically not estimable. The frequentist inference procedure for RMST has been widely advocated for comparison of two survival curves, while research from the Bayesian perspective is rather limited. For the RMST of both right- and interval-censored data, we propose Bayesian nonparametric estimation and inference procedures. By assigning a mixture of Dirichlet processes (MDP) prior to the distribution function, we can estimate the posterior distribution of RMST. We also explore another Bayesian nonparametric approach using the Dirichlet process mixture model and make comparisons with the frequentist nonparametric method. Simulation studies demonstrate that the Bayesian nonparametric RMST under diffuse MDP priors leads to robust estimation and under informative priors it can incorporate prior knowledge into the nonparametric estimator. Analysis of real trial examples demonstrates the flexibility and interpretability of the Bayesian nonparametric RMST for both right- and interval-censored data. This article is protected by copyright. All rights reserved.

PMID:35034347 | DOI:10.1111/biom.13622