Tag: pubmed

Ticks Tick Borne Dis. 2021 Sep 4;12(6):101822. doi: 10.1016/j.ttbdis.2021.101822. Online ahead of print.

ABSTRACT

Epidemiological data often include characteristics such as spatial and/or temporal dependencies and excess zero counts, which pose modeling challenges. Excess zeros in such data may arise from imperfect detection and/or relative rareness of the disease in a given location. Here, we studied the spatio-temporal variation in annual Lyme disease cases in Virginia from 2001-2016 and modeled the disease with a spatio-temporal hierarchical Bayesian model. Using observed ecological and environmental covariates, we constructed a predictive model for the disease spread over space and time, including spatial and temporal random effects. We considered several different models and found that the negative binomial hurdle model performs the best for such epidemiological data. Among the various ecological predictors, the North-South (V component) of winds and relative humidity significantly contributed to predicting the Lyme cases. Our model results provide important insights on the spread of the disease in Virginia and the proposed modeling framework offers epidemiologists and health policymakers a useful tool for improving disease preparedness and control plans for the future.

PMID:34555712 | DOI:10.1016/j.ttbdis.2021.101822

Physiotherapy. 2021 Apr 27;113:12-28. doi: 10.1016/j.physio.2021.04.005. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this systematic review and meta-analysis was to assess the effect of aerobic, resistance and combined exercise training in patients with ALS.

DATA SOURCE: A comprehensive systematic search of CENTRAL, CINAHL, SPORTDiscuss, Embase, Scopus, ProQuest was performed from inception to February 2021.

ELIGIBILITY CRITERIA: The systematic review included all studies that examined the effect of exercise training in ALS patients. Meta-analysis was also carried out on randomized controlled trials (RCTs).

DATA EXTRACTION AND DATA SYNTHESIS: Data related to primary outcomes (functional ability, respiratory function, fatigue, pain, quality of life, upper-body strength, lower-body strength and Vo₂peak) and secondary outcomes (adverse events and feasibility of exercises) was extracted from all studies and systematically reviewed.

RESULTS: 16 trials including 532 patients met the inclusion criteria; of these, eight studies were included in this meta-analysis. The meta-analysis found a statistically significant difference in favor of exercise in functional ability (P=0.001), overall quality of life (P=0.03) and Vo₂peak (P=0.01). The included trials were generally of poor quality and had a risk of bias. However, the results of sensitivity analysis, after omitting studies with high risk of bias, showed no statistically significant difference in functional ability (P=0.05), overall quality of life (P=0.12) and Vo2peak (P=0.13). Finally, no significant difference was found in respiratory function, fatigue, pain, and upper-body and lower-body strength.

CONCLUSIONS: The safety and effectiveness of exercise therapy in ALS patients remains unclear and further high quality RCTs with larger sample size are needed. Systematic Review Registration Number PROSPERO CRD42019140011.

PMID:34555670 | DOI:10.1016/j.physio.2021.04.005

Neoplasia. 2021 Sep 20;23(10):1059-1068. doi: 10.1016/j.neo.2021.08.004. Online ahead of print.

ABSTRACT

Interferon (IFN) therapy has been the standard of care for a variety of cancers for decades due to the pleiotropic actions of IFNs against malignancies. However, little is known about the role of copy number alteration (CNA) of the IFN gene cluster, located at the 9p21.3, in cancer. This large individual patient data meta-analysis using 9937 patients obtained from cBioportal indicates that CNA of the IFN gene cluster is prevalent among 24 cancer types. Two statistical approaches showed that notably deletion of this cluster is significantly associated with increased mortality in many cancer types particularly uterus (OR = 2.71), kidney (OR = 2.26), and brain (OR = 2.08) cancers. The Cancer Genome Atlas PanCancer analysis also showed that CNA of the IFN gene cluster is significantly associated with decreased overall survival. For instance, the overall survival of patients with brain glioma reduced from 93m (diploidy) to 24m (with the CNA of the IFN gene). In conclusion, the CNA of the IFN gene cluster is associated with increased mortality and decreased overall survival in cancer. Thus, in the prospect of immunotherapy, CNA of IFN gene may be a useful biomarker to predict the prognosis of patients and also as a potential companion diagnostic test to prescribe IFN α/β therapy.

PMID:34555656 | DOI:10.1016/j.neo.2021.08.004

Forensic Sci Int. 2021 Aug 30;327:110987. doi: 10.1016/j.forsciint.2021.110987. Online ahead of print.

ABSTRACT

Human gaits are the patterns of limb movements which involve both the upper and lower body parts. These patterns in terms of step rate, gait speed, stance widening, stride, and bipedal forces are influenced by different factors including environmental (such as social, cultural, and behavioral traits) and physical changes (such as age and health status). These factors are reflected on the imprinted shoeprints generated with body forces, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender/sex classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations of forces on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait pattern disorders, biometrics, and sport studies.

PMID:34555663 | DOI:10.1016/j.forsciint.2021.110987

J Mech Behav Biomed Mater. 2021 Sep 15;124:104838. doi: 10.1016/j.jmbbm.2021.104838. Online ahead of print.

ABSTRACT

To develop antimicrobial restorative materials for root caries, we assessed a 4-META/MMA-TBB resin (Bondfill SB Plus, Sun Medical) containing benzalkonium chloride (BAC) or cetylpyridinium chloride (CPC) at 1.25, 2.5, and 5.0 wt%. The same resin without antibacterial agent was used as control. The degree of conversion was measured by attenuated total reflectance-Fourier transform infrared spectroscopy. The 3-point flexural strength test was conducted according to ISO 4049. The antimicrobial effect against three oral bacteria (Streptococcus mutans, S. sobrinus, and Actinomyces naeslundii) was assessed using agar diffusion tests. The shear bond strength to root dentin was assessed after 24 h of storage in water with or without 10,000 thermal cycles. The shear bond strength data were statistically compared using a linear mixed-effects model (α = 0.05). The specimen with 5.0 wt% BAC showed a significantly higher degree of conversion than the control, but it also had significantly lower flexural strength and lower shear bond strength after thermal cycling than the other specimens. When BAC or CPC was added at ≥ 2.5 wt%, the resins inhibited the growth of the three investigated microbes. In conclusion, both BAC and CPC showed significant antimicrobial effects when added at 5.0 wt% to the 4-META/MMA-TBB resin. Up to 2.5 wt%, neither antimicrobial agent affected the degree of conversion, flexural strength, or shear bond strength of the resin.

PMID:34555621 | DOI:10.1016/j.jmbbm.2021.104838

Eur J Cancer. 2021 Sep 20;157:278-290. doi: 10.1016/j.ejca.2021.08.014. Online ahead of print.

ABSTRACT

INTRODUCTION: Which neoadjuvant treatment for locally advanced thoracic oesophagus (TE) or gastro-oesophageal junction carcinoma is best remains an open question. Randomised controlled trials variously accrued patients with adenocarcinoma and squamous cell carcinoma, making strong conclusions hard to obtain. The primary objective of this individual participant data meta-analysis was to investigate the effect of neoadjuvant chemotherapy on overall survival (OS).

PATIENTS AND METHODS: Eligible trials should have closed to accrual before 2016 and compared neoadjuvant chemotherapy and surgery (CS) to surgery alone. All relevant published and unpublished trials were identified via searches of electronic databases, conference proceedings and clinical trial registers. The main end-point was OS. Investigators were contacted to obtain the individual patient data, which was recorded, harmonised and checked. A random-effects Cox model, stratified by trial, was used for meta-analysis and subgroup analyses were preplanned.

RESULTS: 16 trials were identified as eligible. Individual patient data were obtained from 12 trial and 2478 patients. CS was associated with an improved OS versus surgery, hazard ratio (HR) = 0.83 [0.72-0.96], p < 0.0001, translating to an absolute benefit of 5.7% at 5-years from 16.8% to 22.5%. Treatment effects did not vary substantially between adenocarcinoma (HR = 0.73 [0.62-0.87]) and squamous cell carcinoma (HR = 0.91 [0.76-1.08], interaction p = 0.26). A somewhat more pronounced effect was observed in gastro-oesophageal junction (HR = 0.68 [0.50-0.93]) versus TE (HR = 0.87 [0.75-1.00], interaction p = 0.07). CS was also associated with a greater disease-free survival (HR = 0.74 [0.64-0.85], p < 0.001).

CONCLUSIONS: Neoadjuvant chemotherapy conferred a better OS than surgery alone and should be considered in all anatomical location and histological subtypes.

PMID:34555647 | DOI:10.1016/j.ejca.2021.08.014

Redox Biol. 2021 Sep 16;47:102138. doi: 10.1016/j.redox.2021.102138. Online ahead of print.

ABSTRACT

Growing evidence implicates an association between psychosocial stress and oxidative stress (OxSt) although there are not yet reliable biomarkers to study this association. We used a Trier Social Stress Test (TSST) and compared the response of a healthy control group (HC; N=10) against the response of a schizophrenia group (SCZ; N=10) that is expected to have higher levels of OxSt. Because our previous study showed inconsistent changes in conventional molecular markers for stress responses in the neuroendocrine and immune systems, we analyzed the same serum samples using a separate reducing capacity assay that provides a more global measurement of OxSt. This assay uses the moderately strong oxidizing agent iridium (Ir) to probe a sample’s reducing capacity. Specifically, we characterized OxSt by this Ir-reducing capacity assay (Ir-RCA) using two measurement modalities (optical and electrochemical) and we tuned this assay by imposing an input voltage sequence that generates multiple output metrics for data-driven analysis. We defined five OxSt metrics (one optical and four electrochemical metrics) and showed: (i) internal consistency among each metric in the measurements of all 40 samples (baseline and post TSST for N=20); (ii) all five metrics were consistent with expectations of higher levels of OxSt for the SCZ group (three individual metrics showed statistically significant differences); and (iii) all five metrics showed higher levels of OxSt Post-TSST (one metric showed statistically significant difference). Using multivariant analysis, we showed that combinations of OxSt metrics could discern statistically significant increases in OxSt for both the SCZ and HC groups 90 min after the imposed acute psychosocial stress.

PMID:34555595 | DOI:10.1016/j.redox.2021.102138

Comput Methods Programs Biomed. 2021 Sep 16;211:106420. doi: 10.1016/j.cmpb.2021.106420. Online ahead of print.

ABSTRACT

OBJECTIVE: Hepatic encephalopathy (HE) is among the most common complications of cirrhosis. Data for cirrhosis with HE is typically unbalanced. Traditional statistical methods and machine learning algorithms thus cannot identify a few classes. In this paper, we use machine learning algorithms to construct a risk prediction model for liver cirrhosis complicated by HE to improve the efficiency of its prediction.

METHOD: We collected medical data from 1,256 patients with cirrhosis and performed preprocessing to extract 81 features from these irregular data. To predict HE in cirrhotic patients, we compared several classification methods: logistic regression, weighted random forest (WRF), SVM, and weighted SVM (WSVM). We also used an additional 722 patients with cirrhosis for external validation of the model.

RESULTS: The WRF, WSVM, and logistic regression models exhibited better recognition ability for patients with HE than traditional machine learning models (sensitivity> 0.70), but their ability to identify patients with uncomplicated HE was slightly lower (specificity approximately 85%). The comprehensive evaluation index of the traditional model was higher than those of other models (G-means> 0.80 and F-measure> 0.40). For the WRF, the G-means (0.82), F-measure (0.46), and AUC (0.82) were superior to those of the logistic regression and WSVM models, which means that it can better predict the incidence of HE in patients.

CONCLUSION: The WRF model is more suitable for the classification of unbalanced medical data and can be used to construct a risk prediction and evaluation system for liver cirrhosis complicated with HE. The probabilistic prediction models of WRF can help clinicians identify high-risk patients with HE.

PMID:34555589 | DOI:10.1016/j.cmpb.2021.106420

Comput Methods Programs Biomed. 2021 Sep 16;211:106418. doi: 10.1016/j.cmpb.2021.106418. Online ahead of print.

ABSTRACT

Backgound and Objective: Detecting differentially expressed genes is an important step in genome wide analysis and expression profiling. There are a wide array of algorithms used in today’s research based on statistical approaches. Even though the current algorithms work, they sometimes miss-predict. There is no framework available for measuring the quality of current algorithms. New machine learning methods (like gradient boost and deep neural networks) were not used to solve this problem. The Gene-Bench open source python package addresses these issues by providing an evaluation and data handling system for differentially expressed genes detection algorithms on microarray data. We also provide MIDGET, a new group of algorithms based on state of the art machine learning approaches Methods: The Gene-Bench package provides data collected from real experiments that consists of 73 transcription-factor perturbation experiments with validation data from Chip-seq experiments and 129 drug perturbation experiments, synthetic data generated with our own method and three evaluation metrics (Kolmogorov, F1 and AUC/ROC). Besides the data and metrics, Gene-Bench also contains well-known algorithms and a new method to identify differentially expressed genes, called MIDGET: Machine learning Identification Differential Gene Expression Tool that is using big-data and machine learning methods to identify differentially expressed genes. The two new groups of machine learning algorithms provided in our package use extreme gradient boosting and deep neural networks to achieve their results. Results: The Gene-Bench package is highly flexible, allows fast prototyping and evaluating of new and old algorithms and provides multiple new machine-learning algorithms (called MIDGET) that perform better on all evaluation metrics than all the other tested alternatives. While everything provided in Gene-Bench is algorithm independent, the user can also use algorithms implemented in the R language even though the package is written in Python. Conclusions: The Gene-Bench package fills a gap in evaluating and benchmarking differential gene detection algorithms. It also provides machine learning methods that perform detection with higher accuracy in all tested metrics. It is available at https://github.com/raduangelescu/GeneBench/ and can be directly installed from the Python Package Index using pip install genebench.

PMID:34555591 | DOI:10.1016/j.cmpb.2021.106418

Chemosphere. 2021 Sep 16;287(Pt 3):132268. doi: 10.1016/j.chemosphere.2021.132268. Online ahead of print.

ABSTRACT

Dissolved salts, colloidal particles, and active microorganisms in brackish surface water distribution systems (BSWD) cause multiple fouling, poses potential threat to the environmental pollution, and raising technical and economic issues as well. So far, the co-occurrence and interactions of multiple fouling remains largely unknown. Multiple fouling behaviors were assessed in agriculture BSWD under different nitrogen (N) fertilizers. X-ray diffraction, Rietveld refinement analysis, 16S rRNA, and microbial network analysis were conducted to determine the fouling characteristics. Statistical analysis was applied to reveal the relative contributions and interaction of multiple fouling. Our results demonstrated, multiple fouling of precipitates, particulates and biofoulings were co-occurred. Fouling growth was largely attributed to the strong interactions of different fouling. The binary interactions of precipitates – particulates contributed 51.1%, and ternary interactions of precipitates – particulates – biofouling contributed 25.4% to explain the decline of system performance, while the contribution of each single type fouling was minimal. Thereby indicating the significant role of calcium silica, biomineralization and bio-silicates in fouling. The lower acid N fertilizer broken the interaction of multiple fouling by increasing the precipitate crystal parameters and repulsive forces amongst particulates, as well as destroyed microbial interactions in biofouling. Overall, this study open frontier for multiple fouling in-depth profiling and antifouling guidance for effective utilization of BSWD.

PMID:34555585 | DOI:10.1016/j.chemosphere.2021.132268