Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

BACKGROUND AND AIM: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. METHODS: In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. RESULTS: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. CONCLUSIONS: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Association between anemia and depression: results from NHANES 2005-2018 and mendelian randomization analyses.

BACKGROUND: The relationship between anemia and depression remains controversial. OBJECTIVE: To explore the association between anemia/hemoglobin and depression. METHODS: The data for our cross-sectional study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Weighted multivariate logistic regression was performed to examine the association between anemia/hemoglobin and depression. Inverse variance weighted (IVW), weighted-median, and MR-Egger were used in MR analyses to assess the causal relationship between anemia/hemoglobin and depression. Heterogeneity and directional pleiotropy were assessed using the Cochrane Q test and Egger-intercept test, respectively. Sensitivity analysis was conducted by the leave-one-out approach. All analyses were carried out using IBM SPSS 24.0 and R version 4.2.2. RESULTS: A total of 29,391 NHANES participants were included in this study. After adjusting for all covariates, the association between anemia/hemoglobin and depression was not significant (P < 0.05). IVW estimates revealed that broad anemia had no significant effect on the risk of depression (OR = 1.00, 95% CI = 0.99-1.01, P = 0.432). Findings of weighted median and MR-Egger were consistent with those from IVW (weighted median: OR = 1.00, 95% CI = 0.99-1.02; P = 0.547; MR-Egger: OR = 1.01, 95% CI = 0.98-1.03, P = 0.605). The results of three MR Analyses methods also showed no causal association between hemoglobin and depression. CONCLUSIONS: Our findings do not support a causal association between anemia and depression. The association between hemoglobin concentration and depression was not statistically significant either.

Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis.

PURPOSE: Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). METHODS: The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. RESULTS: We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95). CONCLUSION: ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018.

BACKGROUND: Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations. METHODS: Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score. RESULTS: Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors. CONCLUSIONS: Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.

Accuracy of clustered regularly interspaced short palindromic repeats (CRISPR) to diagnose COVID-19, a meta-analysis.

OBJECTIVE: To estimate the accuracy of clustered regularly interspaced short palindromic repeats (CRISPR) in determining coronavirus disease-19 (COVID-19). METHODS: As of January 31, 2022, PubMed, Web of Science, Embase, Science Direct, Wiley and Springer Link were searched. Sensitivity, specificity, likelihood ratio (LR), diagnostic odds ratio (DOR) and area under the summary receiver-operating characteristic (AUC) curve were used to assess the accuracy of CRISPR. RESULTS: According to the inclusion criteria, 5857 patients from 54 studies were included in this meta-analysis. The pooled sensitivity, specificity and AUC were 0.98, 1.00 and 1.00, respectively. For CRISPR-associated (Cas) proteins-12, the sensitivity, specificity was 0.96, 1.00, respectively. For Cas-13, the sensitivity and specificity were 0.99 and 0.99. CONCLUSION: This meta-analysis showed that the diagnostic performance of CRISPR is close to the gold standard, and it is expected to meet the Point of care requirements in resource poor areas.

Distant metastasis and prognostic factors in patients with invasive ductal carcinoma of the breast.

OBJECTIVE: To explore the risk factors and prognostic factors of invasive ductal carcinoma (IDC) and to predict the survival of IDC patients with metastasis. METHOD: We used multivariate logistic regression to identify independent risk factors affecting metastasis in IDC patients and used Cox regression to identify independent prognostic factors affecting the overall survival of patients with metastasis. Nomogram was used to predict survival, while C-index and calibration curves were used to measure the performance of nomogram. Kaplan-Meier method was used to calculate the survival curves of patients with different independent prognostics factors and different metastatic sites, and the differences were compared by log-rank test. The data of our study were obtained from the Surveillance, Epidemiology and End Results cancer registry. RESULT: Our study included 226,094 patients with IDC. In multivariate analysis, independent risk factors of metastasis included age, race, marital status, income, geographic region, grade, T stage, N stage, subtype, surgery and radiotherapy. Independent prognostic factors included age, race, marital status, income, geographic region, grade, T stage, N stage, subtype, surgery and chemotherapy. We established a nomogram, of which the C-index was 0.701 (0.693, 0.709), with the calibration curves showing that the disease-specific survival between actual observation and prediction had a good consistency. The survival curves of different metastatic patterns were significantly different (log-rank test: χ2  = 18784, p < 0.001; χ2  = 47.1, p < 0.001; χ2  = 20, p < 0.001). CONCLUSION: The nomogram we established may provide risk assessment and survival prediction for IDC patients with metastasis, which can be used for clinical decision-making and reference.

Trends in asthma among adults in the United States, National Health and Nutrition Examination Survey 2005 to 2018.

BACKGROUND: Asthma is a common chronic disease in American adults. The prevalence of asthma has varied over time, but there are few studies on the long-term trend of asthma in American adults. OBJECTIVE: To describe the prevalence and trend of asthma in American adults from 2005 to 2018 and analyze the risk factors for asthma. METHODS: Data collection was performed from National Health and Nutrition Examination Survey 2005 to 2018. The unweighted number and weighted percentages of normal participants and patients with asthma and the trends of asthma were calculated. Weighted univariate logistic regression was used to analyze the risk factors for asthma. RESULTS: A total of 39,601 adults were included in this study. From 2005 to 2018, the overall prevalence of asthma in American adults was 8.41%, whereas that in young, middle-aged, and elderly adults was 8.30%, 8.70%, and 7.92%, respectively. The estimated prevalence of asthma in the overall adults and young adults increased with time (P for trend = .03, difference = 0.023 and P for trend = .007, difference = 0.060, respectively), and the estimated prevalence of middle-aged and elderly adults remained stable with time (P for trend = .33, difference = 0.015 and P for trend = .80, difference = -0.024, respectively). CONCLUSION: Asthma in American adults was on the rise. Female sex, non-Hispanic Blacks, individuals with low annual household income, active smokers, obese patients, patients with hypertension, patients with diabetes, and individuals with positive asthma family history were associated with a higher risk for developing asthma.

Real-time quaking-induced conversion assay is accurate for Lewy body diseases: a meta-analysis.

OBJECTIVE: Real-time quaking-induced conversion (RT-QuIC) is a novel in vitro acellular seed amplification analysis and has been widely used to detect prion diseases. Due to the similar mechanism of abnormal aggregation of α-synuclein, RT-QuIC has great potential for diagnosing Lewy body diseases. This meta-analysis was performed to evaluate the diagnostic accuracy of RT-QuIC for Lewy body diseases. METHODS: This study followed the PRISMA statement. We searched six databases for relevant studies published until February 20, 2022. Meta-analysis was conducted using RevMan 5.3, Stata 17.0, and Meta-Disc 1.4. Subgroup analyses were performed to explore sources of heterogeneity. RESULTS: A total of 16 studies were included in this study. The pooled sensitivity and specificity were 0.91 (95%CI: 0.85-0.94) and 0.95 (95%CI: 0.90-0.97), respectively. The pooled positive and negative likelihood ratios were 17.16 (95% CI: 9.16-32.14) and 0.10 (95% CI: 0.06-0.17), respectively. The pooled diagnostic odds rate and area under the summary receiver operating characteristic curve were 171.16 (95% CI: 66.64-439.62) and 0.97 (95% CI: 0.96-0.99), respectively. CONCLUSIONS: This study was the first meta-analysis on RT-QuIC for Lewy body diseases. RT-QuIC is a reliable and accurate method to diagnose Lewy body diseases.

The inhibitory effect of selenium nanoparticles on protein glycation in vitro.

Selenium nanoparticles (Se NPs) possess well-known excellent biological activities and low toxicity, and have been employed for numerous applications except as inhibitors to protein glycation. Herein, the present study is carried out to investigate the inhibitory effect of Se NPs on protein glycation in a bovine serum albumin (BSA)/glucose system. By measuring the amount of glucose covalently bound onto BSA, the formation of fructosamine and fluorescent products, it is found that Se NPs can hinder the development of protein glycation in a dose-dependent but time-independent manner under the selected reaction conditions (55 °C, 40 h). And after comparing the increase of inhibitory rate in different stages, it is observed that Se NPs show the greatest inhibitory effect in the early stage, then in the advanced stage, but no effect in the intermediate stage. Fourier transform infrared spectroscopy characterization of Se NPs collected after glycation and determination of ·OH influence and glyoxal formation show that the mechanism for the inhibitory efficacy of Se NPs is related to their strong competitive activity against available amino groups in proteins, their great scavenging activity on reactive oxygen species and their inhibitory effect on α-dicarbonyl compounds' formation. In addition, it is proved that Se NPs protect proteins from structural modifications in the system and they do not exhibit significant cytotoxicity towards BV-2 and BRL-3A cells at low concentrations (10 and 50 µg mL(-1)). Consequently, Se NPs may be suitable for further in vivo studies as novel anti-glycation agents.

Pediatric urinary tract infections caused by poultry-associated Escherichia coli.

Escherichia coli is the leading cause of urinary tract infections (UTIs) in children and adults. The gastrointestinal tract is the primary reservoir of uropathogenic E. coli, which can be acquired from a variety of environmental exposures, including retail meat. In the current study, we used a novel statistical-genomic approach to estimate the proportion of pediatric UTIs caused by foodborne zoonotic E. coli strains. E. coli urine isolates were collected from DC residents aged 2 months to 17 years from the Children's National Medical Center Laboratory, 2013-2014. During the same period, E. coli isolates were collected from retail poultry products purchased from 15 sites throughout DC. A total of 52 urine and 56 poultry isolates underwent whole-genome sequencing, core genome phylogenetic analysis, and host-origin prediction by a Bayesian latent class model that incorporated data on the presence of mobile genetic elements (MGEs) among E. coli isolates from multiple vertebrate hosts. A total of 56 multilocus sequence types were identified among the isolates. Five sequence types-ST10, ST38, ST69, ST117, and ST131-were observed among both urine and poultry isolates. Using the Bayesian latent class model, we estimated that 19% (10/52) of the clinical E. coli isolates in our population were foodborne zoonotic strains. These data suggest that a substantial portion of pediatric UTIs in the Washington DC region may be caused by E. coli strains originating in food animals and likely transmitted via contaminated poultry meat.IMPORTANCEEscherichia coli UTIs are a heavy public health burden and can have long-term negative health consequences for pediatric patients. E. coli has an extremely broad host range, including humans, chickens, turkeys, pigs, and cattle. E. coli derived from food animals is a frequent contaminant of retail meat products, but little is known about the risk these strains pose to pediatric populations. Quantifying the proportion of pediatric UTIs caused by food-animal-derived E. coli, characterizing the highest-risk strains, and identifying their primary reservoir species could inform novel intervention strategies to reduce UTI burden in this vulnerable population. Our results suggest that retail poultry meat may be an important vehicle for pediatric exposure to zoonotic E. coli strains capable of causing UTIs. Vaccinating poultry against the highest-risk strains could potentially reduce poultry colonization, poultry meat contamination, and downstream pediatric infections.

Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: A systematic review and meta-analysis.

BACKGROUND: Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS. METHODS: PubMed, Web of Science, Cochrane Library, and Embase were used to search relevant studies published up to September 15, 2022. We used Review Manager 5.4, Meta-Disc 1.4, and STATA 16.0 for statistical analysis. RESULTS: The results displayed 18 studies with 782 patients included according to the inclusion and exclusion criteria. Meta-analysis showed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of OCT in the margin assessment of BCS were 0.91 (95% CI 0.88-0.93), 0.88 (95% CI 0.83-0.92), 7.53 (95% CI 5.19-10.93), 0.11(95% CI 0.08-0.14), 70.37 (95% CI 39.78-124.47), and 0.94 (95% CI 0.92-0.96), respectively. CONCLUSIONS: OCT is a promising technique in intraoperative margin assessment of breast cancer patients.

Using machine learning algorithms to identify chronic heart disease: National Health and Nutrition Examination Survey 2011-2018.

OBJECTIVE: The number of heart disease patients is increasing. Establishing a risk assessment model for chronic heart disease (CHD) based on risk factors is beneficial for early diagnosis and timely treatment of high-risk populations. METHODS: Four machine learning models, including logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost), were used to evaluate the CHD among 14 971 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. The area under the receiver-operator curve (AUC) is the indicator that we evaluate the model. RESULTS: In four kinds of models, SVM has the best classification performance (AUC = 0.898), and the AUC value of logistic regression and random forest were 0.895 and 0.894, respectively. Although XGBoost performed the worst with an AUC value of 0.891. There was no significant difference among the four algorithms. In the importance analysis of variables, the three most important variables were taking low-dose aspirin, chest pain or discomfort, and total amount of dietary supplements taken. CONCLUSION: All four machine learning classifiers can identify the occurrence of CHD based on population survey data. We also determined the contribution of variables in the prediction, which can further explore their effectiveness in actual clinical data.

Using source-associated mobile genetic elements to identify zoonotic extraintestinal E. coli infections.

A one-health perspective may provide new and actionable information about Escherichia coli transmission. E. coli colonizes a broad range of vertebrates, including humans and food-production animals, and is a leading cause of bladder, kidney, and bloodstream infections in humans. Substantial evidence supports foodborne transmission of pathogenic E. coli strains from food animals to humans. However, the relative contribution of foodborne zoonotic E. coli (FZEC) to the human extraintestinal disease burden and the distinguishing characteristics of such strains remain undefined. Using a comparative genomic analysis of a large collection of contemporaneous, geographically-matched clinical and meat-source E. coli isolates (n = 3111), we identified 17 source-associated mobile genetic elements - predominantly plasmids and bacteriophages - and integrated them into a novel Bayesian latent class model to predict the origins of clinical E. coli isolates. We estimated that approximately 8 % of human extraintestinal E. coli infections (mostly urinary tract infections) in our study population were caused by FZEC. FZEC strains were equally likely to cause symptomatic disease as non-FZEC strains. Two FZEC lineages, ST131-H22 and ST58, appeared to have particularly high virulence potential. Our findings imply that FZEC strains collectively cause more urinary tract infections than does any single non-E. coli uropathogenic species (e.g., Klebsiella pneumoniae). Our novel approach can be applied in other settings to identify the highest-risk FZEC strains, determine their sources, and inform new one-health strategies to decrease the heavy public health burden imposed by extraintestinal E. coli infections.

Value of Assessment of Different Neoplasias in the Adnexa in the Differential Diagnosis of Malignant Ovarian Tumor and Benign Ovarian Tumor: A Meta-analysis.

To evaluate the accuracy of the assessment of different neoplasias in the adnexa (ADNEX) model in the differential diagnosis of malignant and benign ovarian tumors, the optimal cutoff value and the accuracy in diagnosing ovarian tumors at different stages, PubMed, Web of Science and Cochrane Library databases were retrieved to search literature with per-patient analysis until publication of the last study in November 2021. STATA 14.1, Meta-Disc 1.4 and Revman software 5.3 were used in the performance of meta-analysis. To explore sources of heterogeneity, a subgroup analysis was conducted for the ADNEX model. The pooled sensitivity, specificity, diagnostic odds ratio, positive likelihood, negative likelihood ratio and area under the summary receiver operating characteristic curve were 0.91 (95% confidence interval [CI]: 0.89-0.93), 0.84 (95% CI: 0.80-0.88), 55.55 (95% CI: 40.47-76.26), 5.71 (95% CI: 4.49-7.26), 0.10 (95% CI: 0.08-0.13) and 0.94 (95% CI: 0.92-0.96) in differentiating benign and malignant ovarian tumors, respectively. The area under the curve in identifying benign, borderline, stage I and stages II-IV were 0.93, 0.73, 0.27 and 0.92. The ADNEX model had high diagnostic performance was influential in the diagnosis of benign and stage II-IV ovarian tumors.

Risk of contralateral ovarian cancer in patients undergoing conservative surgery for ovarian cancer.

BACKGROUND: Among patients with ovarian cancer (OC), the risk of contralateral OC remains controversial and few studies have focused on the occurrence of contralateral OC after conservative surgery. METHODS: Basing on the Surveillance, Epidemiology, and End Results (SEER) database registered between 2000 and 2018, Logistic and Cox regressions were established to test the risk factors of contralateral OC. Kaplan-Meier mothed was used to calculate the cumulative risk curve for contralateral OC and compared using log-rank test. Furthermore, the frequency of contralateral OC and standardized incidence ratios (SIRs) were evaluated. RESULTS: 18807 patients were included, 69 patients developed contralateral OC. Logistic and Cox regressions showed patients diagnosed >50 years had lower risk of contralateral OC (Odds ratio [OR]:0.42, 95% confidence interval [CI]: 0.24-0.73; Hazard ratios [HR]:0.44, 95%CI:0.24-0.77). Patients with radical surgery had lower contralateral OC risk (OR:0.20, 95%CI: 0.11-0.36; HR: 0.17, 95%CI: 0.09-0.30). The SIR for contralateral OC was high in all patients (SIR: 2.37, 95%CI: 1.85-3.00) and highest if patients diagnosed <50 years with conservative surgery (SIR: 27.33, 95%CI: 19.86-36.69). However, the SIR for contralateral OC was low in patients diagnosed ≥50 years with radical surgery (SIR: 0.54, 95%CI: 0.26-1.00). No statistically significant SIRs were observed in patients diagnosed ≥50 years with conservative surgery and patients diagnosed <50 years with radical surgery. CONCLUSIONS: Our study provided some information for clinicians to assess the risk of contralateral OC and suggested young patients should not undergo hysterectomy to prevent contralateral OC. Moreover, clinical surveillance cannot be relaxed.

Draft Genome Sequence of Escherichia coli Strain SEC470, Isolated from a Piglet Experiencing Diarrhea.

Escherichia coli strain SEC470 is a diarrhea-causing strain, isolated from a piglet experiencing serious diarrhea in Jingxi Province, China. Here, we present the draft genome of this strain, which provides the genetic basis for exploring the mechanism of enterotoxigenic E. coli infections.

Facile colorimetric method for simple and rapid detection of endotoxin based on counterion-mediated gold nanorods aggregation.

Existence of endotoxin in food and injection products indicates bacterial contaminations and therefore poses threat to human health. Herein, a simple and rapid colorimetric method for the effective detection of endotoxin in food and injections based on counterion-mediated gold nanorods aggregation is first proposed. By taking advantage of the color change of unmodified gold nanorods resulted from endotoxin mediated gold nanorods aggregation, endotoxin could be detected in the concentration range of 0.01-0.6 µM. Further, we studied the performance of gold nanorods with different aspect ratios (2.7 and 3.3) in determination of endotoxin and found that gold nanorods with higher aspect ratio (AR) showed superiority in the sensing sensitivity of endotoxin. A good specificity for endotoxin, a detection limit of 0.0084 µM and recoveries ranging from 84% to 109% in spiked food and injection samples are obtained with the colorimetric method. Results demonstrate that the present method provides a novel and effective approach for on-site screening of endotoxin in common products, which is beneficial for monitoring and reducing the risk of bacterial contaminations in food and injections production.

Highly sensitive and selective colorimetric detection of cartap residue in agricultural products.

The residue of pesticide has posed a serious threat to human health. Fast, broad-spectrum detection methods are necessary for on-site screening of various types of pesticides. With citrate-coated Au nanoparticles (Au NPs) as colorimetric probes, a visual and spectrophotometric method for rapid assay of cartap, which is one of the most important pesticides in agriculture, is reported for the first time. Based on the color change of Au colloid solution from wine-red to blue resulting from the aggregation of Au NPs, cartap could be detected in the concentration range of 0.05-0.6 mg/kg with a low detection limit of 0.04 mg/kg, which is much lower than the strictest cartap safety requirement of 0.1 mg/kg. Due to the limited research on the rapid detection of cartap based on Au NPs, the performance of the present method was evaluated through aggregation kinetics, interference influence, and sample pretreatment. To further demonstrate the selectivity and applicability of the method, cartap detection is realized in cabbage and tea with excellent analyte concentration recovery. These results demonstrate that the present method provides an easy and effective way to analyze pesticide residue in common products, which is of benefit for the rapid risk evaluation and on-site screening of pesticide residue.