Drug-Coated Balloon Versus Plain Balloon Angioplasty for Hemodialysis Dysfunction: A Meta-Analysis of Randomized Controlled Trials.

Background Both drug-coated balloon (DCB) angioplasty and conventional plain balloon angioplasty (PBA) can be implemented to treat hemodialysis dysfunction. The present study aims to compare the safety and efficacy of these 2 approaches by conducting a meta-analysis of available randomized controlled trials. Methods and Results PubMed, Cochrane Library, and Embase databases were queried from establishment to January 2021. A total of 18 randomized controlled trials including 877 and 875 patients in the DCB and PBA groups, respectively, were included in the present meta-analysis. Target lesion primary patency, circuit patency, target lesion revascularization, and mortality were pooled. Odds ratios (ORs) were reported with 95% CIs. Publication bias was analyzed with funnel plot and Egger test. Target lesion primary patency was higher among patients who underwent DCB (OR, 2.93 [95% CI, 2.13-4.03], P<0.001 at 6 months; OR, 2.47 [95% CI, 1.53-3.99], P<0.001 at 1 year). Also, the DCB group had a higher dialysis circuit patency at 6 months (OR, 2.42; 95% CI, 1.56-3.77 [P<0.001]) and 1 year (OR, 1.91; 95% CI, 1.22-3.00 [P=0.005]). Compared with the PBA group, the DCB group had lower odds of target lesion revascularization during follow-up (OR, 0.43 [95% CI, 0.23-0.82], P=0.001 at 6 months; OR, 0.74 [95% CI, 0.32-1.73], P=0.490 at 1 year). The OR of mortality was comparable between 2 groups at 6 months (OR, 1.18; 95% CI, 0.42-3.33 [P=0.760]) and 1 year (OR, 0.93; 95% CI, 0.58-1.48 [P=0.750]). Conclusions Based on evidence from 18 randomized controlled trials, DCB angioplasty is superior to PBA in maintaining target lesion primary patency and circuit patency among patients with dialysis circuit stenosis. DCB angioplasty also reduces target lesion revascularization with a similar risk of mortality compared with PBA.

Prevalence and molecular identification of Nematodirus helvetianus in camels in Iraq.

BACKGROUND AND AIM: Camels from the central part of Iraq are infected with multiple parasitic diseases that have an economic impact by decreasing meat and milk production. This study aimed to evaluate Nematodirus spp. in camels (Camelus dromedarius). MATERIALS AND METHODS: The study animals consisted of camels slaughtered in the central area of Iraq at the Al-Najaf slaughterhouse. All ages and sexes of camels were examined. Worms were recovered and identified microscopically. For molecular characterization, two Iraqi Nematodirus spp. partial ribosomal genes (ITS1 and ITS2) were sequenced and submitted to the NCBI database. RESULTS: Of 160 camels tested, 29 were infected with Nematodirus spp. (18.13%). Twenty-one nematodes containing the Nematodirus genes were identified in the small intestines of naturally infected camels. BLAST analysis revealed 88.1% sequence similarity with that of Nematodirus helvetianus isolated in China and 87.2% similarity with N. helvetianus isolated in the United States. CONCLUSION: The prevalence of N. helvetianus warrants the use of anti-helminthic drugs for these animals and a rationale for future control strategies to prevent the transmission of this infection to other livestock.

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To improve screening specificity and sensitivity, we have built an artificial neural network (ANN) trained on 12 to 14 categories of personal health data from the National Health Interview Survey (NHIS). Years 1997-2016 of the NHIS contain 583,770 respondents who had never received a diagnosis of any cancer and 1409 who had received a diagnosis of CRC within 4 years of taking the survey. The trained ANN has sensitivity of 0.57 ± 0.03, specificity of 0.89 ± 0.02, positive predictive value of 0.0075 ± 0.0003, negative predictive value of 0.999 ± 0.001, and concordance of 0.80 ± 0.05 per the guidelines of Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) level 2a, comparable to current risk-scoring methods. To demonstrate clinical applicability, both USPSTF guidelines and the trained ANN are used to stratify respondents to the 2017 NHIS into low-, medium- and high-risk categories (TRIPOD levels 4 and 2b, respectively). The number of CRC respondents misclassified as low risk is decreased from 35% by screening guidelines to 5% by ANN (in 60 cases). The number of non-CRC respondents misclassified as high risk is decreased from 53% by screening guidelines to 6% by ANN (in 25,457 cases). Our results demonstrate a robustly-tested method of stratifying CRC risk that is non-invasive, cost-effective, and easy to implement publicly.

Lung Nodule Detection via Deep Reinforcement Learning.

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.

Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.

Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification.

PURPOSE: To develop and validate a multiparameterized artificial neural network (ANN) on the basis of personal health information for prostate cancer risk prediction and stratification. METHODS: The 1997 to 2015 National Health Interview Survey adult survey data were used to train and validate a multiparameterized ANN, with parameters including age, body mass index, diabetes status, smoking status, emphysema, asthma, race, ethnicity, hypertension, heart disease, exercise habits, and history of stroke. We developed a training set of patients ≥ 45 years of age with a first primary prostate cancer diagnosed within 4 years of the survey. After training, the sensitivity and specificity were obtained as functions of the cutoff values of the continuous output of the ANN. We also evaluated the ANN with the 2016 data set for cancer risk stratification. RESULTS: We identified 1,672 patients with prostate cancer and 100,033 respondents without cancer in the 1997 to 2015 data sets. The training set had a sensitivity of 21.5% (95% CI, 19.2% to 23.9%), specificity of 91% (95% CI, 90.8% to 91.2%), area under the curve of 0.73 (95% CI, 0.71 to 0.75), and positive predictive value of 28.5% (95% CI, 25.5% to 31.5%). The validation set had a sensitivity of 23.2% (95% CI, 19.5% to 26.9%), specificity of 89.4% (95% CI, 89% to 89.7%), area under the curve of 0.72 (95% CI, 0.70 to 0.75), and positive predictive value of 26.5% (95% CI, 22.4% to 30.6%). For the 2016 data set, the ANN classified all 13,031 patients into low-, medium-, and high-risk subgroups and identified 5% of the cancer population as high risk. CONCLUSION: A multiparameterized ANN that is based on personal health information could be used for prostate cancer risk prediction with high specificity and low sensitivity. The ANN can further stratify the population into three subgroups that may be helpful in refining prescreening estimates of cancer risk.

Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence.

Several studies have linked codeletion of chromosome arms 1p/19q in low-grade gliomas (LGG) with positive response to treatment and longer progression-free survival. Hence, predicting 1p/19q status is crucial for effective treatment planning of LGG. In this study, we predict the 1p/19q status from MR images using convolutional neural networks (CNN), which could be a non-invasive alternative to surgical biopsy and histopathological analysis. Our method consists of three main steps: image registration, tumor segmentation, and classification of 1p/19q status using CNN. We included a total of 159 LGG with 3 image slices each who had biopsy-proven 1p/19q status (57 non-deleted and 102 codeleted) and preoperative postcontrast-T1 (T1C) and T2 images. We divided our data into training, validation, and test sets. The training data was balanced for equal class probability and was then augmented with iterations of random translational shift, rotation, and horizontal and vertical flips to increase the size of the training set. We shuffled and augmented the training data to counter overfitting in each epoch. Finally, we evaluated several configurations of a multi-scale CNN architecture until training and validation accuracies became consistent. The results of the best performing configuration on the unseen test set were 93.3% (sensitivity), 82.22% (specificity), and 87.7% (accuracy). Multi-scale CNN with their self-learning capability provides promising results for predicting 1p/19q status non-invasively based on T1C and T2 images. Predicting 1p/19q status non-invasively from MR images would allow selecting effective treatment strategies for LGG patients without the need for surgical biopsy.

Do current standards of primary care of diabetes meet with guideline recommendations in Trinidad, West Indies?

AIMS: Primary care management of diabetes was examined using the Caribbean Health Research Council (CHRC) guidelines. METHODS: We retrospectively examined a cross-section of 646 type 2 people with diabetics over 12 months with 1st visit between 1997 and 2005. RESULTS: There were more women (65.8%) than men (34.2%) with age range between 29 and 89 years. Blood pressure and weight were evaluated in >95% of patients at each centre. Waist circumference and BMI were not measured at any time and HbA(1)c was infrequently measured (1.6-7%) over the 12 months. Information on family history (87.5%), smoking and alcohol (78.1%), exercise (21.4%), socioeconomic status (19.4%) and education (0.3%), and fasting blood sugar (97.2%), lipid profile (51.8%) and serum creatinine (37.9%) were assessed at the 1st visit. At follow-up patients were advised on treatment compliance (47.2%), diet (34.2%), exercise (18.5%) and rarely on home monitoring of blood glucose (0.3%). Peripheral sensations, pedal pulses (6%), visual acuity (3.3%), fundoscopy (12.1%) and ECG (3.9%) were scarcely examined at the annual visit. CONCLUSIONS: Current management of diabetes in primary care in Trinidad falls short of Caribbean guideline recommendations. The CHRC and Ministry of Health should jointly educate caregivers of diabetes to implement the guidelines, with annual audits to identify shortfalls in management.