Is systematic prostate biopsy an overkill in metastatic prostate carcinoma ? A prospective validation.

PURPOSE: To assess the efficacy of 2-core prostate biopsy in advanced prostate cancer patients. This included a retrospective analysis of 12-core prostate biopsies and a prospective validation that a reduced number of cores are sufficient for histopathological diagnosis. METHODS: The first phase analyzed retrospective data from 12-core prostate biopsies between January 2013 and 2018. In the second phase, from January 2018 to January 2022, in a prospective setting, patients with PSA > 75 ng/dl underwent bone scans first. Those with positive bone scans underwent a 2-core biopsy. Cancer detection rate and complications were analyzed to validate the findings of the first phase. RESULTS: In the retrospective analysis, the number of positive cores in metastatic disease was 12 in 93 (73.8%), 11 in 14 (11.1%), and 10 in 7 (5.6%) patients. Using probability analysis, 94% of patients with metastasis could be detected with a single core and 97.8% with a 2-core biopsy. In the prospective analysis, 52 patients with PSA > 75 were enrolled. 3/52 (5.7%) patients had a negative bone scan. 49 were assigned for 2-core biopsy, out of which 48 (97.9%) had a positive result. One patient underwent a repeat 12-core biopsy. The prospective cohort's complications (p = 0.003) and pain score (p = 0.03) were lower compared to patients who underwent standard 12-core biopsies during phase one of the study period. CONCLUSION: A 2-core biopsy is adequate in almost all patients with metastatic prostate cancer with PSA > 75, and this avoids excess complications and morbidity associated with a systematic 12-core prostate biopsy.

Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN) that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.

Protocol for a case-control study of vitamin D status, adult multidrug-resistant tuberculosis disease and tuberculosis infection in Mumbai, India.

INTRODUCTION: Vitamin D status may be an important determinant of multidrug-resistant tuberculosis (MDR-TB) infection, progression to disease and treatment outcomes. Novel and potentially cost-effective therapies such as vitamin D supplementation are needed to stem the tide of TB and MDR-TB globally, particularly in India, a country that accounts for the largest fraction of the world's TB incidence and MDR-TB incidence, and where vitamin D deficiency is endemic. While vitamin D has shown some promise in the treatment of MDR-TB, its role in the context of MDR-TB infection and progression to disease is largely unknown. METHODS AND ANALYSIS: Through a case-control study in Mumbai, India, we aim to examine associations between vitamin D status and active MDR-TB and to investigate vitamin D status and TB infection among controls. Cases are adult outpatient pulmonary patients with MDR-TB recruited from two public TB clinics. Controls are recruited from the cases' household contacts and from non-respiratory departments of the facilities where cases were recruited. Cases and controls are assessed for serum 25-hydroxyvitamin D concentration, nutrient intake, diet quality, anthropometry and other relevant clinical and sociodemographic parameters. Controls undergo additional clinical assessments to rule out active TB and laboratory assessments to determine presence of TB infection. Statistical analysis investigates associations between vitamin D status and active MDR-TB and between vitamin D status and TB infection among controls, accounting for potential confounding effects of diet, anthropometry and other covariates. ETHICS AND DISSEMINATION: This study has been approved by Harvard T.H. Chan School of Public Health Institutional Review Board; Foundation for Medical Research Institutional Research Ethics Committee and Health Ministry's Screening Committee of the Indian Council for Medical Research. Permission was granted by the Municipal Corporation of Greater Mumbai, India, a collaborating partner on this research. Outcomes will be disseminated through publication and scientific presentation. TRIAL REGISTRATION NUMBER: NCT04342598.

A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: patients with cardiovascular disease.

Intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of electronic health records. We aimed to build a mortality prediction model on a Medical Information Mart for Intensive Care (MIMIC-III) database and to assess whether the use of deep learning techniques like long short-term memory (LSTM) can effectively utilize the temporal relations among clinical variables. The models were built on clinical variable dynamics of the first 48 h of ICU admission of 12,550 records from the MIMIC-III database. A total of 36 variables including 33 time series variables and three static variables were used for the prediction. We present the application of LSTM and LSTM attention (LSTM-AT) model for mortality prediction with such a large number of clinical variables dataset. For training and validation purpose, we have used International Classification of Diseases, 9th edition (ICD-9) codes for extracting the patients with cardiovascular disease, and infections and parasitic disease, respectively. The effectiveness of the LSTM model is achieved over non-recurrent baseline models like naïve Bayes, logistic regression (LR), support vector machine and multilayer perceptron (MLP) by generating state of the art results (area under the curve [AUC], 0.852). Next, by providing attention at each time stamp, we developed a model, LSTM-AT, which exhibits even better performance (AUC, 0.876).

A three plus three parameters mechanistic model for viral filtration.

Viral filtration is an expensive regulatory requirement in downstream processing of monoclonal antibodies (mAbs). This process step is typically operated with an overdesigned filter in order to account for any batch to batch variability in the filter, as well as the feed characteristics. Here, we propose a simple, six-parameter mechanistic model for viral filtration where three parameters are membrane-specific while the other three depend on feed characteristics and membrane-feed interactions. Viruses are considered as passive particles which are retained by the membrane on the basis of size exclusion. The model envisages that the viral filter contains two kind of pores: virus-retentive, small-sized pores and non-retentive, large-sized pores. The small-sized pores get blocked during filtration resulting in decrease in active membrane area, while the large-sized pores get constricted during filtration. The length of constricted part increases during filtration and contributes to increase in hydraulic resistance of the filter. Rate of these processes (blocking and constriction) are assumed to be proportional to the instantaneous rate of retention of the viral particles. The general nature of the model is validated with the experimental data on viral filtration for four different commercial membranes used in biotech industries as well as different model viruses. The proposed model has been demonstrated to describe the behavior of filters with very good accuracy. The best-fit model parameter values indicate about the various phenomena that are responsible for differences in the behavior of the membranes as well as change in retention and flux with feed concentration. The proposed model can be used for improving design of virus filters as well as in appropriate sizing of the filters during processing. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1538-1547, 2017.

A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease.

BACKGROUND AND OBJECTIVES: Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Analyzing human gait serves to be useful in studies aiming at early recognition of the disease. In this paper we perform a comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest. METHODS: For the experiments, we use a real life dataset of 166 people containing both healthy controls and affected people. Following the optimal feature selection process, the dataset is then classified using a neural network. RESULTS AND CONCLUSIONS: The experimental results show Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features. We compare this through different criteria like cross-validated accuracies, true positive rates, false positive rates, positive predicted values and negative predicted values.

Quality assessment of modeled protein structure using physicochemical properties.

Physicochemical properties of proteins always guide to determine the quality of the protein structure, therefore it has been rigorously used to distinguish native or native-like structure from other predicted structures. In this work, we explore nine machine learning methods with six physicochemical properties to predict the Root Mean Square Deviation (RMSD), Template Modeling (TM-score), and Global Distance Test (GDT_TS-score) of modeled protein structure in the absence of its true native state. Physicochemical properties namely total surface area, euclidean distance (ED), total empirical energy, secondary structure penalty (SS), sequence length (SL), and pair number (PN) are used. There are a total of 95,091 modeled structures of 4896 native targets. A real coded Self-adaptive Differential Evolution algorithm (SaDE) is used to determine the feature importance. The K-fold cross validation is used to measure the robustness of the best predictive method. Through the intensive experiments, it is found that Random Forest method outperforms over other machine learning methods. This work makes the prediction faster and inexpensive. The performance result shows the prediction of RMSD, TM-score, and GDT_TS-score on Root Mean Square Error (RMSE) as 1.20, 0.06, and 0.06 respectively; correlation scores are 0.96, 0.92, and 0.91 respectively; R(2) are 0.92, 0.85, and 0.84 respectively; and accuracy are 78.82% (with ± 0.1 err), 86.56% (with ± 0.1 err), and 87.37% (with ± 0.1 err) respectively on the testing data set. The data set used in the study is available as supplement at http://bit.ly/RF-PCP-DataSets.

Modeling of Filtration Processes-Microfiltration and Depth Filtration for Harvest of a Therapeutic Protein Expressed in Pichia pastoris at Constant Pressure.

Filtration steps are ubiquitous in biotech processes due to the simplicity of operation, ease of scalability and the myriad of operations that they can be used for. Microfiltration, depth filtration, ultrafiltration and diafiltration are some of the most commonly used biotech unit operations. For clean feed streams, when fouling is minimal, scaling of these unit operations is performed linearly based on the filter area per unit volume of feed stream. However, for cases when considerable fouling occurs, such as the case of harvesting a therapeutic product expressed in Pichia pastoris, linear scaling may not be possible and current industrial practices involve use of 20-30% excess filter area over and above the calculated filter area to account for the uncertainty in scaling. In view of the fact that filters used for harvest are likely to have a very limited lifetime, this oversizing of the filters can add considerable cost of goods for the manufacturer. Modeling offers a way out of this conundrum. In this paper, we examine feasibility of using the various proposed models for filtration of a therapeutic product expressed in Pichia pastoris at constant pressure. It is observed that none of the individual models yield a satisfactory fit of the data, thus indicating that more than one fouling mechanism is at work. Filters with smaller pores were found to undergo fouling via complete pore blocking followed by cake filtration. On the other hand, filters with larger pores were found to undergo fouling via intermediate pore blocking followed by cake filtration. The proposed approach can be used for more accurate sizing of microfilters and depth filters.

Modeling of separation of aqueous solutions of FeCl3 and AlCl3 by zeolite-clay composite membranes using a space-charge model.

In our earlier work, we reported the separation of FeCl3 from its aqueous solution and AlCl3 from its aqueous solution by analcime zeolite (Z1) membrane and its nitrated (Z2 membrane) and aminated (Z3 membrane) forms. Experimental data on the separation of aqueous solutions of FeCl3 and AlCl3 by zeolite-clay composite membranes has been simulated using the two-dimensional space-charge model. The computational requirement of the model has been considerably reduced by first obtaining a series solution of the nonlinear Poisson-Boltzmann equation. The effective pore radius of the membrane is taken as the one that gives the best fit to the experimental data, while the pore length is determined from the SEM photograph of the cross-sectional view of the membrane. The effective pore radii of the Z1, Z2, and Z3 membranes for FeCl3 solute are found to be 8.0, 7.0, and 5.0 nm, respectively, while for AlCl3 they are 4.5, 2.5, and 2.5 nm, respectively. These values are much less than the average of the pore size range values determined independently in an earlier work using the bubble point method and indicate partial blocking of the pores by these salts. The effective pore radius is larger for FeCl3 as compared to AlCl3 and decreases on modification. The intrinsic rejection is also found to decrease on modification. The permeate flux calculated from the model matches very well with the experimental values.