Malament stitch and increased risk of bladder neck stenosis: any association following open prostatectomy in Enugu Southeast Nigeria

Background Malament stitch is one of the effective techniques employed to minimize bleeding in simple open prostatectomy but concerns about possibility of increased risk of bladder neck stenosis has limited its routine use. Aim We studied patients who had open prostatectomy with Malament stitch to determine the incidence of bladder neck stenosis amongst them. Material and methods This was a prospective study of 72patients who had simple open prostatectomy in which malament stitch was applied from 2010 to 2020. A proforma was designed to collect data. Pretreatment variables were transrectal ultrasound (TRUS) volume of prostate, pretreatment IPSS value, postvoidal residual urine volume before surgery, weight of enucleated prostate adenoma, time to removal of Malament stitch. Outcome measures were done with post treatment IPSS and PVR at 6 weeks, 3 months and 6 months. Cystoscopy was done at 3 months or 6 months for patients with rising outcome measures to determine presence of bladder neck stenosis. Results The mean age of patients in this study was 68.3 years (SD = 7.1, range 52–82). The mean of the pretreatment score for IPSS was 30.7 (SD = 3.9, range 18–34) and 5.9 (SD = 0.2) for QOLS. The mean weight of prostate estimated with ultrasound was 169.5 g and mean weight of enucleated adenoma of the prostate was 132.5 g. The mean time of removal of Malament stitch was 23.1 h. Only 3 (4.2%) patients required cystoscopy because of increasing IPSS and PVR at 3 months postprostatectomy. 2 (2.8%) patients out of 72patients were confirmed to have bladder neck stenosis at cystoscopy. Conclusion Malament stitch did not lead to significant incidence of bladder neck stenosis in this study.

Interactions in Ternary Aqueous Solutions of NMA and Osmolytes—PARAFAC Decomposition of FTIR Spectra Series

Intermolecular interactions in aqueous solutions are crucial for virtually all processes in living cells. ATR-FTIR spectroscopy is a technique that allows changes caused by many types of such interactions to be registered; however, binary solutions are sometimes difficult to solve in these terms, while ternary solutions are even more difficult. Here, we present a method of data pretreatment that facilitates the use of the Parallel Factor Analysis (PARAFAC) decomposition of ternary solution spectra into parts that are easier to analyze. Systems of the NMA–water–osmolyte-type were used to test the method and to elucidate information on the interactions between N-Methylacetamide (NMA, a simple peptide model) with stabilizing (trimethylamine N-oxide, glycine, glycine betaine) and destabilizing osmolytes (n-butylurea and tetramethylurea). Systems that contain stabilizers change their vibrational structure to a lesser extent than those with denaturants. Changes in the latter are strong and can be related to the formation of direct NMA–destabilizer interactions.

Application of NMR and Chemometrics for the Profiling and Classification of Ale and Lager American Craft Beer

In this paper, Nuclear Magnetic Resonance spectroscopy (NMR)-based metabolomics were applied for the discrimination of ale and lager craft American beers. A modified pulse sequence that allows the efficient suppression of the water and ethanol peaks was used to achieve high-quality spectra with minimal sample preparation. The initial chemometrics analysis generated models of low predictive power, indicating the high variability in the groups. Due to this variability, we tested the effect of various data pretreatment and chemometrics approaches to improve the model’s performance. Spectral alignment was found to improve the classification significantly, while the type of normalization also played an important role. NMR combined with statistical and machine-learning techniques such as orthogonal projection to latent structures discriminant analysis (OPLS-DA) and random forest was able to discriminate between ale and lager beers, thus providing an important tool for the quality control and analysis of these products.

Malament Stitch and Increased Risk of Bladder Neck Stenosis: Any Association Following Open Prostatectomy in Enugu Southeast Nigeria.

Background: Malament stitch is one of the effective techniques employed to minimize bleeding in simple open prostatectomy but concerns about possibility of increased risk of bladder neck stenosis has limited its routine use. Aim: We studied patients who had open prostatectomy with malament stitch to determine the incidence of bladder neck stenosis amongst them.Material and methods: this was a prospective study of 72patients who had simple open prostatectomy in which malament stitch was applied from 2010 to 2020. A proforma was designed to collect data. Pretreatment variables were transrectal ultrasound ( TRUS) volume of prostate, pretreatment IPSS value, postvoidal residual urine volume before surgery, weight of enucleated prostate adenoma, time to removal of Malament stitch. Outcome measures were done with post treatment IPSS and PVR at 6weeks, 3months and 6months. Cystoscopy was done at 3months or 6months for patients with rising outcome measures to determine presence of bladder neck stenosis. RESULTS: The mean age of patients in this study was 68.3 years (SD=7.1, range = 52-82). The mean of the pretreatment score for IPSS was 30.7 (SD= 3.9, range= 18- 34) and 5.9 (SD= 0.2) for QOLS. The mean weight of prostate estimated with ultrasound was 169.5g and mean weight of enucleated adenoma of the prostate was 132.5g. The mean time of removal of Malament stitch was 23.1hrsOnly 3 (4.2%)patients required cystoscopy because of increasing IPSS and PVR at three months postprostatectomy . 2 (2.8%)patients out of 72patients were confirmed to have bladder neck stenosis at cystoscopy.Conclusion: Malament stitch did not lead to significant incidence of bladder neck stenosis in this study.

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization

The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.

A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.