Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

Resistance-based directed evolution of nanobodies for higher affinity in prokaryotes.

A prokaryotic resistance-based directed evolution system leveraging protein-fragment complementation assay (PCA) was devised, and its proficiency in detecting protein-protein interactions and discriminating varying degrees of binding affinity was demonstrated by two well-characterized protein pairs. Furthermore, we constructed a random mutant library based on the GBPR36K/E45K mutant, characterized by almost no affinity towards EGFP. This library was subjected to PCA-based prokaryotic directed evolution, resulting in the isolation of back-mutated variants. In summary, we have established an expedited, cost-effective, and structural information-independent PCA-based prokaryotic directed evolution platform for nanobody affinity maturation, featuring tunable screening stringency via modulation of antibiotic concentrations.

Raman and resonance Raman spectroscopic study of red blood cells in patients diagnosed with malaria.

Raman spectroscopy was used to study erythrocytes collected from patients diagnosed with malaria at the University Hospital in Kraków and from healthy volunteers. A laser line with a wavelength of 442 nm was used to induce the Raman resonance of haem, while a laser with a wavelength of 785 nm was used for the normal Raman effect. The results were analysed using Principal Component Analysis. For the 442 nm laser line, analysis of the entire spectral range (3200 cm-1 to 300 cm-1) showed satisfactory separation of Raman spectra for healthy cells from infected cells, which was significantly improved in the 1500 cm-1-1200 cm-1 spectral range. For the 785 nm laser line, some separation was observed in each range studied, but the best results were achieved over the full spectral range. Plasmodium-derived nucleic acids and phosphodiester vibrations were observed at excitation lines of 442 nm and 785 nm, respectively.

3D virtual models and augmented reality for radical prostatectomy: a narrative review.

BACKGROUND AND OBJECTIVE: The increasing popularity of three-dimensional (3D) virtual reconstructions of two-dimensional (2D) imaging in urology has led to significant technological advancements, resulting in the creation of highly accurate 3D virtual models (3DVMs) that faithfully replicate individual anatomical details. This technology enhances surgical reality, providing surgeons with hyper-accurate insights into instantaneous subjective surgical anatomy and improving preoperative surgical planning. In the uro-oncologic field, the utility of 3D virtual reconstruction has been demonstrated in nephron-sparing surgery, impacting surgical strategy and postoperative outcomes in prostate cancer (PCa). The aim of this study is to offer a thorough narrative review of the current state and application of 3D reconstructions and augmented reality (AR) in radical prostatectomy (RP). METHODS: A non-systematic literature review was conducted using Medline, PubMed, the Cochrane Database, and Embase to gather information on clinical trials, randomized controlled trials, review articles, and prospective and retrospective studies related to 3DVMs and AR in RP. The search strategy followed the PICOS (Patients, Intervention, Comparison, Outcome, Study design) criteria and was performed in January 2024. KEY CONTENT AND FINDINGS: The adoption of 3D visualization has become widespread, with applications ranging from preoperative planning to intraoperative consultations. The urological community's interest in intraoperative surgical navigation using cognitive, virtual, mixed, and AR during RP is evident in a substantial body of literature, including 16 noteworthy investigations. These studies highlight the varied experiences and benefits of incorporating 3D reconstructions and AR into RP, showcasing improvements in preoperative planning, intraoperative navigation, and real-time decision-making. CONCLUSIONS: The integration of 3DVMs and AR technologies in urological oncology, particularly in the context of RP, has shown promising advancements. These technologies provide crucial support in preoperative planning, intraoperative navigation, and real-time decision-making, significantly improving the visualization of complex anatomical structures helping in the nerve sparing (NS) approach modulation and reducing positive surgical margin (PSM) rate. Despite positive outcomes, challenges such as small patient cohorts, lack of standardized methodologies, and concerns about costs and technology adoption persist.

Efficacy and safety of PARP inhibitors in the treatment of prostatic cancer: a systematic review and network meta-analysis.

BACKGROUND: Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer-related death in men. Previous studies have shown that the poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors (PARPis) improve the treatment response of patients with metastatic castration-resistant PCa (mCRPC). However, the efficacy and safety of various PARPis in mCRPC patients remain unclear, presenting a significant challenge for clinicians when making treatment decisions. To address this, this study conducted two indirect comparisons to evaluate the efficacy and safety of four PARPis (olaparib, niraparib, rucaparib, and talazoparib) in patients with mCRPC. METHODS: A systematic review and network meta-analysis (NMA) using Bayesian statistics was conducted. A comprehensive literature search was performed of the PubMed, Web of Science, Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI) databases to identify relevant studies from the inception to November 8, 2023, using search terms such as "PARP inhibitor", "olaparib", "rucaparib", "niraparib", "talazoparib", and "mCRPC". Phase 2/3 randomized controlled trials (RCTs) related to PARPi therapy and novel hormonal therapy in patients with mCRPC were included in the analysis. The targeted outcomes included radiographic progression-free survival (rPFS), overall survival (OS), adverse events (AEs), and grade ≥3 AEs. Four reviewers screened the titles and abstracts independently to assess the eligibility of each article. Two researchers independently extracted data from the included studies. The risk of bias and quality of the studies were assessed using the Risk-of-Bias 2 tool. RESULTS: Six high-quality phase 2/3 clinical trials, comprising 3,205 individuals, were selected for the systematic review and NMAs. Two NMAs were conducted due to the different designs of the six clinical trials. The indirect comparison with a random-effects model of olaparib, niraparib, and talazoparib showed that olaparib significantly improved rPFS with a hazard ratio (HR) of 0.67 [95% confidence interval (CI): 0.46-0.96]; however, no such significant difference was observed in relation to olaparib and rucaparib. In terms of OS, no significant difference was observed among olaparib, niraparib, and talazoparib. In relation to the AEs, the PARPi interventions using olaparib, niraparib, and talazoparib increased the rates of grade ≥3 AEs with odds ratios (ORs) of 2.0 (95% CI: 0.89-5.3), 3.0 (95% CI: 1.3-7.4), and 3.7 (95% CI: 1.1-12.0), respectively. In the rank probability analysis, according to the surface under the cumulative ranking (SUCRA), olaparib ranked first, followed by niraparib, and talazoparib. Most of the included studies were assessed to be at low risk of bias. CONCLUSIONS: Olaparib significantly improved rPFS among olaparib, niraparib, and talazoparib. Talazoparib exhibited the highest SUCRA value. Regarding safety, olaparib and rucaparib did not significantly increase the incidence of grade ≥3 AEs. When making personalized treatment decisions, clinicians should consider individual patient characteristics, treatment efficacy, and potential AEs.

Advancements in artificial intelligence for robotic-assisted radical prostatectomy in men suffering from prostate cancer: results from a scoping review.

BACKGROUND: Robotic-assisted radical prostatectomy (RARP) is currently a first-line treatment option for men with localized prostate cancer (PCa), at least 10 years of life expectancy, and candidate for curative treatment. We performed a scoping review to evaluate the role of artificial intelligence (AI) on RARP for PCa. METHODS: A comprehensive literature search was performed using EMBASE, PubMed, and Scopus. Only English papers were accepted. The PICOS (Patient Intervention Comparison Outcome Study type) model was used; P: adult men with PCa undergoing RARP; I: use of AI; C: none; O: preoperative planning improvement and postoperative outcomes; S: prospective and retrospective studies. RESULTS: Seventeen papers were included, dealing with prediction of positive surgical margins/extraprostatic extension, biochemical recurrence, patient's outcomes, intraoperative superimposition of magnetic resonance images to identify and locate lesions for nerve-sparing surgery, identification and labeling of surgical steps, and quality of surgery. All studies found improving outcomes in procedures employing AI. CONCLUSIONS: The integration of AI in RARP represents a transformative advancement in surgical practice, augmenting surgical precision, enhancing decision-making processes and facilitating personalized patient care. This holds immense potential to improve surgical outcomes and teaching, and mitigate complications. This should be balanced against the current costs of implementation of robotic platforms with such a technology.

Impact of ambient conditions on the minor nutrients content in spirulina "Arthrospira platensis" (AP) extracted from Lake Chad revealed by fluorescence spectroscopy coupled with chemometric methods.

In this study, the technique of fluorescence spectroscopy coupled with chemometric methods is used to analyse samples of Lake Chad Spirulina "Arthrospira platensis" (AP), either harvested and conditioned by using the traditional method at different seasons or industrially processed. The content of minor fluorescent nutrients is investigated. To this end, fluorescence excitation-emission matrices (EEMs) of 46 AP samples are recorded in aqueous solution. Synchronous fluorescence (SF) spectra are extracted from these EEMs and their important features are compared to those of PARAFAC methods. Synchronous fluorescence scanning allows different AP samples to be characterized in a single scan. The SF and PARAFAC methods yielded two groups of fluorescent compounds; the first group, consisting of vitamin-like molecules, shows excitation/emission (ex/em) peaks at 340/460, 390/462, 370/440 and 450/526 nm, attributed to caffeic acid, vitamin K, E and riboflavins respectively, while the second group, consisting of pigments, shows ex/em peaks at 610/654, 590/630 and 570/644 nm, attributed to phycocyanins, C-phycocyanin and allophycocyanin. Our fluorescence data showed that while both vitamins and pigments are present in AP during the rainy season, only fluorescent components of vitamin-like compounds are present during the dry season. PCA methods allowed classifying different AP samples according to their geographic origin and harvesting season. Fluorescence spectroscopy therefore appears to be a powerful technique for rapidly assessing the chemical composition of AP.

A rigorous workflow and comparative analysis for accurate determination of vitrinite reflectance using data-driven approaches in the Persian Gulf region.

Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock-Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock-Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock-Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods.

Vibrational spectroscopy data fusion for enhanced classification of different milk types.

The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies.

Insights on the comparative affinity of ribonucleic acids with plant-based beta carboline alkaloid, harmine: Spectroscopic, calorimetric and computational evaluation.

Small molecules as ligands target multifunctional ribonucleic acids (RNA) for therapeutic engagement. This study explores how the anticancer DNA intercalator harmine interacts various motifs of RNAs, including the single-stranded A-form poly (rA), the clover leaf tRNAphe, and the double-stranded A-form poly (rC)-poly (rG). Harmine showed the affinity to the polynucleotides in the order, poly (rA) > tRNAphe > poly (rC)·poly (rG). While no induced circular dichroism change was detected with poly (rC)poly (rG), significant structural alterations of poly (rA) followed by tRNAphe and occurrence of concurrent initiation of optical activity in the attached achiral molecule of alkaloid was reported. At 25 °C, the affinity further showed exothermic and entropy-driven binding. The interaction also highlighted heat capacity (ΔC o p ) and Gibbs energy contribution from the hydrophobic transfer (ΔG hyd) of binding with harmine. Molecular docking calculations indicated that harmine exhibits higher affinity for poly (rA) compared to tRNAphe and poly (rC)·poly (rG). Subsequent molecular dynamics simulations were conducted to investigate the binding mode and stability of harmine with poly(A), tRNAphe, and poly (rC)·poly (rG). The results revealed that harmine adopts a partial intercalative binding with poly (rA) and tRNAphe, characterized by pronounced stacking forces and stronger binding free energy observed with poly (rA), while a comparatively weaker binding free energy was observed with tRNAphe. In contrast, the stacking forces with poly (rC)·poly (rG) were comparatively less pronounced and adopts a groove binding mode. It was also supported by ferrocyanide quenching analysis. All these findings univocally provide detailed insight into the binding specificity of harmine, to single stranded poly (rA) over other RNA motifs, probably suggesting a self-structure formation in poly (rA) with harmine and its potential as a lead compound for RNA based drug targeting.

Risk factors for secondary bladder cancer following prostate cancer radiotherapy.

This review investigates the complex landscape of secondary bladder cancer (SBC) after radiotherapy for prostate cancer (PCa). External beam radiotherapy (EBRT) poses an increased risk for SBC, while brachytherapy seems to be associated with smaller increased risks for SBC due to its targeted radiation delivery, sparing the surrounding bladder tissue. Secondary cancers in the bladder are the most frequently diagnosed secondary cancers in the PCa patient population treated with radiotherapy. Patient-related factors are pivotal, with age emerging as a dual-edged factor. While advanced age is a recognized risk for bladder cancer, younger PCa patients exhibit higher susceptibility to radiation-induced cancers. Smoking, a well-established bladder cancer risk factor, increases this vulnerability. Studies highlight the synergistic effect of smoking and radiation exposure, amplifying the likelihood of genetic mutations and SBC. The latency period of SBC, which spans years to decades, remains a critical aspect. There is a strong dose-response relationship between radiation exposure and SBC risk, with higher doses consistently being associated with a higher SBC risk. While specific models for therapeutic radiation-induced SBC are lacking, insights from related studies, like the Atomic Bomb survivor research, emphasize the bladder's sensitivity to radiation-induced cancer. Chemotherapy in combination with radiotherapy, although infrequently used in PCa, emerges as a potential risk for bladder cancer. Bladder cancer's complex epidemiology, encompassing risk factors, treatment modalities, and cancer types, provides a comprehensive backdrop. As research refines understanding, we hope that this review contributes to guide clinicians, inform patient care, and shape preventive strategies on SBC.

Racial, ethnic, and socioeconomic disparities in rates of stage IV prostate cancer after USPSTF category "D" recommendation against prostate-specific antigen screening: a retrospective cohort study.

Background: In 2012 the United States Preventative Services Task Force (USPSTF) changed its prostate-specific antigen (PSA) screening recommendation to a category "D". The purpose of this study is to examine racial, ethnic, and socioeconomic differences in risk of presentation with metastatic prostate cancer (mPCa) at time of diagnosis before and after the 2012 USPSTF category "D" recommendation. Methods: This is a population-based cohort study. We identified patients with mPCa at diagnosis within the National Cancer Database from 2004-2017. Logistic regression models were used to examine associations of mPCa with age, race, ethnicity, geographic location, education level, income, and insurance status. Linear regression models assuming underlying binomial distribution were fitted to annual percentage of mPCa at diagnosis for years 2012-2017 to evaluate the post category "D" recommendation era. Results: From 2004 to 2017, 88,987 patients presented with mPCa. A higher percentage of mPCa was noted post-USPSTF category "D" recommendation, with a disproportionately greater increase observed among Hispanics and non-Hispanic Blacks [Δslope/year: Hispanics (0.0092), non-Hispanic Blacks (0.0073) and non-Hispanic Whites (0.0070)]. Insurance status impacts race/ethnicity differently: uninsured Hispanics were 3.66 times more likely to present with mPCa than insured Hispanics, while uninsured non-Hispanic Blacks were 2.62 times more likely to present with mPCa than insured non-Hispanic Blacks. Household income appears to be associated with differences in mPCa, particularly among non-Hispanic Blacks. Those earning <$30,000 were more likely to present with mPCa compared to higher income brackets. Conclusions: Since the USPSTF grade "D" recommendation against PSA screening, the percentage of mPCa at diagnosis has increased, with a higher rate of increase among Hispanic and non-Hispanic Blacks compared to non-Hispanic Whites.

METTL14 inhibits the proliferation, migration and invasion of prostate cancer cells by increasing m6A methylation of CDK4.

Background: Methyltransferase-like (METTL) plays an important role in various biological processes, but its role in prostate cancer (PCa) is still unclear. This study aimed to explore the mechanism by which methyltransferase-like 14 (METTL14) inhibits the physiological activity of PCa cells by increasing the N6-methyladenosine (m6A) modification of cyclin-dependent kinase 4 (CDK4). Methods: Clinical samples were collected for bioinformatics analysis. A PCa mouse model was constructed. Cell counting kit-8 (CCK-8), flow cytometry, colony formation assays, scratch assays, Transwell assays, real-time quantitative polymerase chain reaction (RT-qPCR), immunofluorescence and western blotting were used to detect the corresponding indicators. Results: METTL14 was found to be beneficial to inhibit the proliferation, invasion, and migration of PCa cells. When the m6A RNA increased, the half-life of CDK4 mRNA decreased after oe-METTL14 (overexpression of METTL14). Overexpression of CDK4 reversed the effect of oe-METTL14. Coimmunoprecipitation experiments revealed there were interactions between CDK4 and forkhead box M1 (FOXM1). Transfection of si-CDK4 was similar to transfection of oe-METTL14. After transfection with oe-FOXM1, the invasion and migration ability of cells increased, and cell apoptosis decreased. After transfection with si-FOXM1 alone, autophagy related 7 (ATG7) expression was significantly downregulated, and autophagy levels were reduced. The overexpression of ATG7 reversed the effect of si-FOXM1. The tumor volume and weight of the oe-METTL14 group mice were significantly reduced, and tumor proliferation was decreased in comparison to untreated tumor-bearing mice. Conclusions: METTL14 inhibits the invasion and migration of PCa cells and induces cell apoptosis by inhibiting CDK4 stability and FOXM1/ATG7-mediated autophagy.

A review of tanshinone compounds in prostate cancer treatment.

Prostate cancer (PCa) is one of the most common malignant epithelial tumors in men worldwide. PCa patients are initially sensitive to chemotherapy, but patients in the advanced stages of PCa eventually develop resistance, leaving them with limited therapeutic options. Therefore, it is very important to screen new drugs for treating PCa. Salvia miltiorrhiza is a common Chinese herbal medicine used in some Asian countries. It has many functions and is widely used to treat a variety of diseases, including heart diseases and cancers. For the past few years, research has shown that liposoluble constituents of tanshinones (TANs), including cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I, exhibit good anticancer activity in PCa. In this study, we review the progress of TAN compounds (cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I) in treating PCa over the past decade. These compounds can act on the same molecular mechanisms, as they have a very similar structure; they are also found to work slightly differently in PCa. According to current studies, compared with other TAN compounds, TAN IIA appears to hold more potential for treating PCa. The toxicity, side effects or biodistribution of Salvia miltiorrhiza and these four TANs need to be confirmed with further research. Findings obtained in this study may provide important information for the potential clinical application of cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I in the treatment of PCa.

Outpatient Patient Controlled Analgesia (PCA) for the Palliative Care Patient.

This study explores the under-researched domain of patient-controlled analgesia (PCA) for cancer pain management in adult outpatients, focusing on the transition from patient-controlled analgesia pumps (PCA pump) to oral medications. While existing literature primarily addresses the use of PCA in inpatient settings, this descriptive study investigates the initiation of outpatient PCA in palliative care patients. The retrospective chart review includes data from all admissions between July 1, 2014, and December 31, 2020. Among the 49 identified patients, 41 were admitted for cancer-related pain, with an indication for PCA such as insufficient pain relief, highly fluctuating pain, or inadequate response to other routes. Of these patients, 13 were successfully transitioned from outpatient PCA to oral opioids. The study underscores the effective use of PCA as a transitional tool following a pain crisis that necessitates inpatient admission. Future research avenues could explore healthcare utilization, length of stay, and required outpatient resources, such as home visits or telehealth, for optimal PCA use in outpatient settings.

Mapping protein conformational landscapes from crystallographic drug fragment screens.

Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COLAV (COnformational LAndscape Visualization), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP-1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens also enable detailed mapping of proteins' conformational landscapes.

Estimation of plant pollution removal capacity based on intensive air quality measurements.

This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2) from multiple monitoring stations. The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis.

We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis.

An Analysis of Capsaicin, Dihydrocapsaicin, Vitamin C and Flavones in Different Tissues during the Development of Ornamental Pepper.

As a fruit and vegetable crop, the ornamental pepper is not just highly ornamental but also rich in nutritional value. The quality of ornamental pepper fruits is given in their contents of capsaicin, vitamin C (VC), flavonoids and total phenols. The study concentrated on the accumulation of capsaicin and dihydrocapsaicin in different tissues of 18 peppers during fruit growth and development. The results showed that the pericarp and placenta contained significantly higher levels of capsaicin than dihydrocapsaicin. Additionally, the placenta contained significantly higher levels of both capsaicin and dihydrocapsaicin compared to the pericarp. The content of capsaicin was in the range of 0-6.7915 mg·g-1, the range of dihydrocapsaicin content was 0-5.329 mg·g-1. Interestingly, we found that the pericarp is rich in VC (5.4506 mg·g-1) and the placenta is high in flavonoids (4.8203 mg·g-1) and total phenols (119.63 mg·g-1). The capsaicin is the most important component using the correlation analysis and principal component analysis. The qPCR results substantiated that the expression of genes in the placenta was significantly higher than that in the pericarp and that the expression of genes in green ripening stage was higher than that in red ripening stage. This study could be utilized to select the best ripening stages and tissues to harvest peppers according to the use of the pepper and to the needs of producers. It not only provides a reference for quality improvement and processing for consumers and market but also provides a theoretical basis for high-quality pepper breeding.