Renewing the potential of rice crop residues as value-added products in the cosmetics industry.

Purpose of this study is to explore the extraction of potentially valuable cosmetic ingredients from rice crop residues, aiming to mitigate their environmental impact. Methods: We employed AOAC methods to analyze the fat, protein, ash, fiber, soluble, and insoluble carbohydrate content in these residues. To identify sugars rich in galactose and acidic sugars, a total soluble carbohydrate extraction was performed. Cellulose, as part of the insoluble carbohydrates, was isolated through alkaline and acid hydrolysis, while sodium silicate was derived from the ash. Characterization of insoluble cellulose and silicate involved techniques like FTIR, DSC, PXRD, microphotography, porosity assessments, and water absorption studies. For proteins, alkaline solubilization and precipitation at the isoelectric point were utilized, with quantification via BCA and amino acid profiling through gas chromatography. Evaluation of radical scavenging capacity using DPPH led to the calculation of apparent molecular weight via SDS-PAGE. Results: The results revealed low levels of gum, mucilage, and pectin in both residues, contrasting with a high concentration of insoluble polysaccharides. Among these, Iß cellulose displayed potential attributes for cosmetic applications due to its oil and water adsorption characteristics. However, silicates obtained from the ashes did not exhibit direct use potential. In terms of protein extraction, we observed antioxidant properties, with enhanced performance through enzymatic hydrolysis, achieving a hydrolysis degree of 30.41% and a DPPH radical absorption rate exceeding 70%. Conclusion: Rice residues, particularly husk and straw, shown valuable substances suitable for potential cosmetic applications, encompassing cellulose, hydrolyzed proteins, and ash as a silicate precursor.

Association between polymorphisms of the VKORC1 and CYP2C9 genes and warfarin maintenance dose in Peruvian patients.

AIMS: The aim of this study was to investigate the association between VKORC1 and CYP2C9 genes polymorphisms and the maintenance dose of warfarin in Peruvian patients. METHODS: An observational study was conducted on outpatients from the Hospital Grau ESSALUD in Lima, Peru. The participants were selected using nonprobabilistic convenience sampling. Inclusion criteria required patients to have been on anticoagulation therapy for >3 months, maintain stable doses of warfarin (consistent dose for at least 3 outpatient visits), and maintain an international normalized ratio within the therapeutic range of 2.5-3.5. DNA samples were obtained from peripheral blood for gene analysis. RESULTS: Seventy patients (mean age of 69.6 ± 13.4 years, 45.7% female) were included in the study. The average weekly warfarin dose was 31.6 ± 15.2 mg. The genotypic frequencies of VKORC1 were as follows: 7.1% (95% confidence interval, 2.4-15.9) for AA; 44.3% (32.4-56.7) for GA; and 48.6% (36.4-60.8) for GG. No deviation from the Hardy-Weinberg equilibrium was observed in the variants studied (P = .56). The mean weekly warfarin doses for AA, GA and GG genotypes were 16.5 ± 2.9, 26.5 ± 9.5 and 37.9 ± 17.1 mg, respectively (P < .001). The genotypic frequencies of CYP2C9 were as follows: 82.8% (72.0-90.8) for CC (*1/*1); 4.3% (1.0-12.0) for CT (*1/*2); and 12.9% (6.1-23.0) for TT (*2/*2). We did not find a significant association between the CYP2C9 gene polymorphism and the dose of warfarin. CONCLUSIONS: The AA genotype of the VKORC1 gene was associated with a lower maintenance dose of warfarin in Peruvian patients.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . Material and methods: The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Results: Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. Conclusions: The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study.

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.

Introduction: For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy. Materials and methods: In total, 90 patients from three centers (30 CT-MR prostate pairs/center) underwent treatment using volumetric modulated arc therapy for prostate cancer (PCa) (60 Gy in 20 fractions). T2 MRI images were acquired in addition to computed tomography (CT) images for treatment planning. The DL model was a 2D supervised conditional generative adversarial network (Pix2Pix). Patient images underwent preprocessing steps, including nonrigid registration. Seven different supervised models were trained, incorporating patients from one, two, or three centers. Each model was trained on 24 CT-MR prostate pairs. A generic model was trained using patients from all three centers. To compare sCT and CT, the mean absolute error in Hounsfield units was calculated for the entire pelvis, prostate, bladder, rectum, and bones. For dose analysis, mean dose differences of D 99% for CTV, V 95% for PTV, Dmax for rectum and bladder, and 3D gamma analysis (local, 1%/1 mm) were calculated from CT and sCT. Furthermore, Wilcoxon tests were performed to compare the image and dose results obtained with the generic model to those with the other trained models. Results: Considering the image results for the entire pelvis, when the data used for the test comes from the same center as the data used for training, the results were not significantly different from the generic model. Absolute dose differences were less than 1 Gy for the CTV D 99% for every trained model and center. The gamma analysis results showed nonsignificant differences between the generic and monocentric models. Conclusion: The accuracy of sCT, in terms of image and dose, is equivalent to whether MRI images are generated using the generic model or the monocentric model. The generic model, using only eight MRI-CT pairs per center, offers robust sCT generation, facilitating PCa MRI-only radiotherapy for routine clinical use.

Sloanea chocoana and S. pittieriana (Elaeocarpaceae): Chemical and Biological Studies of Ethanolic Extracts and Skincare Properties.

The Colombian Chocó is known for its rich biodiversity and to harbor plant species that are under-explored, including the genus Sloanea. This study aimed to analyze the chemical composition of derivatized ethanolic extracts from S. chocoana and S. pittieriana using BSTFA and TMCS through GC-MS, and to assess cell viability of immortalized human non-tumorigenic keratinocytes (HaCaT) and periodontal ligament fibroblast cells using crude extracts through MTS assay. Antioxidant and photoprotective properties were determined using DPPH assay and spectrophotometry. Antifungal activity of extracts against Candida species was developed following the CLSI standard M27, 4th ed. The sun protective factor (SPF) and UVA/UVB ratio values were calculated using the Mansur equation and the Boots star rating system. The critical wavelength (λc) was determined by calculating the integrated optical density curve's area. The transmission of erythema and pigmentation was calculated through equations that use constants to calculate the flux of erythema and pigmentation. The GC-MS analysis identified 37 compounds for S. chocoana and 38 for S. pittieriana, including alkaloids, triterpenoids, and polyphenolics, among others. Both extracts exhibited proliferative effects on periodontal ligament fibroblasts, did not affect the viability of HaCaT cells, and showed excellent antioxidant activities (46.1% and 43.7%). Relevant antifungal activity was observed with S. pittieriana extract against Candida albicans (GM-MIC: 4 µg/mL), followed by C. auris and C. glabrata (GM-MIC: 32 µg/mL), while S. chocoana extract was active against C. albicans and C. glabrata (GM-MIC: 16 and 32 µg/mL, respectively). High SPF values (31.0 and 30.0), λc (393.98 and 337.81 nm), UVA/UVB ratio (1.5 and 1.2), and low percentage of transmission of erythema and pigmentation were determined for S. chocoana and S. pittieriana, respectively. Results showed that species of Sloanea constitute a promising alternative as ingredients for developing skincare products, and exhaustive studies are required for their sustainable uses.

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review.

CONTEXT: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases. OBJECTIVE: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC). EVIDENCE ACQUISITION: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool. EVIDENCE SYNTHESIS: In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported. CONCLUSIONS: This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice. PATIENT SUMMARY: Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy.

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning.

Voxel-based analysis: Roadmap for clinical translation.

Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization.

Background and Purpose: The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Materials and Methods: Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results: The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions: Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

Preparation, Physicochemical Characterization, and Stability Study of Lippia origanoides Essential Oil-Based Nanoemulsion as a Topical Delivery System.

INTRODUCTION: Fungal diseases are a priority in research, development, and health care, according to the WHO, mainly due to Candida spp. Essential oils (EOs) of the genus Lippia have demonstrated broad antimicrobial biological activity. Previous studies identified the anti-Candida potential of a thymol/p-cymene chemotype EO from Lippia origanoides H.B.K coded "0018". Nanoemulsions favor the biological activity of EOs and overcome limitations such as low solubility, instability against oxidizing agents, pH, light, and low permeability. To develop, characterize, and adjust a prototype of an O/W nanoemulsion containing the "0018" EO from Lippia origanoides for its evaluation in an In vitro permeability study. METHOD: Nanoemulsions were obtained using a high energy high shear method. Their particle size distribution, Z potential, viscosity, pH, encapsulation efficiency (EE), thermodynamic stability and the Turbiscan Stability Index (TSI) were evaluated. The nanoemulsion prototype was adjusted to improve performance characteristics and microbiological efficacy. Thymol was used as an analyte in the EO quantification using UHPLC-DAD. RESULTS: An O/W nanoemulsion with hydrodynamic diameter <200 nm and polydispersity index <0.3, EE >95%, with TSI < 1.5, anti-Candida albicans efficiency >95% was obtained; permeable with a flow of 6.0264 µg/cm2/h and permeability coefficient of 1.3170x10-3 cm/h. CONCLUSION: A pharmaceutical formulation prototype is obtained that maintains the physical and physicochemical characteristics over time. Permeability is verified in an in-vitro model. It is proposed to evaluate its antifungal activity in preclinical or clinical studies as a contribution to the treatment of topical fungal diseases caused by Candida spp., through the use of biological resources and Colombian biodiversity.

Promoter hypermethylation of RARB and GSTP1 genes in plasma cell-free DNA as breast cancer biomarkers in Peruvian women.

BACKGROUND: Promoter hypermethylation is one of the enabling mechanisms of hallmarks of cancer. Tumor suppressor genes like RARB and GSTP1 have been reported as hypermethylated in breast cancer tumors compared with normal tissues in several populations. This case-control study aimed to determine the association between the promoter methylation ratio (PMR) of RARB and GSTP1 genes (separately and as a group) with breast cancer and its clinical-pathological variables in Peruvian patients, using a liquid biopsy approach. METHODS: A total of 58 breast cancer patients and 58 healthy controls, matched by age, participated in the study. We exacted cell-free DNA (cfDNA) from blood plasma and converted it by bisulfite salts. Methylight PCR was performed to obtain the PMR value of the studied genes. We determined the association between PMR and breast cancer, in addition to other clinicopathological variables. The sensitivity and specificity of the PMR of these genes were obtained. RESULTS: A significant association was not found between breast cancer and the RARB PMR (OR = 1.90; 95% CI [0.62-6.18]; p = 0.210) or the GSTP1 PMR (OR = 6.57; 95% CI [0.75-307.66]; p = 0.114). The combination of the RARB + GSTP1 PMR was associated with breast cancer (OR = 2.81; 95% CI [1.02-8.22]; p = 0.026), controls under 50 years old (p = 0.048), patients older than 50 (p = 0.007), and postmenopausal (p = 0.034). The PMR of both genes showed a specificity of 86.21% and a sensitivity of 31.03%. CONCLUSION: Promoter hypermethylation of RARB + GSTP1 genes is associated with breast cancer, older age, and postmenopausal Peruvian patients. The methylated promoter of the RARB + GSTP1 genes needs further validation to be used as a biomarker for liquid biopsy and as a recommendation criterion for additional tests in asymptomatic women younger than 50 years.

Exploring the Potential of Extracts from Sloanea medusula and S. calva: Formulating Two Skincare Gels with Antioxidant, Sun Protective Factor, and Anti-Candida albicans Activities.

Sloanea is a plant genus, native to tropical regions, used in medicinal practices for its anti-inflammatory properties. This study aimed to determine the antioxidant activity, sun protective factor (SPF), and antifungal of extracts obtained from two species of Sloanea and to develop extract-based gels with antioxidants, photoprotective, and anti-Candida albicans effects. Ethanolic extracts from S. medusula and S. calva collected in Chocó, Colombia, were used for antioxidant activity and SPF determination using the DPPH assay and the Mansur equation, respectively. Extracts were characterized using HPLC-MS and used to prepare the gels. The viscosity of the extract-based gels was evaluated using an MCR92 rheometer. In addition, the anti-Candida activity of extracts against five yeasts and anti-C. albicans of gels were evaluated following the Clinical and Laboratory Standards Institute M27, 4th Edition. High DPPH radical scavenging activity (42.4% and 44.7%) and a high SPF value (32.5 and 35.4) were obtained for the extracts of S. medusula and S. calva, respectively. Similarly, extract-based gels showed significant DPPH radical scavenging activity of 54.5% and 53.0% and maximum SPF values of 60 and 57. Extract from S. medusula showed an important antifungal activity against C. albicans (minimal inhibitory concentration (MIC) of 2 µg/mL). In contrast, S. calva extract was active against C. krusei, C. albicans (MIC of 2 µg/mL) and C. tropicalis (MIC of 4 µg/mL). Sloanea medusula gel (0.15%) exhibited an important C. albicans growth inhibition (98%), while with S. calva gel (0.3%) growth inhibition was slightly lower (76%). Polyphenolic and triterpenoid compounds were tentatively identified for S. medusula and S. calva, respectively. Both extracts can be considered promising sources for developing photoprotective gels to treat skin infections caused by C. albicans.

Asociación entre el polimorfismo Val66Met del gen BDNF y el desarrollo de disfunción ejecutiva en pacientes con cáncer de mama

Introducción. La disfunción ejecutiva asociada a quimioterapia es un efecto adverso del tratamiento antineoplásico convencional y afecta a un porcentaje considerable de personas. Se ha reportado que la presencia de ciertos polimorfismos en genes relevantes puede causar mayor susceptibilidad a padecerlo. Objetivo. Determinar la relación entre el polimorfismo Val66Met (196 G>A) del gen BDNF y el desarrollo de disfunción ejecutiva en mujeres con cáncer de mama tratadas con quimioterapia. Métodos. Se evaluaron a 73 pacientes mujeres con cáncer de mama para determinar disfunción ejecutiva antes y después de la quimioterapia. La evaluación fue realizada con la prueba INECO Frontal Screening (IFS). Se determinó el genotipo (GG=Val/Val, GA=Val/Met y AA=Met/Met) por PCR y secuenciamiento del gen BDNF. El análisis de asociación se realizó mediante el cálculo del odds ratio (OR). Resultados. El 13,7% (n = 10) de pacientes presentó el alelo A (GA y AA), además obtuvieron puntajes significativamente menores de la prueba IFS comparado con las homocigotas GG (p A) del gen BDNF y el desarrollo de disfunción ejecutiva en pacientes con cáncer de mama tratadas con quimioterapia; sin embargo, las portadoras del alelo A (Met) presentaron puntajes menores en la evaluación cognitiva.

Introduction. Chemotherapy-associated executive dysfunction is an adverse effect of conventional antineoplastic treatment that affects many patients. It has been reported that the presence of specific polymorphisms in key genes can cause a greater susceptibility to develop this condition. Objective. To determine the relationship between the Val66Met polymorphism (196 G>A) of the BDNF gene and the development of executive dysfunction in female patients with breast cancer treated with chemotherapy. Methods. 73 female breast cancer patients were evaluated for executive dysfunction before and after chemotherapy. The evaluation was carried out with the INECO Frontal Screening test (IFS). The genotype (GG=Val/Val, GA=Val/Met and AA=Met/Met) was determined by PCR and sequencing of BDNF gene. Association analysis was performed by calculating the Odds Ratio (OR) and by quantitative comparison. Results. 13.7% (n = 10) of the sample presented the allele A (GA and AA), which obtained significantly lower scores in the IFS test compared to the homozygous GG (p A) polymorphism of the BDNF gene and the development of executive dysfunction in patients with breast cancer treated with chemotherapy. However, patients with the allele A (Met) presented significant lower scores in the cognitive assessment.

Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.

BACKGROUND: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.

Background and purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours. Materials and methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter. Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations. Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images.

Análisis de posibles peligros en leche materna donada. Revisión / Analysis of potential hazards in donated breast milk. Review

La leche materna donada es un recurso de alto valor que puede ser utilizado para la alimentación de neonatos hospitalizados y a término, por tanto, garantizar su inocuidad es imperativo. Esta revisión de literatura reúne los principales peligros de naturaleza física, química y microbiológica identificados en leche materna, con la intención de proveer una referencia que los consolide de tal forma que la información pueda ser utilizada por bancos de leche humana, gobiernos y agencias regulatorias para establecer mecanismos para su prevención y control. Se realizó una revisión de literatura entre agosto del 2021 y octubre del 2022, utilizando buscadores y descriptores específicos para peligros de transmisión alimentaria en leche materna. Se incluyeron estudios publicados en español o en inglés. Se identificaron 31 agentes biológicos patógenos incluyendo bacterias, virus y parásitos. Como peligros químicos se reportaron medicamentos, drogas, cafeína, infusiones herbales, micotoxinas, alérgenos, especias, suplementos nutricionales, contaminantes ambientales y desinfectantes. Se alerta sobre la presencia potencial de plástico y vidrio de tamaño menor a 7 mm proveniente del ambiente de extracción y recipientes. La presencia de peligros microbiológicos y químicos en leche materna puede darse por transmisión vertical, temperaturas inadecuadas durante el almacenamiento y contaminación en el proceso. La presencia de peligros físicos se relaciona con la manipulación de los implementos en etapas posteriores a la extracción. Se requiere prestar atención a los hábitos de la madre para prevenir peligros químicos, así como más investigación relacionada con micotoxinas en leche materna(AU)

Donated breast milk is a highvalue resource which can be used to feed hospitalized neonates and full-term infants, therefore, ensuring its safety is imperative. This literature review presents the main hazards of physical, chemical and microbiological nature identified in human milk, with the intention of providing a reference that consolidates the reported hazards reported, so the information can be used by human milk banks, governments and regulatory agencies to establish prevention and control mechanisms. A literature review was carried out between August 2021 and October 2022, using search engines and specific descriptors for foodborne hazards in breast milk. Studies published in Spanish and English were considered. 31 pathogenic biological agents including bacteria, viruses and parasites were identified. Medications, drugs, caffeine, herbal infusions, mycotoxins, allergens, spices, nutritional supplements, contaminants of environmental origin and disinfectants were reported as chemical hazards. No physical hazards were identified, however the potential presence of plastic and glass smaller than 7 mm from the extraction environment or containers is alerted. Presence of microbiological and chemical hazards can be due to vertical transmission, inadequate temperature of storing, contamination during extraction, packaging, and infant feeding. Whereas presence of physical hazards is related to implements handling after extraction. Attention to hygiene and habits of the mother to prevent chemical hazards and further research related to mycotoxins in human milk is required(AU)

Multi T1-weighted contrast imaging and T1 mapping with compressed sensing FLAWS at 3 T.

OBJECTIVE: The Fluid And White matter Suppression (FLAWS) MRI sequence provides multiple T1-weighted contrasts of the brain in a single acquisition. However, the FLAWS acquisition time is approximately 8 min with a standard GRAPPA 3 acceleration factor at 3 T. This study aims at reducing the FLAWS acquisition time by providing a new sequence optimization based on a Cartesian phyllotaxis k-space undersampling and a compressed sensing (CS) reconstruction. This study also aims at showing that T1 mapping can be performed with FLAWS at 3 T. MATERIALS AND METHODS: The CS FLAWS parameters were determined using a method based on a profit function maximization under constraints. The FLAWS optimization and T1 mapping were assessed with in-silico, in-vitro and in-vivo (10 healthy volunteers) experiments conducted at 3 T. RESULTS: In-silico, in-vitro and in-vivo experiments showed that the proposed CS FLAWS optimization allows the acquisition time of a 1 mm-isotropic full-brain scan to be reduced from [Formula: see text] to [Formula: see text] without decreasing image quality. In addition, these experiments demonstrate that T1 mapping can be performed with FLAWS at 3 T. DISCUSSION: The results obtained in this study suggest that the recent advances in FLAWS imaging allow to perform multiple T1-weighted contrast imaging and T1 mapping in a single [Formula: see text] sequence acquisition.

Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma.

BACKGROUND: Predictive tools can be useful for adapting surveillance or including patients in adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current models have been built using traditional statistical modelling and prespecified variables, which limits their performance. OBJECTIVE: To investigate the performance of machine learning (ML) framework to predict recurrence after RCC surgery and compare them with current validated models. DESIGN, SETTING, AND PARTICIPANTS: In this observational study, we derived and tested several ML-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XG boost]) to predict recurrence of patients who underwent radical or partial nephrectomy for a nonmetastatic RCC, between 2013 and 2020, at 21 French medical centres. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary end point was disease-free survival. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using the Brier score. ML models were compared with four conventional prognostic models, using decision curve analysis (DCA). RESULTS AND LIMITATIONS: A total of 4067 patients were included in this study (3253 in the development cohort and 814 in the validation cohort). Most tumours (69%) were clear cell RCC, 40% were of high grade (nuclear International Society of Urological Pathology grade 3 or 4), and 24% had necrosis. Of the patients, 4% had nodal involvement. After a median follow-up of 57 mo (interquartile range 29-76), 523 (13%) patients recurred. ML models obtained higher c-index values than conventional models. The RSF yielded the highest c-index values (0.794), followed by S-SVM (c-index 0.784) and XG boost (c-index 0.782). In addition, all models showed good calibration with low integrated Brier scores (all integrated brier scores <0.1). However, we found calibration drift over time for all models, albeit with a smaller magnitude for ML models. Finally, DCA showed an incremental net benefit from all ML models compared with conventional models currently used in practice. CONCLUSIONS: Applying ML approaches to predict recurrence following surgical resection of RCC resulted in better prediction than that of current validated models available in clinical practice. However, there is still room for improvement, which may come from the integration of novel biological and/or imaging biomarkers. PATIENT SUMMARY: We found that artificial intelligence algorithms could better predict the risk of recurrence after surgery for a localised kidney cancer. These algorithms may help better select patients who will benefit from medical treatment after surgery.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.