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PURPOSE: Our study aimed to develop a relatively accurate gastric cancer (GC) screening score system for urban residents and to validate the screening efficacy. METHODS: The present study included a derivation cohort (n = 3406) and a validation cohort (n = 868) of urban residents. Applying the full-stack engineering intelligent system platform of Hualian Health Big Data of Shandong University, the clinical physical examination data of subjects were collected. Univariate and multivariate analyses were used to identify risk factors for GC, and subsequently, an optimal prediction rule was established to create three distinct scoring systems. RESULTS: In the GC-risk scoring system I, age, plateletocrit (PCT), carcinoembryonic antigen (CEA), glucose, albumin, creatinine were independent risk factors of GC, with scores ranging from 0 to 28 and optimal cut-off was 15.5. The second scoring system consisted of age, PCT, RDW-CV, CEA, glucose, albumin, and creatinine, with scores ranging from 0 to 31. The optimal cut-off point was determined to be 15.5. The scoring system III comprise of age, sex, PCT, RDW CV, CEA, glucose, with scores ranging from 0 to 21 and optimal cut-off was 10.5. All three scoring systems demonstrated excellent discrimination for GC, achieving an AUC of 0.884, 0.89, and 0.876, respectively. In external validation, the AUC values were 0.654, 0.658, and 0.714. Notably, the GC-risk scoring system III exhibited the highest screening efficiency. CONCLUSIONS: Urban residents benefited from the effective and verified GC-risk scoring systems, which demonstrated excellent performance in identifying individuals with an elevated risk of GC.
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CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.
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OBJECTIVE: An ethmoid-dominant shadow on computed tomography is an indicator of type 2 inflammation, and is one of the main items used to diagnose and classify the severity of eosinophilic chronic rhinosinusitis in the Japanese diagnostic criteria. Ethmoid sinus dominance is examined using the Lund-Mackay scoring system and may be overestimated due to scoring characteristics. We aim to investigate the accuracy of evaluations of ethmoid dominance using the conventional scoring system and the possibility of conducting an objective evaluation using a more detailed other scoring system. METHODS: Patients diagnosed with eosinophilic chronic rhinosinusitis and who underwent bilateral endoscopic sinus surgery were enrolled in the present study. Computed tomography was performed preoperatively on all subjects. The bilateral anterior and posterior ethmoid sinuses and bilateral maxillary sinus were scored, and the ethmoid-to-maxillary ratio was calculated using 3 different scoring systems: Lund-Mackay (each sinus score ranges between 0 and 2), simplified Zinreich (score ranging between 0 and 3), and Zinreich (score ranging between 0 and 5). RESULTS: A total of 149 patients were eligible for the present study. Significant differences were observed in ethmoid-to-maxillary ratio evaluated by the 3 different scoring systems (2.4⯱â¯0.7, 3.0⯱â¯1.1, and 3.7⯱â¯2.2). Only 2 patients were negative for ethmoid dominance by the Lund-Mackay scoring system, while 14 were negative by the simplified-Zinreich and Zinreich scoring systems. Severity changed from the initial grade in 12 patients. CONCLUSIONS: The present results confirmed a potential overestimation when only the Lund-Mackay scoring system was used to assess ethmoid dominance. Ethmoid dominance has been identified as one of the main predictive factors for the long-term postoperative outcomes of eosinophilic chronic rhinosinusitis and is included in the Japanese diagnostic criteria. A detailed evaluation of ethmoid dominance is desirable for more accurate evaluations of the severity and prognosis of eosinophilic chronic rhinosinusitis.
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Senos Etmoidales , Rinitis , Sinusitis , Tomografía Computarizada por Rayos X , Humanos , Senos Etmoidales/diagnóstico por imagen , Enfermedad Crónica , Femenino , Masculino , Sinusitis/diagnóstico por imagen , Sinusitis/cirugía , Persona de Mediana Edad , Rinitis/diagnóstico por imagen , Rinitis/cirugía , Adulto , Índice de Severidad de la Enfermedad , Anciano , Eosinofilia/diagnóstico por imagen , Adulto Joven , Endoscopía , Reproducibilidad de los Resultados , Adolescente , RinosinusitisRESUMEN
Background: The Molecular International Prognostic Scoring System (IPSS-M) has improved the prediction of clinical outcomes for myelodysplastic syndromes (MDS). The Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinical parameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, we validated the IPSS-M and other molecular prognostic models and compared them with the established IPSS-R and AIPSS-MDS models using data from South American patients. Methods: Molecular and clinical data from 145 patients with MDS and 37 patients with MDS/myeloproliferative neoplasms were retrospectively analyzed. Results: Prognostic power evaluation revealed that the IPSS-M (Harrell's concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predicted overall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) and Munich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognostic discrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC: 0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplified low- and high-risk groups for clinical management, after restratifying from IPSS-R (57% and 32%, respectively, hazard ratio [HR]: 2.8; P=0.002) to IPSS-M, 12.6% of patients were upstaged, and 5% were downstaged (HR: 2.9; P=0.001). The AIPSS-MDS recategorized 51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6; P<0.001), consistent with most patients requiring disease-modifying therapy. Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognoses than the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid option for assessing risks for all patients with MDS, especially in resource-limited centers where molecular testing is not currently a standard clinical practice.
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Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
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Aprendizaje Automático , Proteínas , Proteínas/química , Proteínas/metabolismo , Ligandos , Programas Informáticos , Simulación del Acoplamiento MolecularRESUMEN
Voice disorders, such as dysphonia, are common among the general population. These pathologies often remain untreated until they reach a high level of severity. Assisting the detection of voice disorders could facilitate early diagnosis and subsequent treatment. In this study, we address the practical aspects of automatic voice disorders detection (AVDD). In real-world scenarios, data annotated for voice disorders is usually scarce due to various challenges involved in the collection and annotation of such data. However, some relatively large datasets are available for a reduced number of domains. In this context, we propose the use of a combination of out-of-domain and in-domain data for training a deep neural network-based AVDD system, and offer guidance on the minimum amount of in-domain data required to achieve acceptable performance. Further, we propose the use of a cost-based metric, the normalized expected cost (EC), to evaluate performance of AVDD systems in a way that closely reflects the needs of the application. As an added benefit, optimal decisions for the EC can be made in a principled way given by Bayes decision theory. Finally, we argue that for medical applications like AVDD, the categorical decisions need to be accompanied by interpretable scores that reflect the confidence of the system. Even very accurate models often produce scores that are not suited for interpretation. Here, we show that such models can be easily improved by adding a calibration stage-trained with just a few minutes of in-domain data. The outputs of the resulting calibrated system can then better support practitioners in their decision-making process.
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In a competitive and demanding world, academic stress is of increasing concern to students. This systemic, adaptive, and psychological process is composed of stressful stimuli, imbalance symptoms, and coping strategies. The SISCO-II Academic Stress Inventory (SISCO-II-AS) is a psychometric instrument validated in Chile. It evaluates stressors, symptoms, and coping, both individually and globally. For its practical interpretation, a scale is required. Therefore, this study aims to descriptively analyze the SISCO-II-AS and to obtain its corresponding scales. Employing a non-experimental quantitative approach, we administered the SISCO-II-AS to 1,049 second and third-year students from three Chilean universities, with a disproportionate gender representation of 75.21% female to 24.79% male participants. Through descriptive and bivariate analysis, we established norms based on percentiles. For the complete instrument and its subscales, significant differences by sex were identified, with magnitudes varying from small to moderate. For the full instrument and its subscales, bar scale norms by percentile and sex are presented. Each subscale (stressors, physical and psychological reactions, social behavioural reactions, total reaction, and coping) has score ranges defined for low, medium, and high levels. These ranges vary according to the sex of the respondent, with notable differences in stressors and physical, psychological, and social behavioural reactions. This study stands out for its broad and heterogeneous sample, which enriches the representativeness of the data. It offers a comprehensive view of academic stress in college students, identifying distinctive factors and highlighting the importance of gender-sensitive approaches. Its findings contribute to understanding and guide future interventions. By offering a descriptive analysis of the SISCO-II-AS inventory and establishing bar norms, this research aids health professionals and educators in better assessing and addressing academic stress in the student population.
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Adaptación Psicológica , Estrés Psicológico , Humanos , Masculino , Femenino , Estrés Psicológico/diagnóstico , Estudios Transversales , Estudiantes/psicología , Habilidades de AfrontamientoRESUMEN
Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier. From this study, we observed that these variables do affect the automatic model performance, improving the classification of some sleep stages and worsening others.
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Fases del Sueño , Sueño , Humanos , Electroencefalografía/métodosRESUMEN
BACKGROUND: Inadequate endoscopic assessment of disease activity might lead to suboptimal treatment of patients with inflammatory bowel disease (IBD). AIMS: We aimed to determine if the implementation of an educational mobile app could help improving the quality of colonoscopy reports in patients with IBD. METHODS: We retrospectively analyzed a consecutive series of colonoscopy reports in patients with IBD during the period 2016-2023. The sample was divided into two groups: before and after the implementation of an educational mobile app (JEDII app ™). The main outcome was the inclusion of validated activity assessment scoring systems and previously stablished reporting required elements. RESULTS: A total of 883 IBD colonoscopy reports were included for analysis; 621 (70.3%) procedures were performed before the implementation of the app and 262 (29.7%) after. An IBD scoring system was included in 201 (32.4%) and 148 (56.5%) colonoscopy reports before and after the adoption of the mobile app, respectively (p < 0.001). The mean number of recommended elements for quality IBD colonoscopy reporting was significantly increased after the app implementation (4.3 vs. 1.9, p < 0.001). Diagnosis of ulcerative colitis, gastroenterologist as endoscopist, endoscopist with IBD clinical interest, and the implementation of the educational mobile app were independently associated with the inclusion of an IBD score in the colonoscopy report. CONCLUSION: The inclusion of scoring systems and recommended elements for quality IBD colonoscopy report significantly increased after the implementation of an educational mobile app. E-health technologies should be further explored to improve quality of care in patients with IBD.
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Colitis Ulcerosa , Enfermedades Inflamatorias del Intestino , Aplicaciones Móviles , Humanos , Estudios Retrospectivos , Enfermedades Inflamatorias del Intestino/complicaciones , Colonoscopía/métodos , Colitis Ulcerosa/diagnósticoRESUMEN
A retrospective longitudinal study assessing the explanatory and predictive capacity of body condition score (BCS) in dairy cows on disease risk at the individual and herd level was carried out. Data from two commercial grazing herds from the Argentinean Pampa were gathered (Herd A = 2100 and herd B = 2600 milking cows per year) for 4 years. Logistic models were used to assess the association of BCS indicators with the odds for anestrus at the cow and herd level. Population attributable fraction (AFP) was estimated to assess the anestrus rate due to BCS indicators. We found that anestrus risk decreased in cows calving with BCS ≥ 3 and losing ≤ 0.5 (OR: 0.07-0.41), and that anestrus rate decreased in cohorts with a high frequency of cows with proper BCS (OR: 0.22-0.45). Despite aggregated data having a good explanatory power, their predictive capacity for anestrus rate at the herd level is poor (AUC: 0.574-0.679). The AFP varied along the study in both herds and tended to decrease every time the anestrous rate peaked. We conclude that threshold-based models with BCS indicators as predictors are useful to understand disease risk (e.g., anestrus), but conversely, they are useless to predict such multicausal disease events at the herd level.
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The in vitro algaecide activity of quaternary ammonium (QA) against Prototheca isolated from bovine clinical mastitis was investigated, in which the clinical severity was scored, milk samples were subjected to microbiological culture, and algal species were identified by molecular typing. A total of 4275 milk clinical samples of different cows from ten large dairy farms were used. Forty-four (1%) samples of cows from three dairy farms yielded growth of Prototheca, of which 88.6% (39/44) were identified as Prototheca bovis and 11.3% (5/44) as Prototheca sp. by MALDI-TOF MS, whereas 100% of the isolates were identified as P. bovis using PCR sequencing of the cytb gene. Among cows for which clinical severity scoring was available, 78.8% (26/33) and 21.2% (7/33) had mild and moderate infections, respectively, whereas no animal showed severe clinical signs. The algaecide activity of QA in Prototheca was observed in low concentrations among all isolates, in 20.4% (9/44) at 35 ppm, 36.4% (16/44) at 17 ppm, and 43.2% (19/44) at an 8 ppm, in addition to activity on three reference Prototheca strains. Overall, the study highlights the predominance of P. bovis as the causative agent of algal mastitis in bovines. Prototheca induced abnormalities preponderantly in the milk and mammary gland tissue of cows, and to our knowledge, our study is the first to apply clinical severity scoring in protothecal mastitis. In addition, the study underlines the activity of QA in low concentrations against Prototheca, indicating its potential use as an antiseptic/disinfectant in milking facilities and dairy environments.
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Seed physiology is related to functional and metabolic traits of the seed-seedling transition. In this sense, modeling the kinetics, uniformity and capacity of a seed sample plays a central role in designing strategies for trade, food, and environmental security. Thus, POMONA is presented as an easy-to-use multiplatform software designed to bring several logistic and linearized models into a single package, allowing for convenient and fast assessment of seed germination and or longevity, even if the data has a non-Normal distribution. POMONA is implemented in JavaScript using the Quasar framework and can run in the Microsoft Windows operating system, GNU/Linux, and Android-powered mobile hardware or on a web server as a service. The capabilities of POMONA are showcased through a series of examples with diaspores of corn and soybean, evidencing its robustness, accuracy, and performance. POMONA can be the first step for the creation of an automatic multiplatform that will benefit laboratory users, including those focused on image analysis.
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OBJECTIVE: To identify potential clinical utility of polygenic risk scores (PRS) and exposomic risk scores (ERS) for psychosis and suicide attempt in youth and assess the ethical implications of these tools. STUDY DESIGN: We conducted a narrative literature review of emerging findings on PRS and ERS for suicide and psychosis as well as a literature review on the ethics of PRS. We discuss the ethical implications of the emerging findings for the clinical potential of PRS and ERS. RESULTS: Emerging evidence suggests that PRS and ERS may offer clinical utility in the relatively near future but that this utility will be limited to specific, narrow clinical questions, in contrast to the suggestion that population-level screening will have sweeping impact. Combining PRS and ERS might optimize prediction. This clinical utility would change the risk-benefit balance of PRS, and further empirical assessment of proposed risks would be necessary. Some concerns for PRS, such as those about counseling, privacy, and inequities, apply to ERS. ERS raise distinct ethical challenges as well, including some that involve informed consent and direct-to-consumer advertising. Both raise questions about the ethics of machine-learning/artificial intelligence approaches. CONCLUSIONS: Predictive analytics using PRS and ERS may soon play a role in youth mental health settings. Our findings help educate clinicians about potential capabilities, limitations, and ethical implications of these tools. We suggest that a broader discussion with the public is needed to avoid overenthusiasm and determine regulations and guidelines for use of predictive scores.
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Salud Mental , Trastornos Psicóticos , Humanos , Adolescente , Intento de Suicidio/prevención & control , Inteligencia Artificial , Trastornos Psicóticos/diagnóstico , Trastornos Psicóticos/psicología , Factores de RiesgoRESUMEN
BACKGROUND: Acute appendicitis diagnosis can sometimes be a real challenge in pediatric patients. OBJECTIVE: To establish the importance of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and other hematological parameters adjusted for age and sex in the prediction of acute appendicitis, as well as to describe a new scoring system. MATERIAL AND METHODS: Medical records of 946 children hospitalized for acute appendicitis were retrospectively analyzed. A scoring system based on NLR, PLR, lymphocyte/monocyte ratio (LMR), and C-reactive protein (CRP) adjusted for age and sex was developed. RESULTS: Patients were divided into group I, with negative examination, and group II, with acute appendicitis; mean ages were 12.20 ± 2.31 and 11.56 ± 3.11, respectively. Leukocyte count, neutrophil percentage, NLR, PLR, LMR and PCR were higher in group II. The scores ranged from 0 to 8 points; 4.5 was determined to be the best cut-off point for acute appendicitis with the highest area under the curve (0.96), sensitivity (94%), specificity (86%), positive predictive value (97.5%), negative predictive value (65%), accuracy (92.6%) and misclassification rate (7.4%). CONCLUSION: The proposed scoring system, calculated based on patient age and gender, can be used for unnecessary surgeries to be avoided.
ANTECEDENTES: El diagnóstico de apendicitis aguda representa un reto en pacientes pediátricos. OBJETIVO: Establecer la importancia del índice neutrófilos-linfocitos (INL), índice plaquetas-linfocitos (IPL) y otros parámetros hematológicos ajustados por edad y sexo en la predicción de apendicitis aguda, así como describir un nuevo sistema de calificación. MATERIAL Y MÉTODOS: Se analizaron retrospectivamente expedientes clínicos de 946 niños hospitalizados por apendicitis aguda. Se desarrolló un sistema de calificación basado en INL, IPL, ILM y proteína C reactiva (PCR) ajustados por edad y sexo. RESULTADOS: Los pacientes se dividieron en grupo I de exploración negativa y grupo II de apendicitis aguda; las medias de edad correspondientes fueron 12.20 ± 2.31 y 11.56 ± 3.11. El recuento leucocitario, porcentaje de neutrófilos, INL, IPL, ILM y PCR fueron superiores en el grupo II. La calificación osciló entre 0 y 8 puntos; se determinó que 4.5 fue el mejor punto de corte para apendicitis aguda con mayor área bajo la curva (0.96), sensibilidad (94 %), especificidad (86 %), valor predictivo positivo (97.5 %), valor predictivo negativo (65 %), precisión (92.6 %) y tasa de clasificación errónea (7.4 %). CONCLUSIÓN: El sistema de calificación que se propone, calculado por edad y sexo de los pacientes, se puede utilizar para evitar cirugías innecesarias.
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Apendicitis , Humanos , Niño , Estudios Retrospectivos , Apendicitis/diagnóstico , Recuento de Leucocitos , Linfocitos , Neutrófilos , Enfermedad AgudaRESUMEN
Resumen Antecedentes: El diagnóstico de apendicitis aguda representa un reto en pacientes pediátricos. Objetivo: Establecer la importancia del índice neutrófilos-linfocitos (INL), índice plaquetas-linfocitos (IPL) y otros parámetros hematológicos ajustados por edad y sexo en la predicción de apendicitis aguda, así como describir un nuevo sistema de calificación. Material y métodos: Se analizaron retrospectivamente expedientes clínicos de 946 niños hospitalizados por apendicitis aguda. Se desarrolló un sistema de calificación basado en INL, IPL, ILM y proteína C reactiva (PCR) ajustados por edad y sexo. Resultados: Los pacientes se dividieron en grupo I de exploración negativa y grupo II de apendicitis aguda; las medias de edad correspondientes fueron 12.20 ± 2.31 y 11.56 ± 3.11. El recuento leucocitario, porcentaje de neutrófilos, INL, IPL, ILM y PCR fueron superiores en el grupo II. La calificación osciló entre 0 y 8 puntos; se determinó que 4.5 fue el mejor punto de corte para apendicitis aguda con mayor área bajo la curva (0.96), sensibilidad (94 %), especificidad (86 %), valor predictivo positivo (97.5 %), valor predictivo negativo (65 %), precisión (92.6 %) y tasa de clasificación errónea (7.4 %). Conclusión: El sistema de calificación que se propone, calculado por edad y sexo de los pacientes, se puede utilizar para evitar cirugías innecesarias.
Abstract Background: Acute appendicitis diagnosis can sometimes be a real challenge in pediatric patients. Objective: To establish the importance of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and other hematological parameters adjusted for age and sex in the prediction of acute appendicitis, as well as to describe a new scoring system. Material and methods: Medical records of 946 children hospitalized for acute appendicitis were retrospectively analyzed. A scoring system based on NLR, PLR, lymphocyte/monocyte ratio (LMR), and C-reactive protein (CRP) adjusted for age and sex was developed. Results: Patients were divided into group I, with negative examination, and group II, with acute appendicitis; mean ages were 12.20 ± 2.31 and 11.56 ± 3.11, respectively. Leukocyte count, neutrophil percentage, NLR, PLR, LMR and PCR were higher in group II. The scores ranged from 0 to 8 points; 4.5 was determined to be the best cut-off point for acute appendicitis with the highest area under the curve (0.96), sensitivity (94%), specificity (86%), positive predictive value (97.5%), negative predictive value (65%), accuracy (92.6%) and misclassification rate (7.4%). Conclusion: The proposed scoring system, calculated based on patient age and gender, can be used for unnecessary surgeries to be avoided.
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BACKGROUND: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. OBJECTIVE: Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. METHOD: We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. RESULTS: The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. CONCLUSION: The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.
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The cosmetic industry has been committed to promoting less hazardous products to reduce the environmental impacts of cosmetic ingredients. This requires identifying safer cosmetic ingredients for developing cosmetic formulations that are less harmful to the environment. However, one of the challenges in developing eco-friendly cosmetics relies on integrating all environmental hazard (EH) information of cosmetic ingredients to select the most eco-friendly ones (i.e., ingredients least harmful to the aquatic environment). Thus, we developed a hazard scoring tool (IARA matrix), which integrates data on biodegradation, bioaccumulation, and acute aquatic toxicity, providing a hazard index to classify cosmetic ingredients (raw materials) into categories of EH (low, moderate, high, or very high). The classification of the IARA was based on parameters established by Cradle to Cradle (C2C), the US Environmental Protection Agency (USEPA), and European Regulation 1272/2008, considering the most conservative values of each source. The Leopold matrix was employed as a model for the tool, using a numerical scale from 0 to 6 (lowest to highest EH). According to the IARA, we have successfully demonstrated that ultraviolet (UV) filter ingredients have the highest EH out of 41 cosmetic ingredients commonly used for rinse-off products. In addition to UV filters, triclosan (bactericide) and dimethicone (emollient) presented the second-highest EH for aquatic ecosystems, and humectants presented the lowest hazard index. By applying the IARA in the case study of rinse-off products, we have estimated that the aquatic hazard of cosmetic products can be reduced 46% by identifying less hazardous ingredients and combining them into a cosmetic formulation. In summary, the IARA tool allows the estimation of the EH of cosmetic ingredients, provides safer products, and helps achieve sustainability for cosmetic products. Integr Environ Assess Manag 2023;19:1619-1635. © 2023 SETAC.
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Cosméticos , Triclosán , Estados Unidos , Ecosistema , Cosméticos/toxicidad , AmbienteRESUMEN
INTRODUCTION: In the health care area, tuition is an essential part to provide the instrument that proves the graduates have acquired the necessary skills in their specialties. OBJECTIVE: Evaluation of the improvement in quality of resources for residents after standardized digital training program with rubrics. METHODS: Prospective observational study of first year medical residents in seven medical specialties in four different training centers. Five dimensions were considered to scale the quality of medical resident research: Validation of rubric in investigation methodology topics for each block in b-learning mode; initial and ending evaluation; colloquium investigation rubric; results of final investigation; satisfaction survey of 360 degrees. The instruments were validated using the delphi method with a minimum agreement of 0.8. We considered global values greater than 80 points as good quality. RESULTS: 85 medical residents participated and obtained a final average of 80.62 (±9.59), and the satisfaction of the course was qualified as excellent/good in 82.5%. A positive relation was observed between the scope of the evaluation and the level of satisfaction. Mean quality score for the course was good. There is no relationship between the research experience of the students and the final average r = 0.123 (p = 0.291). CONCLUSIONS: The implementation of research seminars in b-learning mode results in improving the education program for health residents after a training program with a rubric system and their acquiring research skills, and, as a consequence, the final product also showed better quality, even when the student did not have any experience in a scientific publication.
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The present study aims to evaluate the accuracy of the prognostic discrimination and prediction of the short-term mortality of the Marshall computed tomography (CT) classification and Rotterdam and Helsinki CT scores in a cohort of TBI patients from a low- to middle-income country. This is a post hoc analysis of a previously conducted prospective cohort study conducted in a university-associated, tertiary-level hospital that serves a population of >12 million in Brazil. Marshall CT class, Rotterdam and Helsinki scores, and their components were evaluated in the prediction of 14-day and in-hospital mortality using Nagelkerk's pseudo-R 2 and area under the receiver operating characteristic curve. Multi-variate regression was performed using known outcome predictors (age, Glasgow Coma Scale, pupil response, hypoxia, hypotension, and hemoglobin values) to evaluate the increase in variance explained when adding each of the CT classification systems. Four hundred forty-seven patients were included. Mean age of the patient cohort was 40 (standard deviation, 17.83) years, and 85.5% were male. Marshall CT class was the least accurate model, showing pseudo-R 2 values equal to 0.122 for 14-day mortality and 0.057 for in-hospital mortality, whereas Rotterdam CT scores were 0.245 and 0.194 and Helsinki CT scores were 0.264 and 0.229. The AUC confirms the best prediction of the Rotterdam and Helsinki CT scores regarding the Marshall CT class, which presented greater discriminative ability. When associated with known outcome predictors, Marshall CT class and Rotterdam and Helsinki CT scores showed an increase in the explained variance of 2%, 13.4%, and 21.6%, respectively. In this study, Rotterdam and Helsinki scores were more accurate models in predicting short-term mortality. The study denotes a contribution to the process of external validation of the scores and may collaborate with the best risk stratification for patients with this important pathology.
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Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.