Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study.

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Establishing a safe anesthesia concentration window for Nile tilapia (Oreochromis niloticus) (Linnaeus 1758) by monitoring cardiac activity in eugenol immersion baths.

Nile tilapia, Oreochromis niloticus, is the most cultivated fish species in the world, due to its low cost, high growth rate, environmental adaptability, and resistance to disease and stress. Anesthetics for fish become necessary in management because they minimize mortality during transport and maintenance of ponds, one of the most used anesthetics is clove oil, which has eugenol as the major substance, representing 90-95 % of clove oil. The present study evaluates the effect of eugenol on cardiac activity in Oreochromis niloticus specimens and relates it to behavioral data to determine a concentration window for safe anesthesia. For the comportamental analysis, was used five treatments (50, 75, 100, 125, and 150 µL·L-1) were evaluated and for the eletrocardiographic test was used seven groups (Control, Vehicle, 50, 75, 100, 125, and 150 µL·L-1), n = 9/treatment, totaling 108 animals. Behavioral and electrocardiographic tests were performed on all treatments during induction and recovery. The results of the behavioral tests demonstrated the reversibility of the effects with recovery of the posture reflex, varying according to the concentration. The ECG results showed a slow recovery because, at concentrations above 100 µL·L-1, there was no full reversibility of the cardiac effects in the observed experiment time, which could cause greater changes in the tilapia hemodynamics, which led us to identify a window for safe anesthesia. Eugenol is an effective anesthetic in Nile tilapia juveniles when used in concentrations ranging from 50 to 100 µL·L-1, if there is a need for anesthetic deepening, doses above 100 µL·L-1, however, the animals must be monitored due to hemodynamic changes.

Lippia origanoides essential oil possesses anticonvulsant effect in pentylenetetrazol-induced seizures in rats: a behavioral, electroencephalographic, and electromyographic study.

Epilepsy is a neuronal disorder characterized by abnormal excitability of the brain, leading to seizures. Only around 66% of the epileptic patients respond adequately to treatment with existing conventional anticonvulsants, making it necessary to investigate new antiepileptic drugs. The growing research into natural products and their pharmacological properties has become increasingly promising, particularly in the study of essential oils, which are already widely used in popular culture for treating various diseases. The present study evaluated the anticonvulsant effects of Lippia origanoides essential oil (LOEO) (100 mg/kg i. p.) compared to diazepam (DZP) (5 mg/kg i. p.), and the combined administration of these two substances to control convulsions induced by pentylenetetrazol (PTZ) (60 mg/kg i. p.). This evaluation was carried out using 108 male Wistar rats, which were divided into two experiments. Experiment 1-Behavioral assessment: The animals were divided into 4 groups (n = 9): (I) saline solution + PTZ, (II) DZP + PTZ, (III) LOEO + PTZ, (IV) LOEO + DZP + PTZ. The convulsive behavior was induced 30 min after the administration of the tested anticonvulsant drugs, and the observation period lasted 30 min. Experiment 2- Electrocorticographic evaluation: The animals were divided into 8 groups (n = 9): (I) saline solution; (II) LOEO; (III) DZP; (IV) LOEO + DZP; (V) saline + PTZ, (VI) DZP + PTZ (VII) LOEO + PTZ, (VIII) LOEO + DZP + PTZ. PTZ was administered 30 min after LOEO and DZP treatments and electrocorticographic activity was assessed for 15 min. For the control groups, electromyographic recordings were performed in the 10th intercostal space to assess respiratory rate. The results demonstrated that Lippia origanoides essential oil increased the latency time for the appearance of isolated clonic seizures without loss of the postural reflex. The animals had a more intense decrease in respiratory rate when combined with LOEO + DZP. EEG recordings showed a reduction in firing amplitude in the LOEO-treated groups. The combining treatment with diazepam resulted in increased anticonvulsant effects. Therefore, treatment with Lippia origanoides essential oil was effective in controlling seizures, and its combination with diazepam may represent a future option for the treatment of difficult-to-control seizures.

Unmasking hidden risks: The surprising link between PDE5 inhibitors and seizure susceptibility.

BACKGROUND: Phosphodiesterase 5 inhibitors (PDE5i) are the first line treatment for erectile dysfunction; however, several articles and case reports have shown central nervous system effects, that can cause seizures in susceptible patients. This study aims to describe the changes caused by the use of Sildenafil and Tadalafil through the analysis of abnormalities expressed in the electrocorticogram (ECoG) of rats and evaluate the seizure threshold response and treatment of seizures with anticonvulsants. MATERIALS AND METHODS: The study used 108 rats (Wistar). Before surgery for electrode placement in dura mater, the animals were randomly separated into 3 experiments for electrocorticogram analysis. Experiment 1: ECoG response to using PD5i (Sildenafil 20mg/kg and Tadalafil 2.6mg/kg p.o.). Experiment 2: ECoG response to the use of PD5i in association with Pentylenetetrazole (PTZ-30 mg/kg i.p.), a convulsive model. Experiment 3: ECoG response to anticonvulsant treatment (Phenytoin, Phenobarbital and Diazepam) of seizures induced by association IPDE5 + PTZ. All recordings were made thirty minutes after administration of the medication and analyzed for ten minutes, only once. We considered statistical significance level of *p<0.05, **p<0.01 and ***p < 0.001. RESULTS: After administration of Sildenafil and Tadalafil, there were increases in the power of recordings in the frequency bands in oscillations in alpha (p = 0.0920) and beta (p = 0.602) when compared to the control group (p<0.001). After the use of Sildenafil and Tadalafil associated with PTZ, greater potency was observed in the recordings during seizures (p<0.001), however, the Sildenafil group showed greater potency when compared to Tadalafil (p<0.05). Phenobarbital and Diazepam showed a better response in controlling discharges triggered by the association between proconvulsant drugs. CONCLUSIONS: PDE5i altered the ECoG recordings in the rats' motor cortexes, demonstrating cerebral asynchrony and potentiating the action of PTZ. These findings demonstrate that PDE5i can lower the seizure threshold.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal / Desenvolvimento de rede neural convolucional para o diagnóstico radiográfico de osteoartrite dos joelhos no ELSA-Brasil Musculoesquelético

Abstract Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results: The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion: The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.

Resumo Objetivo: Desenvolver um modelo computacional - rede neural convolucional (RNC) - treinado com radiografias da linha de base do Estudo Longitudinal de Saúde do Adulto Musculoesquelético (ELSA-Brasil Musculoesquelético), para a classificação automática de osteoartrite dos joelhos. Materiais e Métodos: Trata-se de um estudo transversal abrangendo todos os exames da linha de base do ELSA-Brasil Musculoesquelético (5.660 radiografias dos joelhos em incidência posteroanterior). Os exames foram interpretados por médico radiologista com treinamento específico e calibração previamente publicada. Resultados: A RNC desenvolvida apresentou área sob a curva característica de operação do receptor de 0,866 (IC 95%: 0,842-0,882). O modelo pode ser calibrado para alcançar, não simultaneamente, valores máximos de 0,907 para acurácia, 0,938 para sensibilidade e 0,994 para especificidade. Conclusão: A RNC desenvolvida pode ser utilizada como ferramenta de triagem, reduzindo o número total de exames avaliados pelos radiologistas do estudo, e/ou como ferramenta de segunda leitura, contribuindo com a redução de possíveis erros de interpretação.

Role of Corneal Epithelial Measurements in Differentiating Eyes with Stable Keratoconus from Eyes that Are Progressing.

Purpose: To evaluate measures of corneal epithelium in eyes that showed documented signs of keratoconus (KC) progression and compare with stable eyes and healthy controls. Also, to determine the correlation of these epithelial parameters with maximum keratometry (K max) and pachymetry. Design: Prospective, observational, comparative study. Participants: One-hundred and fifty eyes from 150 patients. The study included 50 eyes from patients with documented KC progression, 50 eyes with stable KC, and 50 clinically normal eyes to serve as controls. Methods: A spectral-domain (SD)-OCT imaging was obtained in all eyes, and mean values were compared between the groups. The correlation of epithelial parameters with K max and thinnest pachymetry was also investigated. Main Outcome Measures: For the purposes of this study, the epithelial measures maximum, minimum, superior, and inferior values as well as the difference between the minimum and maximum (min-max) and epithelial standard deviation were considered, obtained from SD-OCT and compared between groups. Measurements of the thinnest point and min-max in pachymetry were also recorded. Results: The only epithelial parameter that presented a statistically significant difference between stable and progressive KC was epithelium min-max. Although stable KC presented epithelium min-max mean values of -18.2 ± 6.6, progressive KC eyes presented mean values of -23.4 ± 10.3 (P < 0.0001). Epithelial maximum (P = 0.16), minimum (P = 0.25), superior (P = 0.28), inferior (P = 0.23), and standard deviation (P = 0.25) values were not significantly different between stable and progressive eyes. Difference min-max pachymetry points in stable (-108.3 ± 33.5) and progressive KC (-115.2 ± 56.0) were not significantly different (P = 0.723). There was no significant correlation between epithelium min-max with corneal thinning (P = 0.39) or K max (P = 0.09) regardless of disease progression. Conclusions: Epithelial measures are useful to identify KC eyes that are progressing; the parameters that measure the difference between min-max epithelium points were significantly different between stable and progressive groups, unlike this difference in pachymetry. Finally, this epithelial parameter seems to be independent of corneal thinning and K max. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal.

Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results: The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion: The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.

Objetivo: Desenvolver um modelo computacional - rede neural convolucional (RNC) - treinado com radiografias da linha de base do Estudo Longitudinal de Saúde do Adulto Musculoesquelético (ELSA-Brasil Musculoesquelético), para a classificação automática de osteoartrite dos joelhos. Materiais e Métodos: Trata-se de um estudo transversal abrangendo todos os exames da linha de base do ELSA-Brasil Musculoesquelético (5.660 radiografias dos joelhos em incidência posteroanterior). Os exames foram interpretados por médico radiologista com treinamento específico e calibração previamente publicada. Resultados: A RNC desenvolvida apresentou área sob a curva característica de operação do receptor de 0,866 (IC 95%: 0,842-0,882). O modelo pode ser calibrado para alcançar, não simultaneamente, valores máximos de 0,907 para acurácia, 0,938 para sensibilidade e 0,994 para especificidade. Conclusão: A RNC desenvolvida pode ser utilizada como ferramenta de triagem, reduzindo o número total de exames avaliados pelos radiologistas do estudo, e/ou como ferramenta de segunda leitura, contribuindo com a redução de possíveis erros de interpretação.

Ectasia Risk Model: A Novel Method Without Cut-off Point Based on Artificial Intelligence Improves Detection of Higher-Risk Eyes.

PURPOSE: To develop a new ectasia risk model through artificial intelligence (AI) and machine learning, enabling the creation of an integrated method without a cut-off point per risk factor, and subsequently better at differentiating patients at higher risk of ectasia with normal topography. METHODS: This comparative case-control study included 339 eyes with normal preoperative topography, with 65 eyes that developed ectasia after laser in situ keratomileusis (ectasia group) and 274 eyes that did not develop ectasia (control group). The AI model used known risk factors to engineer 14 additional ones, totaling 20 features. In this methodology, no variable is used in isolation because its cut-off point is never considered. All separation between cases and controls is made through the interaction detected by the machine learning model that gathers the variables considered relevant. The ability to correctly separate ectatic cases identified as high risk, ectatic cases wrongly classified as low risk, and controls were illustrated by the diagram t-distributed stochastic neighbor embedding (t-SNE). RESULTS: Only two original variables (percent tissue altered and corneal thickness) and two derived from the feature engineering process (derivative percent tissue altered and age weighted value) were selected by the final AI model (ie, best performing AI-based model to separate patients at higher risk). The t-SNE visualization demonstrated the greater ability to differentiate between patients considered at risk by the AI-based model, without a cut-off point, compared to all other methods used alone (P < .0001). CONCLUSIONS: This study describes a new AI-based model that integrates different risk factors without a cut-off point, increasing the number of cases correctly identified as at higher risk. [J Refract Surg. 2022;38(11):716-724.].

Caffeine intoxication: Behavioral and electrocorticographic patterns in Wistar rats.

Caffeine is a psychoactive substance used worldwide. The present study analyzes the seizure-related behavior and electrocorticographic (ECoG) patterns observed in rats following of a toxic dose of caffeine (150 mg/kg; intraperitoneal). Sixty-three rats were divided into three experiments: 1-Behavior's Description associated with caffeine-induced convulsion; 2- Comparison of the electrocorticographic patterns induced by caffeine and pentylenetetrazole, and 3- Assessment of the electrocorticographic response to antiepileptic drugs (diazepam, phenytoin, and phenobarbital). The behavioral analysis demonstrated tonic-clonic seizures with a loss of postural reflex and a latency of 365.8 s after the caffeine's administration. Caffeine-induced changes in the ECoG were consistent with the development of seizures with rapid evolution and burst potential consistent with the behavioral patterns observed during the caffeine-induced seizure. The ECoG of the brainwaves varied significantly between the seizures caused by caffeine and pentylenetetrazole. The predominant brain forces observed during the seizures were beta-band oscillations. The caffeine-induced seizures were resistant to attempted control with phenytoin and phenobarbital, but responded well to diazepam, which is consistent with a study of Pilocarpine, which showed that diazepam has anticonvulsant effects. These findings are important for the development of effective treatments for caffeine intoxication, in particular for individuals with a low seizure threshold.

A Combination of Curcuma longa and Diazepam Attenuates Seizures and Subsequent Hippocampal Neurodegeneration.

Epilepsy is one of the most common neurological disorders, which occurs due to the instability in the inhibitory and excitatory synaptic transmissions in the brain. However, many patients develop resistance to the available drugs, which results in cell degeneration caused due to inadequate control of the seizures. Curcumin, Curcuma longa, is known to be effective for the treatment of organic disorders and may prevent seizures, reduce oxidative stress, and decrease brain damage. Given this, the present study evaluated the antiepileptic effects of C. longa in comparison with both the diazepam and the combined application of these two substances, in terms of their effects on the brain activity and the potential histopathological changes in the hippocampus. This study used male Wistar rats (age: 10-12 weeks; weight: 260 ± 20 g), which were pretreated for 4 days with either saline, C. longa, diazepam, or C. longa + diazepam; and on the fifth day, pentylenetetrazol (PTZ) was administered to induce the seizure. In the C. longa group, a significant increase was observed in the latency of the onset of seizure-related behavior. Surprisingly, however, the combined treatment resulted in the best control of the seizure-related behavior, with the greatest latency of the onset of spasms and isolated clonic seizures. This group also obtained the best results in the electroencephalographic trace and seizure control, with a reduction in the frequency and amplitude of the spike-waves. In the saline group, PTZ significantly reduced the number of cells present in the CA1 and CA3 regions of the hippocampus, while the combined treatment obtained the best results in terms of the preservation of the neuron-like cells. These findings indicate that C. longa may contribute to the control of both seizures and the cell damage induced by PTZ, and that its association with diazepam may be a potentially effective option for the treatment of epilepsy in the future.

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil.

BACKGROUND: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease. OBJECTIVE: This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil. METHODS: We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations. RESULTS: We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window. CONCLUSION: Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.

A Novel Panel of Plasma Proteins Predicts Progression in Prodromal Alzheimer's Disease.

BACKGROUND: A cheap and minimum-invasive method for early identification of Alzheimer's disease (AD) pathogenesis is key to disease management and the success of emerging treatments targeting the prodromal phases of the disease. OBJECTIVE: To develop a machine learning-based blood panel to predict the progression from mild cognitive impairment (MCI) to dementia due to AD within a four-year time-to-conversion horizon. METHODS: We created over one billion models to predict the probability of conversion from MCI to dementia due to AD and chose the best-performing one. We used Alzheimer's Disease Neuroimaging Initiative (ADNI) data of 379 MCI individuals in the baseline visit, from which 176 converted to AD dementia. RESULTS: We developed a machine learning-based panel composed of 12 plasma proteins (ApoB, Calcitonin, C-peptide, CRP, IGFBP-2, Interleukin-3, Interleukin-8, PARC, Serotransferrin, THP, TLSP 1-309, and TN-C), and which yielded an AUC of 0.91, accuracy of 0.91, sensitivity of 0.84, and specificity of 0.98 for predicting the risk of MCI patients converting to dementia due to AD in a horizon of up to four years. CONCLUSION: The proposed machine learning model was able to accurately predict the risk of MCI patients converting to dementia due to AD in a horizon of up to four years, suggesting that this model could be used as a minimum-invasive tool for clinical decision support. Further studies are needed to better clarify the possible pathophysiological links with the reported proteins.

Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning.

Background: The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person's blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes. Methods: We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We proposed an ensemble procedure that combines machine learning models for different respiratory infections and analyzed the results in both the first and second waves of COVID-19 cases in Brazil. Results: We obtain a high-performance AUROC of 90 + % for validations in both scenarios. We show that models built solely of SARS-Cov-2 data are biased, performing poorly in the presence of infections due to other RNA respiratory viruses. Conclusions: We demonstrate the potential of a novel machine learning approach for COVID-19 diagnosis based on a CBC and show that aggregating information about other respiratory diseases was essential to guarantee robustness in the results. Given its versatile nature, low cost, and speed, we believe that our tool can be particularly useful in a variety of scenarios-both during the pandemic and after.

Correlation of hair cortisol and interleukin 6 with structural change in the active progression of keratoconus.

PURPOSE: To evaluate interleukin (IL) and hair cortisol concentrations (HCCs) in progressive keratoconus (KC) and compare them with KC-stable eyes and healthy control, and to determine the correlation of these inflammatory mediators and HCCs and their relationship with structural damage represented by increased corneal curvature. SETTING: University of Sao Paulo, Brazil. DESIGN: Prospective observational comparative study. METHODS: 133 eyes of 74 patients were included. The concentrations of tear cytokines: IL1B, IL6, IL8, IL10, IL12p70, and tumor necrosis factor α were obtained by capillary flow and measured using a flow cytometer. HCCs were determined from the most proximal hair segment as an index of cumulative secretion and measured by liquid chromatography mass spectrometry. RESULTS: 133 eyes of 74 patients. Only IL6 was increased in progressive KC tears compared with stable KC (6.59 ± 3.25 pg/mL vs 4.72 ± 1.91 pg/mL; P < .0001) with a positive correlation between IL6 and maximum keratometry (Kmax) (P < .0001). Progressive KC exhibited significantly higher HCC than stable KC (0.624 ± 0.160 ng/mg vs 0.368 ± 0.0647 ng/mg; P < .0001) and healthy controls (0.624 ± 0.160 ng/mg vs 0.351 ± 0.0896 ng/mg; P < .0001). There was a significant correlation between HCC and Kmax (P < .0001). CONCLUSIONS: KC eyes that are progressing have a higher concentration of IL6 and long-term cortisol than patients with stable forms of KC; second, there is a significant correlation between this increase in IL6 and cortisol with corneal structural damage. Finally, there is a meaningful relationship between this interleukin and the previous few months' cortisol levels.

Juventude: abrir os olhos para sonhar um outro mundo possível

Sabe, Sancho, todas essas tempestades que acontecem conosco são sinais de que em breve o tempo se acalmará e que coisas boas têm de acontecer; porque não é possível que o bem e o mal durem para sempre, e segue-se que, havendo o mal durado muito tempo, o bem deve estar por perto (Miguel de Cervantes em Dom Quixote). Os fnais de ano quase sempre remetem à reflexão e alguma espécie de sonho. Principalmente quando o mundo se encontra na perspectiva de controle da atual pandemia e de suas graves consequências. Ao pensar este editorial da revista Visa em Debate no fechamento de sua 4ª edição/2021, veio à mente a palavra Juventude. Procurei intuitivamente segui-la. Talvez seja uma boa ideia para nos instigar, e tentarei colocar aqui o que me inspirou e o porquê de necessitarmos desta palavra como companhia.

Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning.

Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long high-cost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97-0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95-0.98) using a miniaturized spectral range (896-1039 cm-1), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.

Cistos e Tumores Odontogênicos em uma população brasileira: análise retrospectiva de 12 anos à luz da 4ª Edição da Classificação dos Tumores de Cabeça e Pescoço da OMS / Odontogenic cysts and tumors in a brazilian population: a 12-year retrospective analysis in light of the 4th Edition of the WHO Head and Neck Tumor Classification

Introdução: Tendo em vista a importância do campo da patologia oral e maxilofacial, faz-se necessário compreender o impacto da mais recente Classificação dos Tumores de Cabeça e Pescoço da OMS na frequência e distribuição dos cistos e tumores odontogênicos. Este estudo teve como objetivo estabelecer a frequência de lesões odontogênicas ao longo de 12 anos, em um serviço de referência em Patologia Oral, no Nordeste brasileiro. Metodologia: Os casos diagnosticados como cisto (OC) ou tumor (OT) odontogênico de 1999 a 2010 foram revisados e reclassificados de acordo com a atual Classificação da OMS de Tumores de Cabeça e Pescoço. Foram analisados dados referentes ao diagnóstico histopatológico, à localização da lesão, à idade, ao sexo e à etnia. A análise dos dados bivariados foi realizada, calculando-se as razões de prevalência, o teste do qui-quadrado e o teste exato de Fisher. Resultados: Entre 3.034 espécimes, 409 foram OC e 199 foram OT. Os Oc mais frequentes foram o cisto radicular (n = 129) e ceratocisto odontogênico (n = 99). Entre os OT, os mais frequentes foram ameloblastoma (n = 80) e odontoma (n = 47). Conclusões: Houve uma redução considerável na frequência relativa de OT após a reclassificação de lesões... (AU)

Introduction: Due to the importance of oral and maxillofacial pathology, it is necessary to understand the impact of the latest WHO Head and Neck Tumor Classification on the frequency and distribution of odontogenic cysts and tumors. This study aimed to establish the frequency of odontogenic injuries over 12 years in a reference service of Oral Pathology in the Northeast of Brazil. Methodology: All cases which received a diagnosis of odontogenic cyst (OC) or tumour (OT) from 1999 to 2010 were reviewed and reclassified according to the lastest WHO Classification of Head and Neck Tumors. Data regarding the histopathological diagnosis, location of the lesion, age, gender and Ethnicity were analyzed. The bivariate data analysis was performed by calculating the prevalence ratios, as well as the chi-square test and Fisher's exact test. Results: Among 3,034 specimens, 409 were OC and 199 were OT. The most frequent OC were the radicular cyst (n = 129) and odontogenic keratocyst (n = 99). Among the OT, the most frequents were ameloblastoma (n = 80) and odontoma (n = 47). Conclusions: There was a considerable reduction in relative frequency of OTs after the reclassification of important pathological entities... (AU)

Approach and Treatment of Giant Luminal Unicystic Ameloblastoma.

Unicystic ameloblastoma is an odontogenic tumor that affects mainly young patients and usually involves the posterior region of the mandible. In this article, we report on the case a 12-year-old girl presenting with an 8-month history of facial swelling in her lower right quadrant. Radiographic examination revealed a unilocular radiolucent lesion extending from the body of the mandible through to the angle and ascending ramus. An incisional biopsy was performed, and a diagnosis of luminal unicystic ameloblastoma was made based on clinicopathological features. The lesion was treated in two stages, namely, an initial conservative approach via decompression and subsequent excision. The patient has been followed up for 6 months without clinical and radiographic evidence of recurrence. In conclusion, conservative timely intervention combined with a conservative surgical approach has proven efficacious in the treatment of ameloblastoma in this young patient.

Molecular Epidemiology of Agents of Human Chromoblastomycosis in Brazil with the Description of Two Novel Species.

The human mutilating disease chromoblastomycosis is caused by melanized members of the order Chaetothyriales. To assess population diversity among 123 clinical strains of agents of the disease in Brazil we applied sequencing of the rDNA internal transcribed spacer region, and partial cell division cycle and ß-tubulin genes. Strains studied were limited to three clusters divided over the single family Herpotrichiellaceae known to comprise agents of the disease. A Fonsecaea cluster contained the most important agents, among which F. pedrosoi was prevalent with 80% of the total set of strains, followed by 13% for F. monophora, 3% for F. nubica, and a single isolate of F. pugnacius. Additional agents, among which two novel species, were located among members of the genus Rhinocladiella and Cyphellophora, with frequencies of 3% and 1%, respectively.

Caracterização da qualidade de vida de pessoas com hanseníase em tratamento ambulatorial / Characteristics of people of quality of life with leprosy in outpatient treatment / Características de la gente de la calidad de vida con la lepra entratamiento ambulatorio

Objective: To characterize the quality of life of people with leprosy in outpatient treatment. Method: Exploratory-descriptive quantitative study with 59 subjects. To obtain the socioeconomic and clinical data of individuals used the questionnaire, and for the quality of life, the Short Medical Outcomes Study 36 - Item Short-Form Health Survey SF-36. Data analysis was adopted IBM SPSS statistical software. Results: 78% multibacillary; males 54.3%; mean age 45.9 years; economically active 67%. The SF-36 shows average scores calculated values up to score 50, but the physical and emotional aspects (score <50) Age was correlated to the areas of functional capacity and physical aspects, while sex was associated with mental health. Conclusion: The investigated population has adequate quality of life, though, it is observed that this is affected by the disease.

Objetivo: Caracterizar a qualidade de vida de pessoas com hanseníase em tratamento ambulatorial. Método: Estudo quantitativo exploratório descritivo com 59 sujeitos. Para obtenção dos dados socioeconômicos e clínicos dos indivíduos utilizou-se formulário próprio e para a qualidade de vida, o questionário Short Medical Outcomes Study 36 – Item Short-Form Health Survey, o SF-36. Para análise dos dados adotou-se o software estatístico IBM SPSS. Resultados: 78% multibacilares; sexo masculino 54,3%; idade média de 45,9 anos; economicamente ativos 67%. O SF-36 aponta escores médios calculados em valores acima ao escore 50, exceto os aspectos físicos e emocionais (escore < 50) A idade esteve correlacionada aos domínios de capacidade funcional e aspectos físicos, enquanto o sexo esteve associado à saúde mental. Conclusão: A população investigada apresenta qualidade de vida adequada, embora, observa-se que esta se encontra afetada pela doença.

Objetivo: Caracterizar la calidad de vida de las personas con lepra en tratamiento ambulatorio. Método: Estudio cuantitativo exploratorio descriptivo con 59 temas. Para obtener los datos socioeconómicos y clínicos de los individuos utilizaron el cuestionario, y para la calidad de vida, el Short Medical Outcomes Study 36 - Artículo Short-Form Health Survey SF-36. Se aprobó el análisis de datos de IBM SPSS software estadístico. Resultados: 78% multibacilar; varones 54.3%; edad media de 45,9 años; económicamente activa de 67%. El SF-36 muestra las puntuaciones medias calculadas valores de hasta anotar 50, pero los aspectos físicos y emocionales (puntuación <50) La edad se correlacionó con las áreas de la capacidad funcional y los aspectos físicos, mientras que el sexo se asocia con la salud mental. Conclusión: La población investigada ha adecuada calidad de vida, sin embargo, se observa que esta se ve afectada por la enfermedad.