Tratamiento de la hiperhidrosis plantar con toxina butolínica tipo A

El problema de hiperhidrosis afecta al 0,5% de la población, y puede causar considerable estrés emocional, dificultando en ocasiones la vida personal, laboral y social del paciente, llevándole, por ejemplo, a evitar un acto como dar la mano o quitarse los zapatos en público. Por otra parte, el excesivo sudor puede ocasionar maceración cutánea, acrocianosis, queratoderma e incluso deshidratación. La forma más frecuente de hiperhidrosis es la idiopática y en el 60% de los casos afecta a palmas y plantas de los pies. En este artículo presentamos la aplicación del tratamiento con la toxina butolínica tipo A

Efficacy of the treatment of plantar warts using 1064 nm laser and cooling

Cutaneous plantar warts may be treated using several optional methods, with the use of laser surgery having increased in the last few years. This work examined the efficacy of laser treatment combined with simple cooling to reduce pain. The cure rate was approximately 84%. There were no significant differences in the efficacy of treatment for different viral genotypes. The laser parameters were 500 msec pulses, 30 W of power, and a fluence of 212 J/cm2 delivered in up to four sessions. Successful treatment was achieved after an average of 3.6 sessions

Estudio estadístico en cirugía ungueal

Presentamos un estudio estadístico sobre los resultados de las técnicas de cirugía ungueal realizadas en el Servicio de Cirugía de la Clínica Podológica de la Universidad de Barcelona en los últimos años, describiendo el método empleado para su realización y las conclusiones obtenidas

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n=1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d=148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation SpO2, quotients SpO2/RR and arterial SatO2/FiO2 –, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives

Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity

Objective: To study the suitability of cost-sensitive ordinal artificial intelligence-machine learning (AI-ML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. Materials & methods: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a ‘standard’ (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. Results: The study enrolled $n$=1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. $d$=131 variables were collected, becoming $d′$=148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of $d′$ features), c) ‘Ordered Partitions’ ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the ‘standard’ AI-ML baseline. Discussion & conclusion: We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature

Tratamiento resolutivo de un heloma en fondo de saco

Los helomas y durezas resultan de la hiperquerato­sis, que está causada por un incremento en la acti­vidad de los queratinocitos asociada a la estimula­ción de la epidermis de una presión crónica o fric­ción de la piel. Ejemplo de ello incluye irritación debido al uso de un calzado inadecuado o una pre­sión anómala causada por una deformidad en el pie. La hiperqueratosis es una respuesta protectora de la piel normal, que se convierte en patológica cuando la dureza o callo se desarrolla de tal manera que produce una serie de sintomatología

Effect of ABCB1 and ABCC3 Polymorphisms on Osteosarcoma Survival after Chemotherapy: A Pharmacogenetic Study

Standard treatment for osteosarcoma patients consists of a combination of cisplatin, adriamycin, and methotrexate before surgical resection of the primary tumour, followed by postoperative chemotherapy including vincristine and cyclophosphamide. Unfortunately, many patients still relapse or suffer adverse events. We examined whether common germline polymorphisms in chemotherapeutic transporter and metabolic pathway genes of the drugs used in standard osteosarcoma treatment may predict treatment response. METHODOLOGY/PRINCIPAL FINDINGS: In this study we screened 102 osteosarcoma patients for 346 Single Nucleotide Polymorphisms (SNPs) and 2 Copy Number Variants (CNVs) in 24 genes involved in the metabolism or transport of cisplatin, adriamycin, methotrexate, vincristine, and cyclophosphamide. We studied the association of the genotypes with tumour response and overall survival. We found that four SNPs in two ATP-binding cassette genes were significantly associated with overall survival: rs4148416 in ABCC3 (per-allele HR = 8.14, 95%CI = 2.73-20.2, p-value = 5.1x10(-)(5)), and three SNPs in ABCB1, rs4148737 (per-allele HR = 3.66, 95%CI = 1.85-6.11, p-value = 6.9x10(-)(5)), rs1128503 and rs10276036 (r(2) = 1, per-allele HR = 0.24, 95%CI = 0.11-0.47 p-value = 7.9x10(-)(5)). Associations with these SNPs remained statistically significant after correction for multiple testing (all corrected p-values [permutation test] </= 0.03). CONCLUSIONS: Our findings suggest that these polymorphisms may affect osteosarcoma treatment efficacy. If these associations are independently validated, these variants could be used as genetic predictors of clinical outcome in the treatment of osteosarcoma, helping in the design of individualized therapy

Impact of outdoor air pollution on severity and mortality in COVID-19 pneumonia

The relationship between exposure to air pollution and the severity of coronavirus disease 2019 (COVID-19) pneumonia and other outcomes is poorly understood. Beyond age and comorbidity, risk factors for adverse outcomes including death have been poorly studied. The main objective of our study was to examine the relationship between exposure to outdoor air pollution and the risk of death in patients with COVID-19 pneumonia using individual-level data. The secondary objective was to investigate the impact of air pollutants on gas exchange and systemic inflammation in this disease. This cohort study included 1548 patients hospitalised for COVID-19 pneumonia between February and May 2020 in one of four hospitals. Local agencies supplied daily data on environmental air pollutants ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, $NO$ and $NO_X$) and meteorological conditions (temperature and humidity) in the year before hospital admission (from January 2019 to December 2019). Daily exposure to pollution and meteorological conditions by individual postcode of residence was estimated using geospatial Bayesian generalised additive models. The influence of air pollution on pneumonia severity was studied using generalised additive models which included: age, sex, Charlson comorbidity index, hospital, average income, air temperature and humidity, and exposure to each pollutant. Additionally, generalised additive models were generated for exploring the effect of air pollution on C-reactive protein (CRP) level and Sp$O_2$/Fi$O_2$ at admission. According to our results, both risk of COVID-19 death and CRP level increased significantly with median exposure to $PM_{10}$, $NO_2$, $NO$ and $NO_X$, while higher exposure to $NO_2$, $NO$ and $NO_X$ was associated with lower Sp$O_2$/Fi$O_2$ ratios. In conclusion, after controlling for socioeconomic, demographic and health-related variables, we found evidence of a significant positive relationship between air pollution and mortality in patients hospitalised for COVID-19 pneumonia. Additionally, inflammation (CRP) and gas exchange (Sp$O_2$/Fi$O_2$) in these patients were significantly related to exposure to air pollution