Blockchain based transparent and reliable framework for wheat crop supply chain.

The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers' trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system.

Analyzing patients satisfaction level for medical services using twitter data.

Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer's satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.

Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics.

A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements.

Herbal medicine and nutritional supplements are suggested to treat premenstrual somatic and psycho-behavioural symptoms in clinical guidelines; nonetheless, this is at present based on poor-quality trial evidence. Hence, we aimed to design a systematic review and meta-analysis for their effectiveness in alleviating premenstrual symptoms. The published randomized controlled trials (RCTs) were extracted from Google scholar, PubMed, Scopus and PROSPERO databases. The risk of bias in randomized trials was assessed by Cochrane risk-of-bias tool. The main outcome parameters were analysed separately based on the Premenstrual Symptom Screening Tool and PMTS and DRSP scores. Secondary parameters of somatic, psychological, and behavioural subscale symptoms of PSST were also analysed. Data synthesis was performed assuming a random-effects model, and standardized mean difference (SMDs) was analysed using SPSS version 28.0.0 (IBM, Armonk, NY, USA). A total of 754 articles were screened, and 15 RCTs were included (n = 1211 patients). Primary results for participants randomized to an intervention reported reduced PSST (n = 9), PMTS (n = 2), and DSR (n = 4) scores with (SMD = -1.44; 95% CI: -1.72 to -1.17), (SMD = -1.69; 95% CI: -3.80 to 0.42) and (SMD = 2.86; 95% CI: 1.02 to 4.69) verses comparator with substantial heterogeneity. Physical (SMD = -1.61; 95% CI = -2.56 to -0.66), behavioural (SMD = -0.60; 95% CI = -1.55 to0.35) and mood (SMD = 0.57; 95% CI = -0.96 to 2.11) subscale symptom groupings of PSST displayed similar findings. Fifty-three studies (n = 8) were considered at low risk of bias with high quality. Mild adverse events were reported by four RCTs. Based on the existing evidence, herbal medicine and nutritional supplements may be effective and safe for PMS.

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.

White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.

Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes.

In vivo expansion of a CD9+ decidual-like NK cell subset following autologous hematopoietic stem cell transplantation.

Autologous hematopoietic stem cell transplantation (autoHSCT) is a treatment option for hematological disorders and pediatric solid tumors. After an autoHSCT, natural killer (NK) cells are the first lymphocyte subset returning to normal levels. To uncover global changes during NK cell reconstitution after autoHSCT, we performed RNA-sequencing on NK cells before and after autoHSCT. Results showed profound changes in the gene expression profile of NK cells immediately after autoHSCT. Several biological processes including cell cycle, DNA replication and the mevalonate pathway were enriched. Significantly, we observed that following autoHSCT, NK cells acquired a decidual-like gene expression profile, including the expression of CD9. By using multiparametric flow cytometry, we confirmed the expansion of NK cells expressing CD9 immediately after autoHSCT, which exhibited higher granzyme B and perforin expression levels than CD9- NK cells. These results provide insights into the physiopathology of NK cells during their reconstitution after autoHSCT.

A CAD System for Alzheimer's Disease Classification Using Neuroimaging MRI 2D Slices.

Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer's disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN's receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN's capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.

Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto's thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach.

A Review of Image Processing Techniques for Deepfakes.

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.

Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung's tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen's kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively.

[Observational study of the adherence to the correct use of face masks by the general population in open public places during two periods of the COVID-19 pandemic in four Spanish cities.] / Estudio observacional de la adherencia al correcto uso de mascarillas por la población general en lugares públicos abiertos durante dos periodos de la pandemia por COVID-19 en 4 ciudades españolas.

OBJECTIVE: One of the steps adopted to mitigate the pandemic due to SARS-CoV-2 is the use of face masks by the general population. For a face mask to be effective it should cover the nose and the mouth. We wanted to measure the correct use of the face mask by the general population in open public spaces through direct observation. METHODS: We conducted an observational study of the proper use of face masks among the general population in open public places in Bilbao, Santander, Oviedo and Zaragoza from 16th to 26th July, 2020 and from 23rd January to 2nd March, 2021. Sampling for convenience; compliance of the proper use of a mask was evaluated when adults completely covered their mouth and nose. The type of mask and its improper use was registered using a standardized form. The results were obtained using frequency distribution, Pearson's chi-squared test and multivariate logistic regression analysis. RESULTS: A total of 5,464 observations were documented. The overall compliance was 89.5%. We observed that the compliance in 2021 (94.7%) was 10.9 percentage points higher than in 2020 (83.8%) (p<0.001). The main cause of non-compliance was the incorrect placement of face masks (64%); 36% were without masks. The non-reusable face masks were most commonly worn (54.1%). We observed a significant increase in use of high-efficiency face masks in 2021 (27.1%) versus 2020 (13.7%). CONCLUSIONS: In all the cities where the study was conducted we observed an increase in compliance of the proper use of face masks as well as an increased usage of high-efficiency masks. The main cause of non-compliance was incorrect placement.

OBJETIVO: Dentro de las medidas adoptadas para mitigar la pandemia por SARS-CoV-2 se encuentra el uso de mascarillas en la población general. Para que esta medida sea efectiva las mascarillas deben cubrir la nariz y la boca. Nos propusimos conocer su uso correcto por la población general en espacios públicos abiertos mediante observación directa. METODOS: Estudio prospectivo observacional del correcto uso de mascarillas en la población general en espacios abiertos en Bilbao, Santander, Oviedo y Zaragoza, del 16 al 26/07/2020 y del 23/01 al 02/03/2021. Se realizó un muestreo por conveniencia evaluando el cumplimiento del uso de mascarilla cuando los adultos la llevaban cubriendo completamente nariz y boca. Se registró el tipo e inadecuación de su uso mediante formulario estandarizado. Se realizó distribución de frecuencias, comparaciones con χ2 de Pearson y regresión logística multivariable. RESULTADOS: Se realizaron 5.464 observaciones. El cumplimiento global fue del 89,5%; 10,9 puntos mayor en 2021 (94,7%) que en 2020 (83,8%) (p<0,001). La principal causa de incumplimiento fue la colocación incorrecta (64%) frente no llevar nada (36%). Respecto al tipo de mascarillas, las más utilizadas fueron las no reutilizables (54,1%), aumentando en 2021 el uso de las de alta eficacia (13,7% versus 27,6%) de forma significativa. CONCLUSIONES: En todas las ciudades estudiadas se observa un aumento del uso correcto de la mascarilla desde que se hizo obligatorio en espacios públicos, así como aumento de las mascarillas de alta eficacia. La principal causa de incumplimiento es llevar la mascarilla mal colocada.

Estudio observacional de la adherencia al correcto uso de mascarillas por la población general en lugares públicos abiertos durante dos periodos de la pandemia por COVID-19 en 4 ciudades españolas / Evaluation of the effectiveness of a school-based alcohol and cannabis prevention programme among adolescents

Fundamentos: Dentro de las medidas adoptadas para mitigar la pandemia por SARS-CoV-2 se encuentra el uso de mascarillas en la población general. Para que esta medida sea efectiva las mascarillas deben cubrir la nariz y la boca. Nos propusimos conocer su uso correcto por la población general en espacios públicos abiertos mediante observación directa. Métodos: Estudio prospectivo observacional del correcto uso de mascarillas en la población general en espacios abiertos en Bilbao, Santander, Oviedo y Zaragoza, del 16 al 26/07/2020 y del 23/01 al 02/03/2021. Se realizó un muestreo por conveniencia evaluando el cumplimiento del uso de mascarilla cuando los adultos la llevaban cubriendo completamente nariz y boca. Se registró el tipo e inadecuación de su uso mediante formulario estandarizado. Se realizó distribución de frecuencias, comparacionescon χ2 de Pearson y regresión logística multivariable. Resultados: Se realizaron 5.464 observaciones. El cumplimiento global fue del 89,5%; 10,9 puntos mayor en 2021 (94,7%) que en 2020 (83,8%) (p<0,001). La principal causa de incumplimiento fue la colocación incorrecta (64%) frente no llevar nada (36%). Respecto al tipo de mascarillas, las más utilizadas fueron las no reutilizables (54,1%), aumentando en 2021 el uso de las de alta eficacia (13,7% versus 27,6%) de forma significativa. Conclusiones: En todas las ciudades estudiadas se observa un aumento del uso correcto de la mascarilla desde que se hizo obligatorio en espacios públicos, así como aumento de las mascarillas de alta eficacia. La principal causa de incumplimiento es llevar la mascarilla mal colocada.(AU)

Background: One of the steps adopted to mitigate the pandemic due to SARS-CoV-2 is the use of face masks by the general population. For a face mask to be effective it should cover the nose and the mouth. We wanted to measure the correct use of the face mask by the general population in open public spaces through direct observation. Methods: We conducted an observational study of the proper use of face masks among the general population in open public places in Bilbao, Santander, Oviedo and Zaragoza from 16th to 26th July, 2020 and from 23rd January to 2nd March, 2021. Sampling for convenience; compliance of the proper use of a mask was evaluated when adults completely covered their mouth and nose. The type of mask and its improper use was registered using a standardized form. The results were obtained using frequency distribution, Pearson’s chisquared testand multivariate logistic regression analysis. Results: A total of 5,464 observations were documented. The overall compliance was 89.5%. We observed that the compliance in 2021 (94.7%) was 10.9 percentage points higher than in 2020 (83.8%) (p<0.001). The main cause of noncompliance was the incorrect placement of face masks (64%); 36% were without masks. The nonreusable face masks were most commonly worn (54.1%). We observed a significant increase in use of highefficiency face masks in 2021 (27.1%) versus 2020 (13.7%). Conclusions: In all the cities where the study was conducted we observed an increase in compliance of the proper use of face masks as well as an increased usage ofhighefficiency masks. The main cause of non-compliance was incorrect placement.(AU)

NK Cell Reconstitution After Autologous Hematopoietic Stem Cell Transplantation: Association Between NK Cell Maturation Stage and Outcome in Multiple Myeloma.

Autologous hematopoietic stem cell transplantation (autoHSCT) is a standard of care for transplant-eligible patients with multiple myeloma (MM). Among factors that influence outcome after autoHSCT, it has been suggested that the number of natural killer (NK) cells plays an important role. However, the impact that different NK cell subsets and their phenotype could have in disease progression after autoHSCT are less clear. For this reason, we have phenotypically and functionally characterized NK cells during immune system reconstitution after autoHSCT in 54 MM patients. Shortly after leukocyte recovery, an extensive redistribution of NK cell subsets occurs in these patients. In addition, NK cells undergo a profound phenotypic change characterized, among others, by their increased proliferative capacity and immature phenotype. Importantly, MM patients who showed lower frequencies of the mature highly differentiated NKG2A-CD57+ NK cell subset at +30 and +100 days after autoHSCT experienced superior progression-free survival and had a longer time to the next treatment than those with higher frequencies. Our results provide significant insights into NK cell reconstitution after autoHSCT and suggest that the degree of NK cell maturation after autoHSCT affects the clinical outcome of MM patients treated with this therapeutic strategy.

Dependencia al dispositivo móvil e impulsividad en estudiantes universitarios de Riobamba-Ecuador / Mobile device dependence and impulsivity in university students from Riobamba-Ecuador

El uso excesivo de la telefonía celular crea situaciones problemáticas, resaltando la dependencia. Se realizó un estudio con el objetivo de determinar la relación entre los niveles de dependencia al dispositivo móvil y los tipos de impulsividad en universitarios; con enfoque cuantitativo, de tipo no experimental correlacional. La población de estudio estuvo conformada por 2533 estudiantes de 18 a 26 años de la Facultad de Ciencias de la Salud de la Universidad Nacional de Chimborazo, matriculados en el periodo académico agosto 2019 ­ marzo 2020. La recolección de los datos se hizo a través del Test de Dependencia al Móvil y la Escala de Impulsividad (BIS-11). La media de la edad fue de 21,3 años. El 66,5% de la muestra mostró un nivel modera-do de dependencia del dispositivo móvil. El 85,03% tenía impulsividad no planificada, con un puntaje de nivel medio de impulsividad en cada subescala y globalmente. Entre ambas variables fundamentales de estudio se estableció una correlación positiva, maderada y estadísticamente significativa

Excessive use of smartphones creates problematic situations, highlighting dependency. This study was carried out to determine the relationship between the levels of dependence on the mobile device and the types of impulsivity in university students. This research included a quantitative approach, and a non-experimental correlational type. The study population consisted of 2533 students aged 18 to 26 from the Faculty of Health Sciences of the National University of Chimborazo, enrolled in the academic period August 2019 - March 2020. Data collection was done through the Mobile Dependence Test and Impulsivity Scale (BIS-11). The mean age was 21.3 years. 66.5% of the sample showed a moderate level of dependence on the mobile device. 85.03% had unplanned impulsivity, with a score of medium level of impulsivity in each subscale and globally. A positive, lumpy, and statistically significant correlation was established between both fundamental study variables

Acquired thrombotic thrombocytopenic Purpura diagnosed during first trimester of pregnancy with excellent outcome after plasma exchange and rituximab, a case report.

Acquired thrombotic thrombocytopenic purpura is a life-threatening condition that rarely presents during pregnancy. Early diagnosis and treatment with plasma exchange is needed to achieve a good pregnancy outcome.

Use of bortezomib in the management of chronic graft-versus-host disease among multiple myeloma patients relapsing after allogeneic transplantation.

We report on the use of bortezomib for the management of chronic graft versus host disease (cGVHD) among 8 multiple myeloma (MM) patients who relapsed after reduced-intensity conditioning (RIC) allogeneic transplantation. Five patients (62%) responded to bortezomib demonstrating anti-myeloma effect. Four patients had active cGVHD, including 3 patients with severe punctate keratopathy, at the time of bortezomib administration. All showed an improvement in their condition. This is the first report showing that bortezomib may be useful in the management of cGVHD and related ocular involvement.