Blockchain-enabled healthcare monitoring system for early Monkeypox detection.

The recent emergence of monkeypox poses a life-threatening challenge to humans and has become one of the global health concerns after COVID-19. Currently, machine learning-based smart healthcare monitoring systems have demonstrated significant potential in image-based diagnosis including brain tumor identification and lung cancer diagnosis. In a similar fashion, the applications of machine learning can be utilized for the early identification of monkeypox cases. However, sharing critical health information with various actors such as patients, doctors, and other healthcare professionals in a secure manner remains a research challenge. Motivated by this fact, our paper presents a blockchain-enabled conceptual framework for the early detection and classification of monkeypox using transfer learning. The proposed framework is experimentally demonstrated in Python 3.9 using a monkeypox dataset of 1905 images obtained from the GitHub repository. To validate the effectiveness of the proposed model, various performance estimators, namely accuracy, recall, precision, and F1-score, are employed. The performance of different transfer learning models, namely Xception, VGG19, and VGG16, is compared against the presented methodology. Based on the comparison, it is evident that the proposed methodology effectively detects and classifies the monkeypox disease with a classification accuracy of 98.80%. In future, multiple skin diseases such as measles and chickenpox can be diagnosed using the proposed model on the skin lesion datasets.

Blockchain-based rumor detection approach for COVID-19.

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts' veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive.

Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications.

The recent outbreak of novel coronavirus disease (COVID-19) has resulted in healthcare crises across the globe. Moreover, the persistent and prolonged complications of post-COVID-19 or long COVID are also putting extreme pressure on hospital authorities due to the constrained healthcare resources. Out of many long-lasting post-COVID-19 complications, heart disease has been realized as the most common among COVID-19 survivors. The motivation behind this research is the limited availability of the post-COVID-19 dataset. In the current research, data related to post-COVID complications are collected by personally contacting the previously infected COVID-19 patients. The dataset is preprocessed to deal with missing values followed by oversampling to generate numerous instances, and model training. A binary classifier based on a stacking ensemble is modeled with deep neural networks for the prediction of heart diseases, post-COVID-19 infection. The proposed model is validated against other baseline techniques, such as decision trees, random forest, support vector machines, and artificial neural networks. Results show that the proposed technique outperforms other baseline techniques and achieves the highest accuracy of 93.23%. Moreover, the results of specificity (95.74%), precision (95.24%), and recall (92.05%) also prove the utility of the adopted approach in comparison to other techniques for the prediction of heart diseases.

Normative values of cardiac chamber dimensions and global longitudinal strain in Indians: the Indian Normative Data of Echocardiography Analyzed (INDEA) study.

Ethnic-specific normal reference ranges for various echocardiographic measurements are essential for accurate diagnostic interpretation and clinical decision-making. Unfortunately, such normative data for Indians is lacking. A total of 880 healthy volunteers (mean age 39.7 ± 12.3 years, 63.8% men) from six centers across different regions of India were enrolled in this study. Comprehensive transthoracic echocardiographic study was performed in all subjects, in accordance with the existing guideline recommendations. Cardiac chamber dimensions [Left ventricular (LV) end-diastolic diameter and volume; right ventricular (RV) basal diameter, left atrial volume] were obtained and indexed to body surface area. LV ejection fraction, LV global longitudinal strain (LVGLS) and measures of RV systolic function were also obtained. The subjects were divided into 3 age groups (35 years or less, 36-55 years and 56 years or above) for analysis. Age- and gender-specific reference values for various clinically relevant echocardiographic parameters were derived. Compared with women, men had larger cardiac chamber dimensions and volumes, but not when indexed. In contrast, the women had higher LV systolic function, but right ventricular systolic function was not different. The indexed LV volumes in our study were much smaller than those recommended in the American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) 2015 chamber quantification guidelines but were similar to those reported in the Indian patients included in the recent World Alliance Societies of Echocardiography (WASE) Normal Values Study. LVGLS was also comparable with the WASE data. INDEA study is the first, multi-centric study to provide normal echocardiographic references values for Indian adults. Our findings underscore the need to follow India-specific reference values, instead of those recommended by the ASE/EACVI, which are largely applicable to the western populations.

Robinow Syndrome and Brachydactyly: An Interplay of High-Throughput Sequencing and Deep Phenotyping in a Kindred.

We report a family with a spectrum of short stature, craniofacial dysmorphism, and digital anomalies in a father and 2 daughters, with the youngest (proband) displaying a severe phenotype. Clinically, autosomal dominant Robinow syndrome (ADRS) was diagnosed. Whole-exome sequencing identified a heterozygous pathogenic BMP2 variant in the father and his daughters. The phenotype of short stature, facial dysmorphism, and skeletal anomalies with or without cardiac anomalies related to BMP2 haploinsufficiency has some facial and digital resemblance to ADRS. Although this variant segregated in the affected members, it failed to explain the severe phenotype of the proband. A reanalysis of the girl's raw data confirmed 2 disorders: a de novo likely pathogenic DVL1 variant implicated in ADRS and the familial BMP2 variant. A close interplay of high-throughput sequencing and deep phenotyping unraveled the complexities of the blended phenotype in the proband.

A phase II trial of arsenic trioxide for relapsed and refractory acute lymphoblastic leukemia.

We designed a phase II trial of arsenic trioxide (AT) for the treatment of relapsed and refractory acute lymphoblastic leukemia (ALL). The dose administered was 0.25 mg/kg/day intravenously for 5-7 days per week for up to 60 days. Of 11 patients eligible, eight had B-cell and three T-cell ALL and two were Philadelphia chromosome-positive. The median duration of therapy was 21 days (range 7-28). One patient died of an infection. There were no responses. Ten patients have died. The median survival was 3.2 months (range 1.2-4.1). We conclude that AT is not active in the treatment of ALL.

Precision of dimensional measurements of mandible in different positions on orthopantomogram.

OBJECTIVE: To evaluate the precision of dimensional measurements of the mandible in two different positions on orthopantomogram (OPG) (one with mandibular plane parallel to the floor and the other with Frankfort horizontal plane parallel to the floor) and determine the dimensional reliability. MATERIALS AND METHODS: Anatomical landmarks were used to denote points for measurements on mandible as well as OPG and respective measurements made. Magnification was hence calculated and compared with magnification factor (1.2) listed by the manufacturer. RESULTS: Vertical measurements and anterior horizontal and oblique measurements showed minimal magnification. Posterior horizontal and oblique measurements showed increased magnification. The difference in measurements in the two positions and on comparison with the one given by the manufacturer was statistically significant for posterior horizontal measurements but not for posterior oblique. Horizontal measurements crossing the midline were highly magnified and the difference was statistically significant for the two positions and on comparison to the one given by manufacturer. CONCLUSION: Magnification factor given by the manufacturer is not uniform in all locations and varies with changes in positioning and hence should not be relied upon when accurate measurements are to be made.

Renal tubular dysgenesis in an hydropic fetus with trisomy 21: a case report with literature review.

Renal tubular dysgenesis (RTD) is a rare form of noncystic renal disease characterized by paucity or absence of proximal renal tubules. Always lethal in the perinatal period, it has been associated with Potter sequence and with other congenital malformations. An autosomal recessive inheritance has been suggested. We present a case of renal tubular dysgenesis associated with fetal hydrops and trisomy 21, with a review of relevant literature.