Fake news stance detection using selective features and FakeNET

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.

IoT Based Smart Monitoring of Patients' with Acute Heart Failure

The prediction of heart failure survivors is a challenging task and helps medical professionals to make the right decisions about patients. Expertise and experience of medical professionals are required to care for heart failure patients. Machine Learning models can help with understanding symptoms of cardiac disease. However, manual feature engineering is challenging and requires expertise to select the appropriate technique. This study proposes a smart healthcare framework using the Internet-of-Things (IoT) and cloud technologies that improve heart failure patients’ survival prediction without considering manual feature engineering. The smart IoT-based framework monitors patients on the basis of real-time data and provides timely, effective, and quality healthcare services to heart failure patients. The proposed model also investigates deep learning models in classifying heart failure patients as alive or deceased. The framework employs IoT-based sensors to obtain signals and send them to the cloud web server for processing. These signals are further processed by deep learning models to determine the state of patients. Patients’ health records and processing results are shared with a medical professional who will provide emergency help if required. The dataset used in this study contains 13 features and was attained from the UCI repository known as Heart Failure Clinical Records. The experimental results revealed that the CNN model is superior to other deep learning and machine learning models with a 0.9289 accuracy value.

[Update on collaborative autopsy-based research in German pathology, neuropathology, and forensic medicine]

BACKGROUND

Autopsies are a valuable tool for understanding disease, including COVID-19.

MATERIALS AND METHODS

The German Registry of COVID-19 Autopsies (DeRegCOVID), established in April 2020, serves as the electronic backbone of the National Autopsy Network (NATON), launched in early 2022 following DEFEAT PANDEMIcs.

RESULTS

The NATON consortium's interconnected, collaborative autopsy research is enabled by an unprecedented collaboration of 138 individuals at more than 35 German university and non-university autopsy centers through which pathology, neuropathology, and forensic medicine autopsy data including data on biomaterials are collected in DeRegCOVID and tissue-based research and methods development are conducted. More than 145 publications have now emerged from participating autopsy centers, highlighting various basic science and clinical aspects of COVID-19, such as thromboembolic events, organ tropism, SARS-CoV‑2 detection methods, and infectivity of SARS-CoV-2 at autopsy.

CONCLUSIONS

Participating centers have demonstrated the high value of autopsy and autopsy-derived data and biomaterials to modern medicine. The planned long-term continuation and further development of the registry and network, as well as the open and participatory design, will allow the involvement of all interested partners.

Home automation using general purpose household electric appliances with Raspberry Pi and commercial smartphone

This study presents the design and implementation of a home automation system that focuses on the use of ordinary electrical appliances for remote control using Raspberry Pi and relay circuits and does not use expensive IP-based devices. Common Lights, Heating, Ventilation, and Air Conditioning (HVAC), fans, and other electronic devices are among the appliances that can be used in this system. A smartphone app is designed that helps the user to design the smart home to his actual home via easy and interactive drag & drop option. The system provides control over the appliances via both the local network and remote access. Data logging over the Microsoft Azure cloud database ensures system recovery in case of gateway failure and data record for lateral use. Periodical notifications also help the user to optimize the usage of home appliances. Moreover, the user can set his preferences and the appliances are auto turned off and on to meet user-specific requirements. Raspberry Pi acting as the server maintains the database of each appliance. HTTP web interface and apache server are used for communication between the android app and raspberry pi. With a 5v relay circuit and micro-processor Raspberry Pi, the proposed system is low-cost, energy-efficient, easy to operate, and affordable for low-income houses.

PTH and bone material strength in hypoparathyroidism as measured by impact microindentation

PTH levels might be associated with bone material strength as measured by impact microindentation. Resistance to microfracture is decreased in hypoparathyroidism and appears to be associated with more severe disease and to improve with PTH replacement.

INTRODUCTION

PTH is a key regulator of bone structure and remodeling. When PTH is absent in hypoparathyroidism (HypoPT), bone mass is increased and remodeling is decreased. In addition to bone structure and remodeling, bone material properties contribute to fracture resistance. Yet little is known about the relationship between PTH and bone material properties. Impact microindentation provides a clinical assessment of microfracture resistance, measured as the bone material strength index (BMSi).

METHODS

Case-control cross-sectional study of PTH levels and in vivo BMSi measurement by impact microindentation at the anterior tibia in HypoPT patients (n = 17) and in controls matched for age, sex, and menopausal status (n = 17), with follow-up in a subgroup of HypoPT patients (n = 5) after recombinant human parathyroid hormone (1-84) [rhPTH(1-84)] treatment.

RESULTS

BMSi was positively associated with PTH levels in controls (r = 0.58, p = 0.02) and was 11% lower (p = 0.01) in HypoPT patients as compared with controls. In HypoPT, lower BMSi was associated with a trend toward greater supplemental calcium doses (p = 0.07). BMSi increased after rhPTH(1-84) treatment in the HypoPT patients who underwent repeat microindentation.

CONCLUSIONS

PTH levels might be associated with bone material strength, although other factors might be contributory. In HypoPT, resistance to microfracture is decreased and may be associated with greater supplemental calcium doses and might increase with PTH replacement. It remains to be determined whether changes in bone remodeling and microarchitecture contribute to the effects of PTH on microfracture resistance.

Inhibition of phosphotidylinositol-3 kinase pathway by a novel naphthol derivative of betulinic acid induces cell cycle arrest and apoptosis in cancer cells of different origin

Betulinic acid (BA) is a pentacyclic triterpenoid natural product reported to inhibit cell growth in a variety of cancers. However, the further clinical development of BA got hampered because of poor solubility and pharmacological properties. Interestingly, this molecule offer several hotspots for structural modifications in order to address its associated issues. In our endeavor, we selected C-3 position for the desirable chemical modification in order to improve its cytotoxic and pharmacological potential and prepared a library of different triazoline derivatives of BA. Among them, we previously reported the identification of a potential molecule, that is, 3{1N(5-hydroxy-naphth-1yl)-1H-1,2,3-triazol-4yl}methyloxy betulinic acid (HBA) with significant inhibition of cancer cell growth and their properties. In the present study, we have shown for the first time that HBA decreased the expression of phosphotidylinositol-3 kinase (PI3K) p110α and p85α and caused significant downregulation of pAKT and of NFκB using human leukemia and breast cancer cells as in vitro models. Further it was revealed that PI3K inhibition by HBA induced cell cycle arrest via effects on different cell cycle regulatory proteins that include CDKis cyclins and pGSK3β. Also, this target-specific inhibition was associated with mitochondrial apoptosis as was reflected by the increased expression of mitochondrial bax, downregulated bcl2 and decreased mitochondrial levels of cytochrome c, together with reactive oxygen species generation and decline in mitochondrial membrane potential. The apoptotic effectors such as caspase 8, caspase 9 and caspase 3 were found to be upregulated besides DNA repair-associated enzyme, that is, PARP cleavage caused cancer cell death. Pharmacodynamic evaluation revealed that both HBA and BA were safe upto the dose of 2000 mg/kg body weight and with acceptable pharmacodynamic parameters. The in vitro data corroborated with in vivo anticancer activity wherein Ehrlich solid tumor showed that HBA as a more potent agent than BA without any body weight loss and mortality.