Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.
A Meta-Analysis of a Cohort Study on the Association between Sleep Duration and Type 2 Diabetes Mellitus

Objective To study the association between sleep duration and the incidence of type 2 diabetes mellitus (T2DM) and to provide a theoretical basis for the prevention of T2DM through a meta-analysis. Methods PubMed, Web of Science, Scopus, Embase, Cochrane Library, ProQuest, CNKI, Wanfang, VIP, and SINOMED were searched from their inception until May 2020. All cohort studies on the relationship between sleep duration and T2DM in adults were included. According to the inclusion and exclusion criteria, two authors independently assessed the literature and extracted the data. Metaregression and publication bias were evaluated, and sensitivity and meta-analyses were conducted with RevMan 5.3. Results A total of 17 studies were collected, involving 737002 adults. The incidence of T2DM was 4.73% in short sleep duration (SSD) (t ≤ 6 h), 4.39% in normal sleep duration (NSD) (6 h < t < 9 h), and 4.99% in long sleep duration (LSD) (t ≥ 9 h). The meta-analysis demonstrated that SSD increased the risk of T2DM compared with NSD (RR = 1.22, 95% CI: 1.15-1.29, P < 0.001), LSD increased the risk of T2DM compared with NSD (RR = 1.26, 95% CI: 1.15-1.39, P < 0.001), and the risk of T2DM has no significant difference between SSD and LSD (RR = 0.97, 95% CI: 0.89-1.05, P = 0.41). The sensitivity of each study was robust and the publication bias was weak. Conclusion SSD or LSD can increase the risk of T2DM.
The Exploration of Novel Pharmacophore Characteristics and Multidirectional Elucidation of Structure-Activity Relationship and Mechanism of Sesquiterpene Pyridine Alkaloids from Tripterygium Based on Computational Approaches

Sesquiterpene pyridine alkaloids are a large group of highly oxygenated sesquiterpenoids, which are characterized by a macrocyclic dilactone skeleton containing 2-(carboxyalkyl) nicotinic acid and dihydro-β-agarofuran sesquiterpenoid, and are believed to be the active and less toxic components of Tripterygium. In this study, 55 sesquiterpene pyridine alkaloids from Tripterygium were subjected to identification of pharmacophore characteristics and potential targets analysis. Our results revealed that the greatest structural difference of these compounds was in the pyridine ring and the pharmacophore model-5 (Pm-05) was the best model that consisted of three features including hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and hydrophobic (HY), especially hydrophobic group located in the pyridine ring. It was proposed that 2-(carboxyalkyl) nicotinic acid part possessing a pyridine ring system was not only a pharmacologically active center but also a core of structural diversity of alkaloids from Tripterygium wilfordii. Furthermore, sesquiterpene pyridine alkaloids from Tripterygium were predicted to target multiple proteins and pathways and possibly played essential roles in the cure of Alzheimer's disease, breast cancer, Chagas disease, and nonalcoholic fatty liver disease (NAFLD). They also had other pharmacological effects, depending on the binding interactions between pyridine rings of these compounds and active cavities of the target genes platelet-activating factor receptor (PTAFR), cannabinoid receptor 1 (CNR1), cannabinoid receptor 1 (CNR2), squalene synthase (FDFT1), and heat shock protein HSP 90-alpha (HSP90AA1). Taken together, the results of this present study indicated that sesquiterpene pyridine alkaloids from Tripterygium are promising candidates that exhibit potential for development as medicine sources and need to be promoted.
Photobiomodulation: An Effective Approach to Enhance Proliferation and Differentiation of Adipose-Derived Stem Cells into Osteoblasts

Osteoporosis is regarded as the most common chronic metabolic bone condition in humans. In osteoporosis, bone mesenchymal stem cells (MSCs) have reduced cellular function. Regenerative medicine using adipose-derived stem cell (ADSC) transplantation can promote the growth and strength of new bones, improve bone stability, and reduce the risk of fractures. Various methods have been attempted to differentiate ADSCs to functioning specialized cells for prospective clinical application. However, commonly used therapies have resulted in damage to the donor site and morbidity, immune reactions, carcinogenic generation, and postoperative difficulties. Photobiomodulation (PBM) improves ADSC differentiation and proliferation along with reducing clinical difficulties such as treatment failures to common drug therapies and late initiation of treatment. PBM is a noninvasive, nonthermal treatment that encourages cells to produce more energy and to undergo self-repair by using visible green and red and invisible near-infrared (NIR) radiation. The use of PBM for ADSC proliferation and differentiation has been widely studied with multiple outcomes observed due to laser fluence and wavelength dependence. In this article, the potential for differentiating ADSCs into osteoblasts and the various methods used, including biological induction, chemical induction, and PBM, will be addressed. Likewise, the optimal laser parameters that could improve the proliferation and differentiation of ADSC, translating into clinical success, will be commented on.
The Association Between Different Obesity Phenotypes and Liver Fibrosis Scores in Elderly Individuals with Fatty Liver in Taiwan

Purpose To examine the association between different phenotypes of obesity or metabolic syndromes and liver fibrosis score in a Taiwanese elderly population with fatty liver. Patients and Methods This cross-sectional study included 1817 participants aged ≥65 years with fatty liver diagnosed by sonography. We used ethnicity-specific criteria for body mass index and metabolic syndrome, and to define obesity phenotypes as metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Correlated fibrosis severity was calculated using the nonalcoholic fatty liver disease (NAFLD) fibrosis score (NFS) and Fibrosis-4 (FIB-4). Fibrosis severity was divided into two categories according to NFS (no-to-mild fibrosis and advanced fibrosis, defined as NFS ≤ 0.676 and >0.676, respectively) and FIB-4 score (no-to-mild fibrosis and advanced fibrosis, defined as FIB-4 score ≤2.67 and >2.67, respectively). Results Compared with that in the MHNO group, the associated risk (odds ratio [OR], 95% confidence interval [CI]) of advanced fibrosis by NFS was 2.43 (1.50–3.93), 2.35 (1.25–4.41), and 6.11 (3.90–9.59), whereas that of advanced fibrosis by FIB-4 score was 1.34 (0.83–2.18), 2.37 (1.36–4.13), and 1.38 (0.82–2.31) in the MUNO, MHO, and MUO groups, respectively. Conclusion Both metabolic syndrome and obesity were positively associated with more advanced fibrosis according to NFS. The detrimental effect of obesity appears to be more than metabolic abnormalities per se in the elderly with more advanced fibrosis severity according to the FIB-4 score.
Biologically Pre-Treated Habitation Waste Water as a Sustainable Green Urine Pre-Treat Solution

The ability to recover water from urine and flush water is a critical process to allow long term sustainable human habitation in space or bases on the moon or mars. Organic N present as urea or similar compounds can hydrolyze producing free ammonia. This reaction results in an increase in the pH converting ammonium to ammonia which is volatile and not removed by distillation. The increase in pH will also cause precipitation reactions to occur. In order to prevent this, urine on ISS is combined with a pretreat solution. While use of a pretreatment solution has been successful, there are numerous draw backs including: storage and use of highly hazardous solutions, limitations on water recovery (<85%), and production of brine with pore dewatering characteristics. We evaluated the use of biologically treated habitation wastewaters (ISS and early planetary base) to replace the current pretreat solution. We evaluated both amended and un-amended bioreactor effluent. For the amended effluent, we evaluated “green” pretreat chemicals including citric acid and citric acid amended with benzoic acid. We used a mock urine/air separator modeled after the urine collection assembly on ISS. The urine/air separator was challenged continually for >6 months. Depending on the test point, the separator was challenged daily with donated urine and flushed with amended or un-amended reactor effluent. We monitored the pH of the urine, flush solution and residual pH in the urine/air separator after each urine event. We also evaluated solids production and biological growth. Our results support the use of both un-amended and amended bioreactor effluent to maintain the operability of the urine /air separator. The ability to use bioreactor effluent could decrease consumable cost, reduce hazards associated with current pre-treat chemicals, allow other membrane based desalination processes to be utilized, and improve brine characteristics.
Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) method is proposed for detecting and recognizing hidden layer defects in highways. Three common road features, namely cracks, voids, and subsidence, were collected using ground-penetrating imaging. Image segmentation was performed on acquired images. Original features were extracted from thresholded binary images and were compressed using the kl algorithm. The SVM classification algorithm was used for condition classification. For parameter optimization of the SVM algorithm, the grid search method and particle swarm optimization algorithm were used. The recognition rate using the grid search method was 88.333%; the PSO approach often yielded local maxima, and the recognition rate was 86.667%; the improved adaptive PSO algorithm avoided local maxima and increased the recognition rate to 91.667%.
Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.
Fight against COVID-19: The case of antiviral surfaces

The COVID-19 pandemic is the largest global public health outbreak in the 21st century so far. Based on World Health Organization reports, the main source of SARS-CoV-2 infection is transmission of droplets released when an infected person coughs, sneezes, or exhales. Viral particles can remain in the air and on the surfaces for a long time. These droplets are too heavy to float in air and rapidly fall down onto the surfaces. To minimize the risk of the infection, entire surrounding environment should be disinfected or neutralized regularly. Development of the antiviral coating for the surface of objects that are frequently used by the public could be a practical route to prevent the spread of the viral particles and inactivation of the transmission of the viruses. In this short review, the design of the antiviral coating to combat the spread of different viruses has been discussed and the technological attempts for minimizing the coronavirus outbreak have been highlighted.
circCEP128 Knockdown Suppresses Bladder Cancer Progression via Regulating microRNA-515-5p/SDC1 Axis

Background Dysregulation of circular RNAs (circRNAs) is associated with bladder cancer progression. Nevertheless, the mechanisms of circRNA centrosomal protein 128 (circCEP128) underlying bladder cancer progression remain poorly understood. Methods The levels of circCEP128, microRNA-515-5p (miR-515-5p) and syndecan-1 (SDC1) were determined via reverse transcription-quantitative polymerase chain reaction or Western blot. The effects of circCEP128, miR-515-5p and SDC1 on bladder cancer progression were investigated via MTT and colony formation assays, flow cytometry and transwell analysis and subcutaneous xenograft experiments. The interactions between miR-515-5p and circCEP128 or SDC1 were examined through bioinformatics prediction and luciferase reporter assay. Results circCEP128 and SDC1 were highly expressed and miR-515-5p was low expressed in bladder cancer tissues and cells. circCEP128 knockdown hindered cell proliferation, migration and invasion and promoted cell apoptosis in bladder cancer. circCEP128 loss increased miR-515-5p expression through direct interaction in bladder cancer cells. MiR-515-5p depletion mitigated the influences of circCEP128 knockdown on bladder cancer cell phenotypes. SDC1 was a direct target of miR-515-5p. circCEP128 positively regulated SDC1 expression via miR-515-5p. MiR-515-5p restrained the malignant progression of bladder cancer cells by decreasing SDC1 expression. circCEP128 knockdown hindered the growth of bladder cancer xenograft tumors by up-regulating miR-515-5p and down-regulating SDC1. Conclusion circCEP128 knockdown hampered the tumorigenesis and progression of bladder cancer by regulating miR-515-5p/SDC1 axis in vitro and in vivo, deepening our understanding on the molecular mechanisms of circCEP128 in bladder cancer.
Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes

Background High glycemic Variability (HGV) has become a stronger predictor of hypoglycemia. However, clinical factors associate with HGV still are unknown. Objective To determine clinical variables that were associated with a coefficient of variation (CV) above 36% evaluated by continuous glucose monitoring (CGM) in a group of patients with diabetes mellitus. Methods A cohort of patients with type 2 diabetes (T2D) was evaluated. Demographic variables, HbA1c, glomerular filtration rate (GFR) and treatment regimen were assessed. A bivariate analysis was performed, to evaluate the association between the outcome variable (CV> 36%) and each of the independent variables. A multivariate model was constructed to evaluate associations after controlling for confounding variables. Results CGM data from 274 patients were analyzed. CV> 36% was present in 56 patients (20.4%). In the bivariate analysis, demographic and clinical variables were included, such as time since diagnosis, hypoglycemia history, A1c, GFR and treatment established. In the multivariate analysis, GFR <45 mL/min (OR 2.81; CI 1.27,6.23; p:0.01), A1c > 9% (OR 2.81; CI 1.05,7.51; p:0.04) and hypoglycemia history (OR 2.09; CI 1.02,4.32; p:0.04) were associated with HGV. Treatment with iDPP4 (OR 0.39; CI 0.19,0.82; p:0.01) and AGLP1 (OR 0.08; CI 0.01,0.68; p:0.02) was inversely associated with GV. Conclusion Clinical variables such as GFR <45 mL/min, HbA1C>9% and a history of hypoglycemia are associated with a high GV. Our data suggest that the use of technology and treatments able to reduce glycemic variability could be useful in this population to reduce the risk of hypoglycemia and to improve glycemic control.
Evolution of the in vitro degradation of Zn–Mg alloys under simulated physiological conditions

The primary reactions occurring upon the insertion of Zn-derived materials inside an organism are of the utmost importance as the chemical species resulting from degradation of these new resorbable biomaterials will be crucial for the interaction with the surrounding tissues. In this sense, the degradation of Zn–Mg alloys under physiologically simulated conditions was investigated. The presence of magnesium (1–2%) as an alloying element in Zn alloys affected the composition of the corrosion layer and the associated in vitro degradation behaviour. A detailed physico-chemical characterization of the in vitro built-up of the corrosion layers of pure Zn and two Zn–Mg alloys (Zn–1Mg and Zn–2Mg) was achieved by confocal Raman spectroscopy, scanning electron microscopy and energy-dispersive X-ray spectroscopy. This study revealed that the presence of Mg in Zn–Mg alloys modulated simonkolleite turnover, promoted brucite formation and yielded a calcium phosphate layer containing skorpionite and hydroxyapatite. These last compounds, by being bone analogues may favour the osseointegration of Zn–Mg-based materials over that of pure Zn. When comparing both Zn–Mg alloys, the distinct evolutions observed in these compounds' (skorpionite and hydroxyapatite) formation may present specific advantageous according to the bone-healing process required. A detailed analysis of the corrosion behaviour, achieved by electrochemical techniques, showed that Zn–Mg alloys had a corrosion resistance inferior to that of pure Zn; however, the built-up of potential biocompatible corrosion layers together with superior strength and castability of these Zn–Mg alloys make them special attractive biomaterials for clinical bone implants, particularly adequate in load-bearing applications.
A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients

Purpose To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n=231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness. In addition, in order to quantify the improvement in performance added by deep learning signature, the net reclassification improvement (NRI) was calculated. The results were validated in external validation cohort (n=77). Results A deep learning signature with 9 selected features was significantly associated with OS in both training cohort (hazard ratio [HR]=5.455, 95% CI: 3.393–8.769, P<0.001) and external validation cohort (HR=3.029, 95% CI: 1.673–5.485, P=0.004). The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability with C-index of 0.800 as well as a good calibration, which was validated in external validation cohort (C-index=0.723). Additional value of deep learning signature to the nomogram was statistically significant (NRI=0.093, P=0.027 for training cohort; NRI=0.106, P=0.040 for validation cohort). Decision curve analysis confirmed the clinical usefulness of this nomogram in predicting OS. Conclusion The deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients.
Sex-specific metabolic pathways associate with Alzheimer's Disease (AD) endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery (EMIF-AD MBD) cohort

BACKGROUND: Physiological differences between males and females could contribute to the development of AD. Here, we examined metabolic pathways that may lead to precision medicine initiatives. METHODS: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, CSF biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses and assessment of metabolites' discriminatory performance in AD. RESULTS: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (AUC = 0.83, SE = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046). CONCLUSIONS: Metabolites involved in neurotransmission pathways were predictors of AD in females.