Miracle-dz/UniFAR-FTUs huggingface dataset:
Porphyromonas gingivalis under palmitate-induced obesogenic microenvironment modulates the inflammatory transcriptional signature of macrophage-like cells

Metabolic diseases and low-grade chronic inflammation are interconnected: obese persons are at higher risk of developing periodontitis. However, the molecular mechanisms involved in the development and progression of periodontitis in an obesogenic microenvironment in response to periodontopathogens are still lacking. This study aims to investigate the combined effects of palmitate and Porphyromonas gingivalis on the secretion of pro-inflammatory cytokines and on transcriptional landscape modifications in macrophage-like cells. U937 macrophage-like cells were treated with palmitate and stimulated with P. gingivalis for 24h. Cytokines IL-1β, TNF-α and IL-6 were measured by ELISA in the culture medium and cell extracted RNA was submitted to a microarray analysis followed by Gene Ontology analyses. P. gingivalis, in presence of palmitate, potentiated IL-1β and TNF-α secretion in comparison to palmitate alone. Gene Ontology analyses also revealed that the combination palmitate-P. gingivalis potentiated the number of gene molecular functions implicated in the regulation of immune and inflammatory pathways compared to macrophages treated with palmitate alone. Our results provide the first comprehensive mapping of gene interconnections between palmitate and P. gingivalis during inflammatory responses in macrophage-like cells. These data highlight the importance of considering systemic conditions, specifically obesogenic microenvironment, in the management of periodontal disease in obese patients.
Wireless Local Area Networks Threat Detection Using 1D-CNN

Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio channel access, user disconnection from access points, and injection attacks, among others. In this paper, we propose a machine learning algorithm to detect Layer 2 threats in WLANs through network traffic analysis. Our approach uses a deep neural network to identify malicious activity patterns. We detail the dataset used, including data preparation steps, such as preprocessing and division. We demonstrate the effectiveness of our solution through series of experiments and show that it outperforms other methods in terms of precision. The proposed algorithm can be successfully applied in Wireless Intrusion Detection Systems (WIDS) to enhance the security of WLANs and protect against potential attacks.
Interactions between large‐scale and local factors influence seed predation rates and seed loss

Abstract Herbivores often have highly variable impacts on plant fecundity. The relative contribution of different environmental factors operating at varying spatial scales in affecting this variability is often unclear. We examined how density‐dependent seed predation at local scales and regional differences in primary productivity are associated with variation in the magnitude of pre‐dispersal seed predation on Monarda fistulosa (Lamiaceae). Within M. fistulosa populations growing in a low‐productivity region (LPR), Montana, USA, and a high‐productivity region (HPR), Wisconsin, USA, we quantified the magnitude of pre‐dispersal seed predation among individual plants differing in seed head densities. Out of a total of 303 M. fistulosa plants that were surveyed, we found half as many herbivores in seed heads in the LPR (n = 133 herbivores) compared to the HPR (n = 316). In the LPR, 30% of the seed heads were damaged in plants with low seed head density, while 61% of seed heads were damaged in plants with high seed head density. Seed head damage was consistently high in the HPR (about 49% across the range of seed head density) compared to the LPR (45% across a range of seed head density). However, the proportion of seeds per seed head that were destroyed by herbivores was nearly two times higher (~38% loss) in the LPR compared to HPR (22% loss). Considering the combined effects of probability of damage and seed loss per seed head, the proportion seed loss per plant was consistently higher in the HPR regardless of seed head density. Nevertheless, because of greater seed head production, the total number of viable seeds produced per plant was higher in HPR and high‐density plants, despite being exposed to greater herbivore pressure. These findings show how large‐scale factors can interact with local‐scale factors to influence how strongly herbivores suppress plant fecundity.
AirDNA sampler: An efficient and simple device enabling high-yield, high-quality airborne environment DNA for metagenomic applications

Analyzing temporal and spatial distributions of airborne particles of biological origins is vital for the assessment and monitoring of air quality, especially with regard to public health, environmental ecology, and atmospheric chemistry. However, the analysis is frequently impeded by the low levels of biomass in the air, especially with metagenomic DNA analysis to explore diversity and composition of living organisms and their components in the air. To obtain sufficient amounts of metagenomic DNA from bioaerosols, researchers usually need a long sampling time with an expensive high-volume air sampler. This work shows the utilization of an air sampling device containing an economical, high-volume portable ventilation fan in combination with customized multi-sheet filter holders to effectively obtain high yields of genomic DNA in a relatively short time. The device, named ‘AirDNA’ sampler, performed better than other commercial air samplers, including MD8 Airport and Coriolis compact air samplers. Using the AirDNA sampler, an average DNA yield of 40.49 ng (12.47–23.24 ng at 95% CI) was obtained in only 1 hour of air sampling with a 0.85 probability of obtaining ≥10 ng of genomic DNA. The genomic DNA obtained by the AirDNA system is of suitable quantity and quality to be further used for amplicon metabarcoding sequencing of 16S, 18S, and cytochrome c oxidase I (COI) regions, indicating that it can be used to detect various prokaryotes and eukaryotes. Our results showed the effectiveness of our AirDNA sampling apparatus with a simple setup and affordable devices to obtain metagenomic DNA for short-term or long-term spatiotemporal analysis. The technique is well suited for monitoring air in built environments, especially monitoring bioaerosols for health purposes and for fine-scale spatiotemporal environmental studies.
Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

This study aims to address the challenges of managing the vast amount of data generated by Internet of Things (IoT) devices by categorizing stakeholders based on their roles in IoT security. As the number of connected devices increases, so do the associated security risks, highlighting the need for skilled stakeholders to mitigate these risks and prevent potential attacks. The study proposes a two-part approach, which involves clustering stakeholders according to their responsibilities and identifying relevant features. The main contribution of this research lies in enhancing decision-making processes within IoT security management. The proposed stakeholder categorization provides valuable insights into the diverse roles and responsibilities of stakeholders in IoT ecosystems, enabling a better understanding of their interrelationships. This categorization facilitates more effective decision making by considering the specific context and responsibilities of each stakeholder group. Additionally, the study introduces the concept of weighted decision making, incorporating factors such as role and importance. This approach enhances the decision-making process, enabling stakeholders to make more informed and context-aware decisions in the realm of IoT security management. The insights gained from this research have far-reaching implications. Not only will they benefit stakeholders involved in IoT security, but they will also assist policymakers and regulators in developing effective strategies to address the evolving challenges of IoT security.
A case of cutaneous psoriasis vulgaris achieving complete remission post major breast reconstructive surgery

Abstract Psoriasis is a debilitating chronic inflammatory systemic condition largely affecting the skin. Major surgery is relatively contraindicated due to the propensity for triggering psoriatic flares and koebnerization of the surgical scars. We detail an interesting case of complete psoriasis remission following a right nipple-sparing mastectomy with sentinel lymph node biopsy with vascular augmented pedicled transverse rectus abdominal myocutaneous (TRAM) flap in a patient with systemic psoriasis vulgaris and arthropathy. Intra-operatively, majority of the psoriatic plaques were excised or de-epithelized and used as part of the ipsilateral TRAM flap. Post-operatively, koebnerization did not occur and her psoriasis was cured completely even after cancer chemotherapy. One of several hypotheses include excision with de-epithelization of most of the psoriatic plaques reduces disease and inflammatory burden leading to complete remission. Perhaps, surgery could one day play a supporting role to existing treatment options to achieve psoriasis remission.
Assessment of the Risk Factors Related to the Length of Hospital Stay and Postoperative Complications in Patients Undergoing Primary Total Knee Arthroplasty

Objective To assess the risk factors involving longer hospital stays and early postoperative complications (first 30 days after surgery) in patients undergoing total knee arthroplasty (TKA). Materials and Methods A cross-sectional study was conducted with collection of data of patients who underwent TKA in a private hospital between 2015 and 2019. The following data were collected: age, gender, body mass index, and clinical comorbidities. We also collected intraoperative data such as the grade on the classification of the American Society of Anesthesiologists (ASA), the duration of the surgery, the length of stay, the postoperative complications, and readmission within 30 days. Statistical models were used to investigate the possible risk factors associated with longer hospital stays and postoperative complications. Results There was evidence of an increase in the length of hospital stay in older patients, with higher grades on the ASA classification or who suffered postoperative complications. For each increase in 1 year of age, we expect the length of stay to be multiplied by 1.008 (95% confidence interval [95%CI]: 1.004 to 1.012; p < 0,001). In patients who were ASA grade III, the time is expected to be multiplied by 1.297 (95%CI: 1.083 to 1.554; p = 0,005) when compared with grade-I patients. In patients who suffered postoperative complications, the time is expected to be multiplied by 1.505 (95%CI: 1.332 to 1.700; p < 0.001) compared with patients without complications. Conclusion The present study demonstrated that, in patients who underwent primary TKA, preoperative characteristics such as older age and ASA grade ≥ III, as well as the development of postoperative complications, independently predict the increase in the length of hospital stay.
Well-Leg Compartment Syndrome Due to Hemilithotomy Positioning after Arthroscopic Reconstruction of the PCL

Introduction: Acute compartment syndrome (ACS) occurs due to decreased perfusion of an osseofascial space due to increased compartmental pressure. Due to its potentially devastating sequelae, emphasis is placed on its prompt diagnosis. While fractures continue to be the most common cause of ACS, mechanisms such as crush injuries and even surgical positioning are described etiologies of compartment syndrome. ACS of the well-leg from hemilithotomy positioning has been previously depicted in medical literature; however, there have been no illustrations of this complication after elective arthroscopic-assisted posterior cruciate ligament (PCL) reconstruction. Case Report: This report discusses a patient undergoing PCL reconstruction who developed an ACS in the non-operative extremity that was positioned in hemilithotomy in a leg positioner. Conclusion: ACS is an uncommon but serious complication that can occur from hemilithotomy positioning. Surgeons should be mindful of risk factors that can place a patient at higher risk, including length of the case, body habitus, height of elevation of the leg, and method of leg support. The prompt recognition and surgical management of ACS can prevent the devastating long-term complications.
A statistical theory of optimal decision-making in sports betting

The recent legalization of sports wagering in many regions of North America has renewed attention on the practice of sports betting. Although considerable effort has been previously devoted to the analysis of sportsbook odds setting and public betting trends, the principles governing optimal wagering have received less focus. Here the key decisions facing the sports bettor are cast in terms of the probability distribution of the outcome variable and the sportsbook’s proposition. Knowledge of the median outcome is shown to be a sufficient condition for optimal prediction in a given match, but additional quantiles are necessary to optimally select the subset of matches to wager on (i.e., those in which one of the outcomes yields a positive expected profit). Upper and lower bounds on wagering accuracy are derived, and the conditions required for statistical estimators to attain the upper bound are provided. To relate the theory to a real-world betting market, an empirical analysis of over 5000 matches from the National Football League is conducted. It is found that the point spreads and totals proposed by sportsbooks capture 86% and 79% of the variability in the median outcome, respectively. The data suggests that, in most cases, a sportsbook bias of only a single point from the true median is sufficient to permit a positive expected profit. Collectively, these findings provide a statistical framework that may be utilized by the betting public to guide decision-making.
Using AI for Detection, Prediction and Classification of Retinal Detachment

The current state of machine learning (ML) and deep learning (DL) algorithms used to detect, classify and predict the onset of retinal detachment (RD) were examined in this scoping review. This severe eye condition can cause vision loss if left untreated. By analyzing the medical imaging modalities such as fundus photography, AI could help to detect peripheral detachment at an earlier stage. We have searched five databases: PubMed, Google Scholar, ScienceDirect, Scopus, and IEEE. Two reviewers independently carried out the selection of the studies and their data extractions. 32 studies fulfilled our eligibility criteria from the 666 references collected. In particular, based on the performance metrics employed in these studies, this scoping review provides a general overview of emerging trends and practices concerning using ML and DL algorithms for detecting, classifying, and predicting RD.
A new form of neurotherapy for a patient with anxiety disorder and anomic aphasia after neurosurgery for a ruptured brain aneurysm post-COVID-19.

INTRODUCTION AND OBJECTIVE The aim of this study is to evaluate the effectiveness of a new, neuromarker-based form of neurotherapy for a patient with anxiety disorders and anomic aphasia after a neurosurgical operation for a ruptured brain aneurysm of the left middle cerebral artery (MCA), detected after COVID-19. CASE REPORT A 78-year-old right-handed patient, not previously treated for any chronic diseases except stage II hypertension, contracted COVID-19, confirmed by real time RT- PCR. He was treated on an outpatient basis. Two months later, he developed an unusually severe headache and disorientation. A ruptured brain aneurysm of the left MCA was diagnosed. The patient underwent a neurosurgical operation - clipping- very well, with no neurological or neuropsychiatric disorders, except for mild aphasia and occasional anxiety attacks. Four weeks after surgery, anxiety disorder and mild aphasia worsened. High levels of anxiety on the Hospital Anxiety and Depression (HAD) Scale, and mild anomic aphasia in the Boston Naming Test (BNT) was found. A functional neuromarker of anxiety in comparision to a normative database (Human Brain Index, HBI) was detected. The patient was offered a new, neuromarker-based form of neurotherapy, which proved effective in reducing the disorders. The patient improved in social communication and is gradually returning to social activities. CONCLUSION In patients with anxiety disorders, anomic aphasia and related difficulties in social functioning after aSAH, especially after COVID-19, multidimensional diagnosis and therapy, preferably based on functional neuromarkers, is needed. HBI methodology can be successfully used in the neurodiagnosis and implementation of individualized neurotherapy for such patients.
Stack and queue layouts of some graph products

In this paper,we consider a k - stack (respectively,k - queue) layout of a graph G, which consists of a linear ordering of the vertices,and a partition of the edges into k sets of pairwise non - crossing (respectively,non - nested) edges. The problem of stack layouts (also called book embeddings) and queue layouts abstracts layout problems arising in the routing of multilayer printed circuit boards and in the design of fault - tolerant processor arrays. Motivated by numerous applications to VLSI design and graph drawing,stack layouts and queue layouts are widely studied in the literature,while this paper studies these parameters for direct product,hierarchical product and lexicographic product.
Classification and management of sexual dysfunctions in multiple sclerosis patients: A review of current literature

Multiple sclerosis (MS) is the most frequent neurological disease in young adults, with the greatest incidence between age of 30 and 35 years. Sexual dysfunctions (SDs) are frequent, but are often underestimated in patients with MS, and can have a significantly high impact on patient’s quality of life. Aim of this review is to summarize sexual dysfunctions in male and female MS patients and to illustrate current and emerging therapeutic options for treatment.
Tolvaptan’s Association with Low Risk of Acute Kidney Injury in Patients with Advanced Chronic Kidney Disease and Acute Decompensated Heart Failure

Introduction: Furosemide, a loop diuretic, is often empirically used to treat acute decompensated heart failure (ADHF) initially. Conversely, decongestion using tolvaptan, an aquaretic, is thought to maintain renal function compared to furosemide. However, it has not been investigated in patients with advanced chronic kidney disease (CKD) at high risk of developing acute kidney injury (AKI). This study aimed to investigate AKI incidence using tolvaptan add-on treatment, compared to increased furosemide treatment for patients with ADHF complicated by advanced CKD. Methods: We retrospectively studied patients with advanced CKD (estimated glomerular filtration rate [eGFR] <45 mL/min/1.73 m2) who developed ADHF under outpatient furosemide treatment. The exposure was set to tolvaptan add-on treatment, and the control was set to increased furosemide treatment. Results: Of the 163 patients enrolled, 79 were in the tolvaptan group and 84 in the furosemide group. The mean age was 71.6 years, the percentage of males was 63.8%, the mean eGFR was 15.7 mL/min/1.73 m2, and patients with CKD stage G5 were 61.9%. AKI incidence was 17.7% in the tolvaptan group and 42.9% in the furosemide group (odds ratio [95% confidence interval]: 0.34 [0.13–0.86], p = 0.023 in multivariate logistic regression analysis). Persistent AKI incidence was 11.8% in the tolvaptan group and 32.9% in the furosemide group (odds ratio [95% confidence interval]: 0.34 [0.10–1.06], p = 0.066 in the multinomial logit analysis). Conclusion: This study suggests that tolvaptan may be better than furosemide in patients with ADHF experiencing complicated advanced CKD.
A Review of Requirements for Information Models in Learning Health Systems

Learning Health System (LHS) and integrated care are challenged due to a fragmented health data landscape. An information model is agnostic to the underlying data structures and can potentially contribute to mitigating some of the gaps. In a research project, Valkyrie, we are exploring how metadata can be organized and used to promote service coordination and interoperability across levels of care. An information model is viewed as central in this context and as a future integrated LHS support. We examined the literature regarding property requirements for data, information and knowledge models in the context of semantic interoperability and an LHS. The requirements were elicited and synthesized into five guiding principles as a vocabulary to inform the information model design of Valkyrie. Further research on requirements and guiding principles for information model design and evaluation are welcomed.
Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techniques used for long-term prediction of T2DM and evaluate their performance, we conducted a scoping review using PRISMA-ScR. Of the 40 papers included in this review, 23 studies used Machine Learning (ML) as the most common AI technique, with Deep Learning (DL) models used exclusively in four studies. Of the 13 studies that used both ML and DL, 8 studies employed ensemble learning models, and SVM and RF were the most used individual classifiers. Our findings highlight the importance of accuracy and recall as validation metrics, with accuracy being used in 31 studies, followed by recall in 29 studies. These discoveries emphasize the critical role of high predictive accuracy and sensitivity in detecting positive T2DM cases.
From Assessment to Action: Exploring the Dynamics Between Maturity Assessments and Strategy Implementation in Digital Health

A country's digital health maturity is a key factor in the digital transformation of a national health system. Although many maturity assessment models exist in the literature, they perform as stand-alone tools without a clear indication to inform a country's strategy implementation in digital health. This study explores the dynamics between maturity assessments and strategy implementation in digital health. First, it analyses the word token distribution of key concepts in indicators from five pre-existing digital health maturity assessment models and those originated from the WHO's Global Strategy on Digital Health. Second, it compares type and token distributions in the selected topics mapped against the policy actions under the GSDH. The findings reveal existing maturity models with a significantly heavier focus on health information systems and highlight gaps in measuring and contextualising topics e.g., equity, inclusion, and digital frontiers.