Recombinant human adenovirus-p53 therapy for the treatment of oral leukoplakia and oral squamous cell carcinoma: a systematic review

Background and Objectives: Oral cancer is the 6th most common cancer in the world and oral leukoplakia is an oral potentially malignant disorder that could develop into oral cancer. This systematic review focusses on randomized clinical trials for recombinant adenovirus p-53 (rAD-p53) therapy for the treatment of oral leukoplakia and cancer. Materials and Methods: We searched for research articles on various databases such as Pubmed/Medline, Embase, CNKI (China National Knowledge Infra-structure), Springerlink, cochrane andWeb of sciences from 2003 to 2020. MeSH (Medical Subject Headings) terms were used for the search. Inclusion criteria included original research, randomized clinical trials and articles only in English language. Exclusion criteria were any articles that were not research articles, not randomized trials, non-human studies, etc. The articles were further graded on the Jadad scale. Results: 578 articles were assessed from various databases; only 3 articles were found to be appropriate for this review. Thus, meta-analysis was not performed because of heterogeneity and lack of data. In the three studies, whether rAD-p53 was used as a standalone therapy or with other therapies, there was a beneficial effect of the therapy. Furthermore, there were no serious adverse events and the only adverse events reported were fever, pain at the local injection site, flu-like symptoms and lowered WBC count. Conclusions: Thus, we can conclude that this therapy has a potential for beneficial therapeutic effects and further clinical trials with more patients need to be performed to get better understanding of the effect of rAD-p53 therapy, which probably will pave the way to its approval in other parts of the world

Blockchain-Enabled Provenance Tracking for Sustainable Material Reuse in Construction Supply Chains

The growing complexity of construction supply chains and the significant impact of the construction industry on the environment demand an understanding of how to reuse and repurpose materials. In response to this critical challenge, research gaps that are significant in promoting material circularity are described. Despite its potential, the use of blockchain technology in construction faces challenges in verifiability, scalability, privacy, and interoperability. We propose a novel multilayer blockchain framework to enhance provenance tracking and data retrieval to enable a reliable audit trail. The framework utilises a privacy-centric solution that combines decentralised and centralised storage, security, and privacy. Furthermore, the framework implements access control to strengthen security and privacy, fostering transparency and information sharing among the stakeholders. These contributions collectively lead to trusted material circularity in a built environment. The implementation framework aims to create a prototype for blockchain applications in construction supply chains

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages. To extend the benefits of BLOOM to other languages without incurring prohibitively large costs, it is desirable to adapt BLOOM to new languages not seen during pretraining. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, we find that adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at https://github.com/bigscience-workshop/multilingual-modeling

Genome-Wide Analysis of the Emerging Infection with Mycobacterium avium Subspecies paratuberculosis in the Arabian Camels (Camelus dromedarius)

Mycobacterium avium subspecies paratuberculosis (M. ap) is the causative agent of paratuberculosis or Johne's disease (JD) in herbivores with potential involvement in cases of Crohn's disease in humans. JD is spread worldwide and is economically important for both beef and dairy industries. Generally, pathogenic ovine strains (M. ap-S) are mainly found in sheep while bovine strains (M. ap-C) infect other ruminants (e.g. cattle, goat, deer), as well as sheep. In an effort to characterize this emerging infection in dromedary/Arabian camels, we successfully cultured M. ap from several samples collected from infected camels suffering from chronic, intermittent diarrhea suggestive of JD. Gene-based typing of isolates indicated that all isolates belong to sheep lineage of strains of M. ap (M. ap-S), suggesting a putative transmission from infected sheep herds. Screening sheep and goat herds associated with camels identified the circulation of this type in sheep but not goats. The current genome-wide analysis recognizes these camel isolates as a sub-lineage of the sheep strain with a significant number of single nucleotide polymorphisms (SNPs) between sheep and camel isolates (∼1000 SNPs). Such polymorphism could represent geographical differences among isolates or host adaptation of M. ap during camel infection. To our knowledge, this is the first attempt to examine the genomic basis of this emerging infection in camels with implications on the evolution of this important pathogen. The sequenced genomes of M. ap isolates from camels will further assist our efforts to understand JD pathogenesis and the dynamic of disease transmission across animal species

Effect of nocturnal hypoxemia on glycemic control among diabetic Saudi patients presenting with obstructive sleep apnea

BackgroundObstructive sleep apnea (OSA) is a prevalent disease that is associated with an increased incidence of type II diabetes mellitus (DM) if left untreated. We aimed to determine the association between glycosylated hemoglobin (HbA1c) levels and both nocturnal hypoxemia and apnea-hypopnea index (AHI) among a Saudi patients with OSA.MethodsA cross-sectional study that enrolled 103 adult patients diagnosed with DM and confirmed to have OSA by full night attended polysomnography between 2018 and 2021. Those who presented with acute illness, chronic obstructive pulmonary disease (COPD)/restrictive lung diseases causing sleep-related hypoxemia, or no available HbA1c level within 6 months before polysomnography were excluded from the study. Univariate and multivariate linear regression analyses between HbA1c levels and parameters of interest were tested.ResultsSixty-seven (65%) of the studied population had uncontrolled DM (HbA1c ≥7%). In univariate regression analysis, there was a significant positive association between HbA1c, and sleep time spent with an oxygen saturation below 90% (T90), female gender, and body mass index (BMI) (p<0.05) but not AHI, or associated comorbidities (p>0.05). In the multivariate analysis, HbA1c was positively associated with increasing T90 (p<0.05), and ODI (p<0.05), but not with AHI (p>0.05).ConclusionNocturnal hypoxemia could be an important factor affecting glycemic control in patients with OSA suffering from DM irrespective of the severity of both diseases

Intelligent Prediction of Stuck Pipe Remediation using Machine Learning Algorithms

Stuck pipe is still a major operational challenge that imposes a significant amount of downtime and associated costs to petroleum and gas exploration operations. The possibility of freeing stuck pipe depends on response time and subsequent surface action taken by the driller during and after the sticking is experienced. A late and improper reaction not only causes a loss of time in trying to release stuck pipe but also results in the loss of an important portion of expensive tubular, downhole equipment and tools. Therefore, a fast and effective response should be made to release the stuck pipe. Investigating previous successful responses that have solved stuck pipe issues makes it possible to predict and adopt the proper treatments. This paper presents a study on the application of machine learning methodologies to develop an expert system that can be used as a reference guide for the drilling engineer to make intelligent decisions and reduce the lost time for each stuck pipe event. Field datasets, including the drilling operation parameters, formation type, and fluid mud characteristics, were collected from 385 wells drilled in Southern Iraq from different fields. The new models were developed to predict the stuck pipe solution for vertical and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. These machine learning methods offer insights that could improve response time and strategies for treating stuck pipe

Pediatric Neurology Workforce in Saudi Arabia: A 5-Year Update

Background: The medical workforce plays a pivotal role in advancing human health, particularly within the healthcare system of Saudi Arabia. While government-employed healthcare providers form the central structure of the system and offer free healthcare services, the private healthcare sector is also witnessing significant growth. In parallel, the field of child neurology has experienced notable transformations in recent years, with continued expansion. This expansion brings forth a range of challenges for both current and future pediatric neurologists, necessitating careful consideration and proactive measures to address them. Aim of the study: To investigate and analyze the current characteristics of the workforce, with a specific focus on their employment status and related data. Methods: This is a cross-sectional analysis, using a survey to assess the distribution of pediatric neurologists in Saudi Arabia (SA). The final analytical sample included 82 subjects, working in 13 regions in SA. A descriptive analysis was used to address the study question. Results: The survey received responses from a total of 82 pediatric neurologists in Saudi Arabia (response rate 55%), with 38 (46%) being men and 44 (54%) being women. The mean age was 33 ± 1.225 years. The majority of participants practiced in major cities such as Riyadh and Jeddah. Nearly 50% of pediatric neurologists experienced some form of delay in obtaining their first job, ranging from 1 to 36 months. Conclusion: The landscape of the pediatric neurology workforce is currently witnessing noteworthy demographic shifts. With the majority of practitioners concentrated in major cities, there is an ongoing demand for qualified professionals in peripheral areas. This study describes the real-life challenges faced by pediatric neurologists, particularly the delay in securing employment after graduation, and underscores the critical importance of addressing these persistent issues along the journey of pediatric neurology

Extraction and Evaluation of Bioactive Compounds from Date (Phoenix dactylifera) Seed Using Supercritical and Subcritical CO2 Techniques

Date (Phoenix dactylifera) seed is a potential source of natural antioxidants, and the use of innovative green and low temperature antioxidant recovery techniques (using CO2 as solvent) such as supercritical fluid (SFE) and subcritical (SubCO2) extractions can improve their yields and quality in the extracts. SFE, SubCO2 and Soxhlet techniques were employed to enrich antioxidants in extracts from Sukari (SKSE), Ambara (AMSE), Majdool (MJSE) and Sagai (SGSE) date seeds. Extract yields were evaluated and modelled for SFE extract using response surface methodology. Significantly higher (p < 0.05) phenolics (143.48–274.98 mg GAE/100 g), flavonoids (78.35–141.78 mg QE/100 g), anthocyanins (0.39–1.00 mg/100 g), and carotenoid (1.42–1.91 mg BCE/100 g) contents were detected in extracts obtained using SFE and SubCO2 methods. The evaluation of in vitro antioxidant properties showed that SFE and SubCO2 seed extracts demonstrated promising antioxidant (13.42–23.83 µg AAE/mL), antiradical (228.76–109.69 µg/mL DPPH IC50), ferric reducing antioxidant power (1.43–2.10 mmol TE/100 g) and ABTS cation scavenging (375.74-717.45 µmol TE/100 g) properties that were significantly higher than Soxhlet extracts. Both SFE and SubCO2 techniques can be effectively utilized as innovative and environmentally friendly alternatives to obtain high quality antioxidant rich extracts from date seed. These extracts may have potential functional and nutraceutical applications