Fractional view analysis of sexual transmitted human papilloma virus infection for public health.

The infection of human papilloma virus (HPV) poses a global public health challenge, particularly in regions with limited access to health care and preventive measures, contributing to health disparities and increased disease burden. In this research work, we present a new model to explore the transmission dynamics of HPV infection, incorporating the impact of vaccination through the Atangana-Baleanu derivative. We establish the positivity and uniqueness of the solution for the proposed model HPV infection. The threshold parameter is determined through the next-generation matrix method, symbolized by [Formula: see text]. Moreover, we investigate the local asymptotic stability of the infection-free steady-state of the system. The existence of the solutions of the recommended model is determined through fixed-point theory. A numerical scheme is presented to visualize the dynamical behavior of the system with variation of input factors. We have shown the impact of input parameters on the dynamics of the system through numerical simulations. The findings of our investigation delineated the principal parameters exerting significant influence for the control and prevention of HPV infection.

Biceps Pulley Lesions: Diagnostic Accuracy of Nonarthrographic Shoulder MRI and the Value of Various Diagnostic Signs.

BACKGROUND: There is limited data in the literature regarding the role of nonarthrographic MRI for detecting biceps pulley (BP) lesions. PURPOSE: To assess the accuracy of nonarthrographic MRI for detecting BP lesions, and to evaluate the diagnostic value of various MRI signs (superior glenohumeral ligament discontinuity/nonvisibility, long head of biceps (LHB) displacement sign or subluxation/dislocation, LHB tendinopathy, and supraspinatus and subscapularis tendon lesions) in detecting such lesions. STUDY TYPE: Retrospective. POPULATION: 84 patients (32 in BP-lesion group and 52 in BP-intact group-as confirmed by arthroscopy). FIELD STRENGTH/SEQUENCE: 1.5-T, T1-weighted turbo spin echo (TSE), T2-weighted TSE, and proton density-weighted TSE spectral attenuated inversion recovery (SPAIR) sequences. ASSESSMENT: Three radiologists independently reviewed all MRI data for the presence of BP lesions and various MRI signs. The MRI signs and final MRI diagnoses were tested for accuracy regarding detecting BP lesions using arthroscopy results as the reference standard. Furthermore, the inter-reader agreement (IRA) between radiologists was determined. STATISTICAL TESTS: Student's t-tests, Chi-squared, and Fisher's exact tests, and 4-fold table test were used. The IRA was calculated using Kappa statistics. A P-value <0.05 was considered statistically significant. RESULTS: The sensitivity, specificity, and accuracy of nonarthrographic MRI for detecting BP lesions were 65.6%-78.1%, 90.4%-92.3%, and 81%-86.9%, respectively. The highest accuracy was noticed for the LHB displacement sign (84.5%-86.9%), and the highest sensitivity was registered for the LHB tendinopathy sign (87.5%). Furthermore, the highest specificity was observed for the LHB displacement sign and LHB subluxation/dislocation sign (98.1%-100%). The IRA regarding final MRI diagnosis and MRI signs of BP lesions was good to very good (κ = 0.76-0.98). DATA CONCLUSION: Nonarthrographic shoulder MRI may show good diagnostic accuracy for detecting BP lesions. The LHB displacement sign could serve as the most accurate and specific sign for diagnosis of BP lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

OBJECTIVES: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists. METHODS: A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard. RESULTS: The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced. CONCLUSIONS: The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation. CLINICAL RELEVANCE STATEMENT: This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care. KEY POINTS: • This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans. • The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance. • The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.

Thyroid Hormone and Mitochondrial Dysfunction: Therapeutic Implications for Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed nonalcoholic fatty liver disease (NAFLD), is a widespread global health concern that affects around 25% of the global population. Its influence is expanding, and it is anticipated to overtake alcohol as the leading cause of liver failure and liver-related death worldwide. Unfortunately, there are no approved therapies for MASLD; as such, national and international regulatory health agencies undertook strategies and action plans designed to expedite the development of drugs for treatment of MASLD. A sedentary lifestyle and an unhealthy diet intake are important risk factors. Western countries have a greater estimated prevalence of MASLD partly due to lifestyle habits. Mitochondrial dysfunction is strongly linked to the development of MASLD. Further, it has been speculated that mitophagy, a type of mitochondrial quality control, may be impaired in MASLD. Thyroid hormone (TH) coordinates signals from the nuclear and mitochondrial genomes to control mitochondrial biogenesis and function in hepatocytes. Mitochondria are known TH targets, and preclinical and clinical studies suggest that TH, thyroid receptor ß (TR-ß) analogs, and synthetic analogs specific to the liver could be of therapeutic benefit in treating MASLD. In this review, we highlight how mitochondrial dysfunction contributes to development of MASLD, and how understanding the role of TH in improving mitochondrial function paved the way for innovative drug development programs of TH-based therapies targeting MASLD.

Sulconazole-Loaded Solid Lipid Nanoparticles for Enhanced Antifungal Activity: In Vitro and In Vivo Approach.

Solid lipid nanoparticles (SLNs) have the advantages of a cell-specific delivery and sustained release of hydrophobic drugs that can be exploited against infectious diseases. The topical delivery of hydrophobic drugs needs pharmaceutical strategies to enhance drug permeation, which is a challenge faced by conventional formulations containing a drug suspended in gel, creams or ointments. We report the fabrication and optimization of SLNs with sulconazole (SCZ) as a model hydrophobic drug and then a formulation of an SLN-based topical gel against fungal infections. The SLNs were optimized through excipients of glyceryl monostearate and Phospholipon® 90 H as lipids and tween 20 as a surfactant for its size, drug entrapment and sustained release and resistance against aggregation. The SCZ-SLNs were physically characterized for their particle size (89.81 ± 2.64), polydispersity index (0.311 ± 0.07), zeta potential (-26.98 ± 1.19) and encapsulation efficiency (86.52 ± 0.53). The SCZ-SLNs showed sustained release of 85.29% drug at the 12 h timepoint. The TEM results demonstrated spherical morphology, while DSC, XRD and FTIR showed the compatibility of the drug inside SLNs. SCZ-SLNs were incorporated into a gel using carbopol and were further optimized for their rheological behavior, pH, homogeneity and spreadability on the skin. The antifungal activity against Candida albicans and Trichophyton rubrum was increased in comparison to a SCZ carbopol-based gel. In vivo antifungal activity in rabbits presented faster healing of skin fungal infections. The histopathological examination of the treated skin from rabbits presented restoration of the dermal architecture. In summary, the approach of formulating SLNs into a topical gel presented an advantageous drug delivery system against mycosis.

Itraconazole Loaded Biosurfactin Micelles with Enhanced Antifungal Activity: Fabrication, Evaluation and Molecular Simulation.

Itraconazole (ITZ) is a broad-spectrum antifungal for superficial subcutaneous and systemic fungal infections. This study aimed to enhance the antifungal activity of ITZ using surfactin A (SA), a cyclic lipopeptide produced by the SA-producing Bacillus strain NH-100, possessing strong antifungal activity. SA was extracted, and ITZ-loaded SA micelles formulations were prepared via a single-pot rinsing method and characterized by particle size, zeta potential, and infrared spectroscopy. In vitro dissolution at pH 1.2, as well as hemolysis studies, was also carried out. The fabricated formulations were stable and non-spherical in shape, with an average size of about 179 nm, and FTIR spectra depicted no chemical interaction among formulation components. ITZ-loaded micelles showed decreased hemolysis activity in comparison to pure ITZ. The drug released followed the Korsmeyer-Peppas model, having R2 0.98 with the diffusion release mechanism. In an acidic buffer, drug release of all prepared formulations was in the range of 73-89% in 2 h. The molecular simulation showed the outstanding binding and stability profile of the ITZ-SA complex. The aromatic ring of the ITZ mediates a π-alkyl contact with a side chain in the SA. It can be concluded that ITZ-loaded micelles, owing to significant enhanced antifungal activity up to 6-fold due to the synergistic effect of SA, can be a promising drug delivery platform for delivery of poorly soluble ITZ.

Tioconazole-Loaded Transethosomal Gel Using Box-Behnken Design for Topical Applications: In Vitro, In Vivo, and Molecular Docking Approaches.

Tioconazole (TCZ) is a broad-spectrum fungicidal BCS class II drug with reported activity against Candida albicans, dermatophytes, and certain Staphylococci bacteria. We report the use of TCZ-loaded transethosomes (TEs) to overcome the skin's barrier function. TCZ-loaded TEs were fabricated by using a cold method with slight modification. Box-Behnken composite design was utilized to investigate the effect of independent variables. The fabricated TEs were assessed with various physicochemical characterizations. The optimized formulation of TCZ-loaded TEs was incorporated into gel and evaluated for pH, conductivity, drug content, spreadability, rheology, in vitro permeation, ex vivo permeation, and in vitro and in vivo antifungal activity. The fabricated TCZ-loaded TEs had a % EE of 60.56 to 86.13, with particle sizes ranging from 219.1 to 757.1 nm. The SEM images showed spherically shaped vesicles. The % drug permeation was between 77.01 and 92.03. The kinetic analysis of all release profiles followed Higuchi's diffusion model. The FTIR, DSC, and XRD analysis showed no significant chemical interactions between the drug and excipients. A significantly higher antifungal activity was observed for TCZ-loaded transethosomal gel in comparison to the control. The in vivo antifungal study on albino rats indicated that TCZ-loaded transethosomal gel showed a comparable therapeutic effect in comparison to the market brand Canesten®. Molecular docking demonstrated that the TCZ in the TE composition was surrounded by hydrophobic excipients with increased overall hydrophobicity and better permeation. Therefore, TCZ in the form of transethosomal gel can serve as an effective drug delivery system, having the ability to penetrate the skin and overcome the stratum corneum barrier with improved efficacy.

Knowledge, Attitude, and Practice of using Antibiotics among the Community in Eastern Province, Saudi Arabia.

Objectives: The purpose of this study is to explore and evaluate the knowledge, attitude, and practice of using antibiotics among the community in the Eastern Province, of Saudi Arabia. Methods: A cross-sectional study was conducted, from February 2022 through March 2022, among people in Eastern Province, Saudi Arabia. Results: The study population was 753 of the Saudi community. The questionnaire used open-ended and multiple-choice questions. Out of 753 participants, only 254 (55.6%) think that antibiotics can treat a bacterial infection, whereas 163 (35.7%) and 82 (17.9%) think it treats viral and fungal infections respectively. 26 (5.7%) of the respondents think that they should be stopped when the antibiotics are finished, while 72 (15.8%) think that they should be stopped when the symptoms disappear. The most common side effect in the respondent's opinion was diarrhea 183 (40%), followed by 168 (36.76%) antibiotics resistance. The major reason for taking antibiotics without a prescription was found to be mild symptoms 28 (50.90%), adequate information 21 (38.18%), difficulty in taking appointments 17 (30.90%), long waiting hours during the hospital visits 15 (27.27%). 169 (36.9%) kept the leftover antibiotics for reuse. The most common antibiotics used for self-medication were found to be Amoxicillin and clavulanic acid 23 (41.8%), followed by Amoxicillin 9 (16.36%). Knowledge of antibiotic resistance was highest in the age group 20-30 (62.7%) (P < 0.05). Conclusion: People in Saudi Arabia show a high awareness of antibiotics' proper usage, and this returns to the great awareness campaign held by The Ministry of Health (MoH).

Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.

This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86-0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups.

Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.

The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.

Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.

Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despite still being poorly understood. Several diseases, according to some data, are caused by HBPs that express themselves abnormally. Accurate identification of these molecules is the first step in investigating the roles of HBPs and understanding their biological mechanisms. For a better understanding of cell development and cellular mechanisms, accurate HBP determination from a given protein sequence is essential. Using traditional biochemical experiments, it is difficult to correctly separate HBPs from an increasing number of proteins because of the high experimental costs and lengthy experiment periods. The abundance of protein sequence data that has been gathered in the post-genomic era necessitates a computational method that is automated and enables quick and accurate identification of putative HBPs within a large number of candidate proteins. A brand-new machine-learning-based predictor is suggested as the HBP identification method. To produce the desirable feature set for the method proposed, statistical moment-based features and amino acids were combined, and the random forest was used to train the feature set. During 5-fold cross validation experiments, the suggested method achieved 94.37% accuracy and 0.9438 F1-scores, respectively, demonstrating the importance of the Hahn moment-based features.

Organic Covalent Interaction-based Frameworks as Emerging Catalysts for Environment and Energy Applications: Current Scenario and Opportunities.

The term "covalent organic framework" (COF) refers to a class of porous organic polymeric materials made from organic building blocks that have been covalently bonded. The preplanned and predetermined bonding of the monomer linkers allow them to demonstrate directional flexibility in two- or three-dimensional spaces. COFs are modern materials, and the discovery of new synthesis and linking techniques has made it possible to prepare them with a variety of favorable features and use them in a range of applications. Additionally, they can be post-synthetically altered or transformed into other materials of particular interest to produce compounds with enhanced chemical and physical properties. Because of its tunability in different chemical and physical states, post-synthetic modifications, high stability, functionality, high porosity and ordered geometry, COFs are regarded as one of the most promising materials for catalysis and environmental applications. This study highlights the basic advancements in establishing the stable COFs structures and various post-synthetic modification approaches. Further, the photocatalytic applications, such as organic transformations, degradation of emerging pollutants and removal of heavy metals, production of hydrogen and Conversion of carbon dioxide (CO2 ) to useful products have also been presented. Finally, the future research directions and probable outcomes have also been summarized, by focusing their promises for specialists in a variety of research fields.

Voriconazole Cyclodextrin Based Polymeric Nanobeads for Enhanced Solubility and Activity: In Vitro/In Vivo and Molecular Simulation Approach.

Hydroxypropyl ß-cyclodextrin (HPßCD) based polymeric nanobeads containing voriconazole (VRC) were fabricated by free radical polymerization using N, N'-methylene bisacrylamide (MBA) as a cross-linker, 2-acrylamide-2-methylpropane sulfonic acid (AMPS) as monomer and ammonium persulfate (APS) as reaction promoter. Optimized formulation (CDN5) had a particle size of 320 nm with a zeta potential of -35.5 mV and 87% EE. Scanning electron microscopy (SEM) depicted porous and non-spherical shaped beads. No evidence of chemical interaction was evident in FT-IR studies, whereas distinctive high-intensity VRC peaks were found superimposed in XRD. A stable polymeric network formation was evident in DSC studies owing to a lower breakdown in VRC loaded HPßCD in comparison to blank HPßCD. In vitro release studies showed 91 and 92% drug release for optimized formulation at pH 1.2 and 6.8, respectively, with first-order kinetics as the best-fit model and non-Fickian diffusion as the release mechanism. No evidence of toxicity was observed upon oral administration of HPßCD loaded VRC polymeric nanobeads owing to with cellular morphology of vital organs as observed in histopathology. Molecular docking indicates the amalgamation of the compounds highlighting the hydrophobic patching mediated by nanogel formulation. It can be concluded that the development of polymeric nanobeads can be a promising tool to enhance the solubility and efficacy of hydrophobic drugs such as VRC besides decreased toxicity and for effective management of fungal infections.

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.

A novel bud mutant of navel orange (Citrus sinensis) shows tolerance to chlorosis in acidic and magnesium-deficient soils.

Interveinal chlorosis in old leaves is a common occurrence in citrus orchards in southern China. The present study investigates the 'Langfeng' navel orange (LF, Citrus sinensis) grafted onto a Trifoliate orange (TO, Poncirus trifoliata) rootstock, which exhibits healthy green leaves, and the 'Newhall' navel orange (NHE, C. sinensis) grafted onto TO, which has typical magnesium (Mg) deficiency-induced chlorosis. Chemical analysis of the rhizosphere soil revealed that the pH values were around 3.92 and that both Mg and calcium (Ca) were significantly deficient in the rhizosphere soil of both grafting combinations (LF/TO and NHE/TO). Furthermore, the chlorotic leaves of NHE/TO had significantly lower levels of Mg, Ca, and phosphorus (P), and the green leaves of NHE/TO had significantly lower levels of Mg and Ca compared to the green leaves of the LF/TO. This suggests that Mg deficiency may be the primary cause of chlorosis in NHE/TO. A greenhouse study using the same graft combinations showed that the LF/TO plants had better growth than the NHE/TO, possibly by promoting Mg uptake and/or improving Mg distribution to leaves, thereby increasing carbon dioxide (CO2) assimilation and photosynthesis, optimizing carbohydrate distribution, and increasing plant biomass. This results in a phenotype that is tolerant to Mg deficiency. In conclusion, these findings suggest that the LF navel orange could be utilized in the development of new citrus varieties with improved Mg-use efficiency.

Microstructural architecture and mechanical properties of empowered cellulose-based aerogel composites via TEMPO-free oxidation.

This paper describes the development of cellulose-based aerogel composites enhanced via a new refinement process. The behaviour and microstructure of treated cellulose aerogel composites are examined including, how the constituents interact and contribute to the overall aerogel composite mechanism. The various forms of cellulose such as treated microcrystalline cellulose (MCT), nanofibrillated cellulose (NFC) and nanocrystalline cellulose (NCC) are also compared. Treated cellulose/Polyvinyl alcohol (PVA) aerogel composites show reinforced microstructural systems that enhance the mechanical property of the aerogels. The specific modulus of treated cellulose aerogels could be increased five-fold compared to the stiffness of untreated cellulose aerogels, reaching specific moduli of 21 kNm/kg. The specific strength of treated cellulose aerogels was also increased by four folds at 1.7 kNm/kg. These results provide insight into the understanding of the morphology and structure of treated cellulose-based aerogel composites.

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.

Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred .

Mitochondrial Dysfunction Plays Central Role in Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a global pandemic that affects one-quarter of the world's population. NAFLD includes a spectrum of progressive liver disease from steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis and can be complicated by hepatocellular carcinoma. It is strongly associated with metabolic syndromes, obesity, and type 2 diabetes, and it has been shown that metabolic dysregulation is central to its pathogenesis. Recently, it has been suggested that metabolic- (dysfunction) associated fatty liver disease (MAFLD) is a more appropriate term to describe the disease than NAFLD, which puts increased emphasis on the important role of metabolic dysfunction in its pathogenesis. There is strong evidence that mitochondrial dysfunction plays a significant role in the development and progression of NAFLD. Impaired mitochondrial fatty acid oxidation and, more recently, a reduction in mitochondrial quality, have been suggested to play a major role in NAFLD development and progression. In this review, we provide an overview of our current understanding of NAFLD and highlight how mitochondrial dysfunction contributes to its pathogenesis in both animal models and human subjects. Further we discuss evidence that the modification of mitochondrial function modulates NAFLD and that targeting mitochondria is a promising new avenue for drug development to treat NAFLD/NASH.

Perioperative Tissue Oximetry-driven Fluid Resuscitation Improves Flap Perfusion in Autologous Free Tissue Breast Reconstruction.

Introduction: The use of tissue oximetry for monitoring following free tissue transfer has become a common practice to facilitate early detection of poor flap perfusion. We hypothesized that T stat readings may guide fluid administration in the postoperative period and improve perfusion in patients undergoing autologous breast reconstruction. Methods: Patients undergoing free flap breast reconstruction from 2015 to 2018 were reviewed. Mean percutaneous oximetry readings of the first four postoperative days were recorded. The mean change at 24 hours from the original reading was calculated (∆TO). The study population was divided in two groups based on whether administration of intravenous fluids (IVFs) was increased/maintained (group 1) or decreased (group 2) after postoperative day 1. Results: A total of 120 patients were identified. The mean age was 53, and mean BMI was 33. Overall, patients for whom fluid administration was decreased experienced an increase in their tissue perfusion, while patient who received a bolus or maintained the same rate of IVF experienced a decrease. Patients who had a negative ∆TO experienced a statistically significant difference between groups 1 and 2 at 24 and 72 hours (-4 versus +3 and -11 versus +13, respectively). For patients with a positive ∆TO, although decreasing fluids resulted in higher readings, it did not reach statistical significance at 24 or 72 hours (0 versus +2 and +4 versus +6, respectively). Conclusions: In patients undergoing free tissue breast reconstruction, tissue oximetry readings may be used as a novel guide for postoperative fluid management.

Comparative Study of the Perspectives on the Impact of a Culturally Responsive Picture Exchange Communication System for Children With Autism Spectrum Disorder in the United Arab Emirates.

Background: The Picture Exchange Communication System (PECS) is a widely accepted way to develop the communication skills of children with developmental disabilities such as autism spectrum disorder (ASD). However, the efficacy of PECS has been largely studied in Western societies and little research is available regarding its role in the development of children with ASD in a non-Western cultural context. The purpose of this study was to compare the parent and the practitioner perceptions of the use of PECS for children with ASD in the United Arab Emirates. Method: A new scale made up of three subscales was used to collect the data from the parents and the practitioners who were either related to or working with a cohort of children with ASD. The data were analyzed using Statistical Package for the Social Sciences (SPSS) and subjected to the computation of means, t-tests, analysis of variance (ANOVA), binary logistical regression, and two-way factorial ANOVA. Results: The results showed uncertainty or neutrality among the parents and the practitioners toward the use of PECS. Variables such as age, years of experience using PECS, and gender were noted to influence perceptions of PECS. Conclusion: The need for policymakers to consider developing regular training for parents and practitioners on the use of PECS and other implications are discussed.