Journal of population therapeutics and clinical pharmacology = Journal de la therapeutique des populations et de la pharmacologie clinique最新文献

Developmental Dyslexia (DD) and Attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders that often coexist and share complex genetic underpinnings. Our case study integrates psychological assessments and whole exome sequencing to explore the genetic basis of DD and ADHD co-occurrence in a single proband (a nine-year-old female born to healthy) from a consanguineous Pakistani family. We present a proband with symptoms of impulsivity, inattention, and severe hyperactive behavior, along with speech impairment and moderate learning disabilities. The study identified non-synonymous variations in genes associated with both disorders, such as COMT, ADRA1A, and HTR2A, DNAAF4, DCDC2, KIAA0319, LRRC56, and PHRF1. Network analysis revealed key pathways like S100 Family Signaling, G-Protein Coupled Receptor Signaling, and Dopamine Receptor Signaling shedding light on potential mechanisms underlying the observed phenotypes. The study emphasizes the complexity of these conditions and underscores the need for personalized interventions to address diagnosis challenges.

Oral lichen planus (OLP) is a chronic inflammatory disease that commonly affects the skin and mucous membranes. There is a difference of opinion among clinicians about whether OLP has been associated with oral candidiasis. Nonetheless, in OLP patients, the oral candidiasis prevalence rate ranges from 7.7 to 16.6%, as established through biopsy findings, whereas 37-50% of the prevalence rate has been noticed in culture findings. Oral candidiasis has been linked to several local and systemic factors, including salivary gland dysfunction, dental prostheses, topical or inhaled corticosteroids, smoking, and the use of systemic medications. The aim was to highlight the association of Candida in patients diagnosed with OLP, correlate the use of steroid therapy, and enumerate the factors of using steroid therapy as implicated causes for oral candidiasis. A search was made using search engines such as PubMed, Scopus, Cochrane Database of Systematic Reviews, Science Citation Index, NIH Public Access, and Clarivate Analytics (Figure 1). The keywords using the research option for this field were "Oral Candidiasis" AND "Oral Lichen planus" or "Candidiasis" AND "Corticosteroids" or "Topical Corticosteroids" AND Oral Lichen planus or "Inhalation Corticosteroids" AND "Candidiasis" or "Oral Lichen planus" AND "Corticosteroids." The database search was made for the duration of 1991 to -2021 (Table 1). Additional articles were obtained regarding the literature on OLP and oral candidiasis and were considered background material. The incidence of oral candidiasis and associated lichen planus following steroid therapy enlisted by various authors has been addressed. According to the results of this study, there is a positive correlation between the presence of oral candidiasis in the OLP's patients treated with corticosteroids. Finally, this meta-analysis concluded that there is a positive correlation between the presence of Candida species in OLP and steroid medication.

The purpose of this study is to improve the classification accuracy of the C4.5 Algorithm utilizing the forward selection technique. Breast Cancer from the UCI Machine Learning Repository is the dataset utilized. There are 286 records in the dataset with nine attributes and one class (label). The suggested model was evaluated with two existing classification models (C4.5 and Naïve Bayes) using the RapidMiner program. The procedure consists of multiple stages, the first of which consists of selecting the dominant trait using the feature selection technique (weight by information gain). The second step is forward selection based on the outcome of feature selection. Before processing, the dataset is separated into training and testing halves, where the ratios of comparison are 70:30, 80:20, and 90:10. The final step is examining the output. The experimental results demonstrate that the forward selection methodology employing the C4.5 (C4.5 + FS) method outperforms the C4.5 and Naïve Bayes classification techniques. C4.5 + FS (Split Data 70:30) has an accuracy value of 76.74%, C4.5 + FS (Split Data 80:20) has an accuracy value of 78.95%, C4.5 + FS (Split Data 90:10) has an accuracy value of 78.57%, C4.5 (Split Data 70:30) has an accuracy value of 65.12%, and Naïve Bayes (Split Data is 70:30) has an accuracy value 85.55%. In comparison to typical classification algorithms (C4.5 and Naïve Bayes), the average accuracy values increased by 12.97% and 8.32%, respectively. In terms of precision, recall, and F-measure, the forward selection strategy utilizing the C4.5 method beat all other classification techniques, achieving 79.84%, 92.50%, and 85.55%, respectively. In addition, the results demonstrated an increase in the average Area Under Curve (AUC) from 0.628 to 0.732%. Therefore, it can be inferred that the forward selection strategy can be applied to the Breast Cancer Data Set in order to increase the accuracy value of classification method C4.5.

Several sirolimus (SRL) population pharmacokinetics (PopPK) were conducted to explain its pharmacokinetic variability, and the results varied across studies. Thus, we conducted a systematic review to summarize significant predictors influencing SRL pharmacokinetic variability. Moreover, discrepancies in model methodologies across studies were also reviewed and discussed. Four databases (PubMed, CINAHL Complete, Science Direct, and Scopus) were systematically searched. The PICO framework was used to identify eligible studies conducted in humans and employ a nonlinear-mixed effects strategy. Based on the inclusion and exclusion criteria, 20 studies were included. SRL pharmacokinetics were explained using 1- or 2-compartment models. Only one study assessed the model using an external approach, while the rest employed basic or advanced internal approaches. Significant covariates influencing SRL pharmacokinetics were bodyweight, age, CYP3A5 polymorphism, gender, BSA, height, cyclosporine dose or trough concentration, triglyceride, total cholesterol, hematocrit, albumin, aspartate aminotransferase, alanine aminotransferase, and total bilirubin. Of these, bodyweight, age, and CYP3A5 polymorphism were the three most identified significant predictors for SRL clearance. This review summarizes significant predictors to predict SRL clearance, which can subsequently be used to individualize SRL maintenance dose. However, the PopPK model selected for such prediction should be based on the resemblance of population characteristics between the target population and those used to conduct the model. Moreover, the predictability of the models in the target population should be assessed before implementation in clinical practice.

Background: Long-term effects of the disease were founded and described as post-COVID-19 syndrome post-COVID syndrome are the symptoms that develop and last for four weeks or even months and cannot be explained by an alternative diagnosis; symptoms may remain for >3 months. The cause of persistent symptoms is unclear. Objectives: This study aimed to Estimate the prevalence of residual symptoms among a sample of people infected with COVID-19 who survived and recovered in Erbil city and to find out the relation between the severity of the disease and post-COVID-19 syndrome. And to know the incidence of post-COVID-19 syndrome in chronic disease patients.

Patients and methods: This cross-sectional study was conducted in 6 primary health care centers in 6 municipalities in Erbil city, Kurdistan region, Iraq. A convenience sample of randomly selected 300 patients was involved in the study depending on inclusion criteria. For one year, starting from March 1, 2021, until the end of February 2022.

Results: The prevalence of post-COVID syndrome among the 300 cases was 54.67%. Of these cases, 11.7% had one symptom, 26% had two signs, and 17% had with three or more symptoms. There was a significant statistical association between persistent symptoms and the severity of COVID-19. Also, there was a substantial statistical association between persistent symptoms and chronic disease, most (67.9%) respondents with post-COVID-19 syndromes suffered from chronic disease, and the p-value was 0.001. There was a significant statistical association between persistent symptoms and disease duration; most (73%) of cases with post-COVID-19 syndrome got the disease from 7 to 14 days, while 54.5% of them struggled with COVID-19 for >14 days.

Conclusion: Most people who have recovered from COVID-19 have many long-lasting symptoms that make it hard to go about their daily lives. This is now called a post-COVID syndrome. Getting to this status could have been caused by several things. Age, gender, whether a person has a chronic disease, disease severity, and duration are all things to consider, Even though all COVID-19 victims should be kept an eye on for long-term evaluation and treatment of post-COVID symptoms.

Based on human capital theory, the goal of this study was to look into the role of innovation capabilities in the UAE healthcare sector's competitiveness. Following the qualitative research design, data were gathered through interviews with 90 experts and professionals working in the health industry in UAE. Furthermore, the study also analyzed the UAE's Human Development Index (HDI), innovation capacities, and human development competitiveness from 2014 to 2020 using the conceptual statistics of Key Performance Indicators (KPIs) obtained in the health sector. Results revealed that UAE achieved some positive indicator results from 2020 to 2021. In addition, there are significant areas of improvement through which the UAE can build a better rank in the global competitiveness index of the health sector. The study provided key policy insights for understanding the strengths and weaknesses of innovation capabilities and human development competitiveness in the healthcare sector. Avenues of growth policy and future research directions are suggested.

Deep learning in medical image analysis has indicated increasing interest in the classification of signs of abnormalities. In this study, a new convolutional neural network (CNN) architecture (MIDNet18) Medical Image Detection Network was proposed for the classification of retinal diseases using optical coherence tomography (OCT) images. The model consists of 14 convolutional layers, seven Max Pooling layers, four dense layers, and one classification layer. A multi-class classification layer in the MIDNet18 is used to classify the OCT images into either normal or any of the three abnormal types: Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). The dataset consists of 83,484 training images, 41,741 validation images, and 968 test images. According to the experimental results, MIDNet18 obtains an accuracy of 98.86%, and their performances are compared with other standard CNN models; ResNet-50 (83.26%), MobileNet (93.29%) and DenseNet (92.5%). Also, MIDNet18 with a p-value < 0.001 has been proved to be statistically significant than other standard CNN architectures in classifying retinal diseases using OCT images.

The bone grafts taken from the patient himself (Auto genius graft) to restore the contour of the lost facial bone lost is the ideal technique on the road to recovery from injuries of war or traffic accidents. This novel surgical procedure utilizes a new modified Problast painted with a mixture of a biocompatible materials to restore the lost parts of the zygomatic bones, due to traffic accidents, shell injuries, or bullet injuries.

Background: Menstrual problems with all manifestations ranging from life-threatening bleeding to amenorrhea are considered patterns of abnormal uterine bleeding (AUB), which is until now a popular reason for referral to the gynaecologic clinic and requires a special diagnostic tool.

Objective: To assess the accuracy of hysteroscopy in diagnosing endometrial pathologies and to compare it with sonographic and histopathologic reports.

Patients and methods: A prospective study conducted in the Baghdad Teaching Hospital on 60 Iraqi females having varying complaints from abnormal uterine bleeding in pre- and post-menopausal women, infertility, and chronic pelvic pain with normal or abnormal ultrasound findings. Office hysteroscopy was done and an endometrial biopsy was obtained for histopathology for a period of 10 months between September 2020 and June 2021.

Results: The current study showed that hysteroscopy was more accurate in diagnosing sensitivity of endometrial poly (100%), fibroids (83%), hyperplasia (84.2%), and cancer (50%) whereas ultrasounds were more accurate in diagnosing sensitivity to endometrial myoma (90%). Hysteroscopy and ultrasound showed low sensitivity in detecting endometrial cancer (50% and 34%, respectively).

Conclusion: Although a transvaginal ultrasound was considered an integral part in the diagnosis of endometrial pathologies, it can be used for initial investigation, however, when suspecting endometrial pathology, hysteroscopy can be more advanced for evaluation, immediate treatment of endometrial masses, and obtaining targeted biopsies. Hysteroscopy showed high sensitivity in detecting endometrial polyps.

Aim: The aim of this study was to prepare a polyherbal mouthwash and evaluate its antimicrobial and anti-inflammatory efficacy against commercially available herbal mouthwash. The objective was to signify whether the novel herbal combination (extracts of Zingiber officinale [ginger], Curcuma longa [turmeric], and Syzygium aromaticum [clove] 5% v/w) could be a better alternative to commercially available herbal mouthwashes.

Materials and methods: An in vitro study was undertaken in which extracts of Z. officinale (ginger), C. longa (turmeric), and S. aromaticum (clove) 5% v/w were used. Seven different concentrations were prepared and tested against Streptococcus mutans, Enterococcus faecalis, Candida albicans, and Staphylococcus aureus in Mueller-Hinton agar medium. Plates were incubated aerobically at 37° C for 48 h, and the zone of inhibition was measured using a vernier caliper. Commercially available herbal mouthwash (Hiora) was used as a control group. The data were analyzed by descriptive analytics.

Results: Results showed that the efficacy of novel polyherbal mouthwash had comparatively less significant antimicrobial properties against the microorganisms as compared to the commercially available herbal mouthwash. The minimum inhibitory concentration was also found to be very high, that is, 100 μg/mL.

Conclusion: There was no significant antimicrobial and anti-inflammatory effects for the polyherbal mouthwash as compared to commercially available herbal mouthwash (Hiora). Because this combination is readily available, it can be a cost-effective alternative to commercially available herbal mouthwashes.