Search Results (10)

Background and Objectives: Children with cerebral palsy (CP) improve walking abilities through partial body weight-supported treadmill training (PBWSTT) and loaded treadmill training (LTT), but there is no consensus on the most effective method. This study aimed to evaluate the effects of PBWSTT and LTT on spatiotemporal gait parameters in children with CP. Materials and Methods: A randomized clinical trial involved 25 children aged 12+ with spastic diplegic CP from various outpatient clinics in Taif and Makkah between January 2024 and January 2025. Participants were randomly assigned to PBWSTT (30% body weight support, n = 12) or LTT (60% lower limb weight loading, n = 13) with 45 min sessions three times per week for eight weeks, including conventional therapy. Results: The spatiotemporal gait parameters (such as gait speed, cadence, stride length, swing phase, and swing width) significantly improved within the PBWSTT and LTT groups, but no significant difference was found between the groups. The gross motor function measure, dimension E (for walking, running, and jumping), showed significantly higher improvement in the PBWSTT group compared to the LTT group (p = 0.047). Conclusions: This study indicates that PBWSTT and LTT can improve gait parameters in children with CP, with PBWSTT promoting postural control and LTT improving mobility. These findings suggest that the proposed rehabilitation strategies can significantly improve the functional outcomes of pediatric cerebral palsy patients. Full article

Background: Chronic pain affects a significant proportion of the population in the United States and poses a significant public health concern. Beyond physical discomfort, chronic pain has been increasingly linked to cognitive dysfunction, including impairments in memory, attention, executive function, and decision-making. The relationship is particularly pronounced in older adults and may contribute to the onset or progression of neurodegenerative diseases. Objective: This comprehensive review explores the relationship between chronic pain and cognitive function, emphasizing the underlying neurobiological mechanisms, structural brain changes, and associated comorbidities. Methods: A review was conducted using peer-reviewed studies that began with the earliest pain and cognition mechanisms, followed by further investigation of cognitive effects of chronic pain, neuroimaging findings, and comorbid neuropsychiatric and neurodegenerative conditions. Sources included large-scale cohort studies, functional MRI analyses, and neurobiological investigations focusing on prefrontal cortex activity, default mode network alterations, and gray matter atrophy. Results: Chronic pain is associated with cognitive deficits through multiple converging pathways. It contributes to measurable impairments in cognitive function and is linked to structural and functional brain alterations. Regions of interest include the dorsolateral prefrontal cortex, medial prefrontal cortex, and default mode network, which can be connected to the neural resource hypothesis because of their cognitive domain impairments. A better understanding of these mechanisms highlights the importance of early recognition and multidisciplinary management strategies, including neuromodulation and cognitive rehabilitation. Future research should prioritize longitudinal studies and integrated interventions targeting both pain and cognitive health. Full article

Background and Objectives: Polycystic ovarian syndrome (PCOS) is a widespread endocrine disorder affecting 5–18% of females in their childbearing age. The aim of this study is to assess the efficacy of combining a low dosage of human chorionic gonadotropin (HCG) along with clomiphene citrate (CC) for stimulating ovulation in infertile women diagnosed with CC-resistant PCOS. Materials and Methods: A randomized controlled trial was carried out on 300 infertile CC-resistant PCOS women. All participants were assigned to two groups: the CC-HCG group and the CC-Placebo group. Subjects in the CC-HCG group were given CC (150 mg/day for 5 days starting on the 2nd day of the cycle) and HCG (200 IU/day SC starting on the 7th day of the cycle). Subjects in the CC-Placebo group were given CC and a placebo. The number of ovarian follicles > 18 mm, cycle cancellation rate, endometrial thickness, ovulation rate, clinical pregnancy rate, and occurrence of early ovarian hyper-stimulation syndrome were all outcome variables in the primary research. Results: Data from 138 individuals in the CC-HCG group and 131 participants in the CC-Placebo group were subjected to final analysis. In comparison to the CC-Placebo group, the cycle cancellation rate in the CC-HCG group was considerably lower. The CC-HCG group exhibited a substantial increase in ovarian follicles reaching > 18 mm, endometrial thickness, and ovulation rate. The clinical pregnancy rate was higher in the CC-HCG group (7.2% vs. 2.3%; CC-HCG vs. CC-Placebo). Upon adjusting for BMI and age, the findings of our study revealed that individuals in the CC-HCG group who had serum prolactin levels below 20 (ng/mL), secondary infertility, infertility duration less than 4 years, baseline LH/FSH ratios below 1.5, and serum AMH levels more than 4 (ng/mL) had a higher likelihood of achieving pregnancy. In the CC-Placebo group, there was a greater prediction of clinical pregnancy for those with serum AMH (<4), primary infertility, serum prolactin ≤ 20 (ng/mL), baseline LH/FSH < 1.5, and infertility duration < 4 years. Conclusions: The use of a small dose of HCG along with CC appeared to be an effective treatment in reducing cycle cancelation, improving the clinical pregnancy rate and ovulation rate in CC-resistant PCOS patients. The trial was registered with Clinical Trials.gov, identifier NCT02436226 Full article

Background: Organ transplantation is inherently dependent on the availability of organ donors. There is a noticeable paucity of literature addressing the rates of organ donation registration and the awareness of Islamic regulations (Fatwa) regarding organ donation within Saudi Arabia. Our study aimed to evaluate the level of organ donation registration, awareness of Islamic regulations, and knowledge of the Saudi Center for Organ Transplantation (SCOT) within the Saudi society. Methods: We conducted a cross-sectional survey from 30 March to 9 April 2023. This survey aimed to assess the awareness of Islamic (Fatwa) guidance on organ donation, the role of SCOT, and the rate of organ donation registration facilitated through the Tawakkalna app, the official health passport application in Saudi Arabia. Results: Out of 2329 respondents, 21% had registered as potential deceased organ donors, despite 87% acknowledging the importance of organ donation. Awareness of the Islamic Fatwa regarding organ donation was reported by 54.7% of respondents, and 37% recognized the Fatwa’s acceptance of brain death criteria. The likelihood of registration as organ donors was higher among Saudi citizens under 45 years of age, females, healthcare workers (HCWs), individuals with higher education, relatives of patients awaiting organ donations, those informed about the Islamic Fatwas, and those willing to donate organs to friends. Conversely, being over the age of 25, Saudi nationality, employment as an HCW, awareness of SCOT, and prior organ donation registration were predictive of a heightened awareness of Islamic Fatwas. However, perceiving the importance of organ donation correlated with a lower awareness of the Fatwas. Significant positive correlations were found between awareness of SCOT, awareness of Fatwas, and registration for organ donation. Conclusions: While the Saudi population exhibits a high regard for the importance of organ donation, this recognition is not adequately translated into registration rates. The discrepancy may be attributable to limited awareness of SCOT and the relevant Islamic Fatwas. It is imperative to initiate organ donation awareness campaigns that focus on religious authorization to boost organ donation rates and rectify prevalent misconceptions. Full article

Klebsiella is a common dangerous pathogen for humans and animals and is widely present in the digestive system. The genus Klebsiella is ubiquitous, as it is endemic to surface water, soil, and sewage. In this study, 70 samples were obtained from soil-dwelling invertebrates from September 2021 to March 2022 from Taif and Shafa in different altitudinal regions of Saudi Arabia. Fifteen of these samples were identified as Klebsiella spp. The Klebsiella isolates were genetically identified as Klebsiella pneumoniae using rDNA sequencing. The antimicrobial susceptibility of the Klebsiella isolates was determined. Amplification of virulence genes was performed using PCR. In this study, 16S rDNA sequencing showed a similarity from 98% to 100% with related K. pneumonia from the NCBI database, and the sequences were deposited in the NCBI GenBank under accession numbers ON077036 to ON077050. The growth inhibition properties of ethanolic and methanolic extracts of the medicinal plant Rhazya stricta’s leaves against K. pneumoniae strains using the minimum inhibitory concentration (MIC) method and disc diffusion were evaluated. In addition, the biofilm inhibitory potential of these extracts was investigated using crystal violet. HPLC analysis identified 19 components divided into 6 flavonoids, 11 phenolic acids, stilbene (resveratrol), and quinol, and revealed variations in the number of components and their quantities between extracts. Both extracts demonstrated interesting antibacterial properties against K. pneumoniae isolates. The 2 extracts also showed strong biofilm inhibitory activities, with percentages of inhibition extending from 81.5% to 98.7% and from 35.1% to 85.8% for the ethanolic and methanolic extracts, respectively. Rhazya stricta leaf extract revealed powerful antibacterial and antibiofilm activities against K. pneumoniae isolates and could be a good candidate for the treatment or prevention of K. pneumonia-related infections. Full article

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%. Full article

Real-time in situ measurements are increasingly being used to improve the estimations of simulation models via data assimilation techniques such as particle filter. However, models that describe complex processes such as water flow contain a large number of parameters while the data available are typically very limited. In such situations, applying particle filter to a large, fixed set of parameters chosen a priori can lead to unstable behavior, i.e., inconsistent adjustment of some of the parameters that have only limited impact on the states that are being measured. To prevent this, in this study correlation-based variable selection is embedded in the particle filter, so that at each step only a subset of the most influential parameters is adjusted. The particle filter used in this study includes genetic algorithm operators and Monte Carlo Markov Chain for alleviating filter degeneracy and sample impoverishment. The proposed method was applied to a water flow model (Hydrus-1D) in which soil water content at various depths and soil hydraulic parameters were updated. Two case studies are presented. Overall, the proposed method yielded parameters and states estimates that were more accurate and more consistent than those obtained when adjusting all the parameters. Furthermore, the results show that the higher the influence of a parameter on the model output under the current conditions, the better the estimation of this parameter is. Full article

The association of COVID-19 with neurological complications is a well-known fact, and researchers are endeavoring to investigate the mechanistic perspectives behind it. SARS-CoV-2 can bind to Toll-like receptor 4 (TLR-4) that would eventually lead to α-synuclein aggregation in neurons and stimulation of neurodegeneration pathways. Olive leaves have been reported as a promising phytotherapy or co-therapy against COVID-19, and oleuropein is one of the major active components of olive leaves. In the current study, oleuropein was investigated against SARS-CoV-2 target (main protease 3CL^pro), TLR-4 and Prolyl Oligopeptidases (POP), to explore oleuropein potency against the neurological complications associated with COVID-19. Docking experiments, docking validation, interaction analysis, and molecular dynamic simulation analysis were performed to provide insight into the binding pattern of oleuropein with the three target proteins. Interaction analysis revealed strong bonding between oleuropein and the active site amino acid residues of the target proteins. Results were further compared with positive control lopinavir (3CL^pro), resatorvid (TLR-4), and berberine (POP). Moreover, molecular dynamic simulation was performed using YASARA structure tool, and AMBER14 force field was applied to examine an 100 ns trajectory run. For each target protein-oleuropein complex, RMSD, RoG, and total potential energy were estimated, and 400 snapshots were obtained after each 250 ps. Docking analyses showed binding energy as −7.8, −8.3, and −8.5 kcal/mol for oleuropein-3CL^pro, oleuropein-TLR4, and oleuropein-POP interactions, respectively. Importantly, target protein-oleuropein complexes were stable during the 100 ns simulation run. However, an experimental in vitro study of the binding of oleuropein to the purified targets would be necessary to confirm the present study outcomes. Full article

(1) Background: Pneumocystis jirovecii pneumonia (PCP) has a substantial impact on the morbidity and mortality of patients, especially those with autoimmune disorders, thus requiring optimal dosing strategies of Trimethoprim–Sulfamethoxazole (TMP-SMX). Therefore, to ensure the safety of TMP-SMX, there is a high demand to review current evidence in PCP patients with a focus on dose optimization strategies; (2) Methods: Various databases were searched from January 2000 to December 2021 for articles in English, focusing on the dose optimization of TMP-SMX. The data were collected in a specific form with predefined inclusion and exclusion criteria. The quality of each article was evaluated using a Newcastle–Ottawa Scale (NOS) for retrospective studies, Joanna Briggs Institute (JBI) critical checklist for case reports, and Cochrane bias tool for randomized clinical trials (RCTs); (3) Results: Thirteen studies met the inclusion criteria for final analysis. Of the 13 selected studies, nine were retrospective cohort studies, two case reports, and two randomized controlled trials (RCT). Most of the studies compared the high-dose with low-dose TMP-SMX therapy for PCP. We have found that a low dose of TMP-SMX provides satisfactory outcomes while reducing the mortality rate and PCP-associated adverse events. This strategy reduces the economic burden of illness and enhances patients’ compliance to daily regimen plan; (4) Conclusions: The large-scale RCTs and cohort studies are required to improve dosing strategies to prevent initial occurrence of PCP or to prevent recurrence of PCP in immune compromised patients. Full article

Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low. Full article