Tareq Nafea Alharby

Objectives: Research on patient satisfaction with community pharmacy services in Saudi Arabia has mostly concentrated on large cities, leaving a significant gap in understanding these services within the Hail region. Evaluating patient satisfaction in Hail is essential to address regional service gaps in privacy and counseling. Thus, this study was designed to assess the patient satisfaction with community pharmacy services in the Hail region. Methods: This cross-sectional study targeting the community pharmacy visitors in Hail region was conducted among 200 participants for a duration of three months (i.e., from 14 June 2024–29 September 2024). Patient satisfaction was assessed by a reliable and valid questionnaire. The instrument consisted of 17 items; and all of them were 5-point Likert scale responses (Poor = 1, Fair = 2, Good = 3, Very good = 4, Excellent = 5). To guarantee diverse geographic representation, 20 community pharmacies were purposefully chosen from several Hail City districts. In order to achieve balanced sample across all sites, a total of 200 participants were recruited, with roughly 10 patients joined from each Pharmacy. The data was analyzed for descriptive statistics using the SPSS, version 29.0. Results: The majority of the 200 participants in this study (mean age of 37.5 years) were married (60.5%) and male (56.5%). More than half had only completed high school, and only 16% had insurance. Most of the patients (53%) consistently visited the same pharmacy. Pharmacist-patient relationships received the highest level of satisfaction, but accessibility—specifically, the availability of private consultation areas—received the lowest. Regular pharmacy visitors reported higher levels of satisfaction across multiple domains, whereas younger, male, single, better-educated, insured, and healthier patients had more favorable perceptions of medicine availability, highlighting disparities in demographic and health factors. Conclusion: The majority of community pharmacy visitors were young adults, married, had a range of educational backgrounds, and frequently used the services without a prescription or health insurance. Overall, patient satisfaction was good; nevertheless, to further improve patient experience and access to pharmacy services, improvements in private consultation facilities and pharmaceutical pricing are required.

Tamoxifen (TMF), a lipid-soluble selective estrogen receptor modulator (SERM), is extensively used in the treatment of breast cancer. In this work, we systematically investigate the covalent and non-covalent interactions of TMF with the perfect B ₁₂ N ₁₂ compared to as well as with calcium- and potassium-encapsulated derivatives (B ₁₂ CaN ₁₂ and B ₁₂ KN ₁₂ ). These interactions are predominantly mediated by the dimethylamino group (-N(CH₃)₂) of TMF and were examined using density functional theory (DFT) calculations at the M06-2X level, incorporating D3 dispersion corrections (M06-2X-D3) and the 6–31 + G** basis set. The results reveal that TMF undergoes strong chemisorption on B ₁₂ KN ₁₂ (-2.27 eV) and B ₁₂ CaN ₁₂ (-1.91 eV), in contrast to weaker adsorption on the pristine B ₁₂ N ₁₂ surface (-1.75 eV). The strong binding of TMF to B₁₂N₁₂ via its dimethylamino group occurs through a synergistic combination of covalent interactions and hydrogen bonding, accompanied by a larger charge transfer from the drug to the cage, leading to a significant increase in dipole moment and a change in the energy gap. Thermodynamic analyses based on Gibbs free energy and enthalpy changes confirm that complex formation is highly stable, spontaneous, and exothermic. Notably, B ₁₂ N ₁₂ exhibits the shortest recovery time, indicating rapid TMF detachment, whereas B ₁₂ CaN ₁₂ and B ₁₂ KN ₁₂ show longer desorption times, favoring sustained release. ADMET predictions suggest moderate intestinal absorption and good membrane permeability, indicating potential for oral bioavailability. Docking studies reveal that B ₁₂ KN ₁₂ enhances TMF binding to EGFR, HER2, and Caspase-8, despite a minor reduction in ERα affinity, supporting the potential of B ₁₂ KN ₁₂ as a nanocarrier for HER2-driven breast cancer treatment.

Background: Hypothyroidism is a globally prevalent disorder with variations in its occurrence. However, few studies have reported its prevalence in Saudi Arabia. This study aimed to establish the prevalence, characteristics, and predictors of subclinical and overt hypothyroidism in a clinical cohort from the Hail region of Saudi Arabia. Methods: A retrospective cross-sectional design was used. Electronic medical records of adults (aged ≥18 years) who underwent thyroid function testing (TSH, FT4, and FT3) at the study institution were reviewed. Participants with missing laboratory or demographic data and those with hyperthyroidism (TSH < 0.4 mIU/L) were excluded. Hypothyroidism was classified biochemically using internationally accepted TSH and FT4 thresholds. The final analysis cohort comprised 724 participants. Results: Of 724 participants, 471 (65.1%) were euthyroid, 234 (32.3%) had subclinical hypothyroidism, and 19 (2.6%) had overt hypothyroidism, yielding a total biochemical hypothyroidism prevalence of 34.9% (n = 253). Women were over-represented (89.6%), and obesity was prevalent (69%). The biochemical results confirmed that overt hypothyroidism showed significantly elevated TSH and decreased FT4 compared to subclinical hypothyroidism. There were non-significant associations between gender, BMI, and hypertension with thyroid status. Age was an independent predictor of hypothyroid status (OR = 0.983, 95% CI: 0.970–0.995, p = 0.008), and systolic blood pressure was also independently associated (OR = 1.011, 95% CI: 1.002–1.019, p = 0.016). Multiple regression showed age, hypertension, gonarthrosis, and chest pain were independently associated with TSH levels. Hierarchical clustering suggested potential regional patterns in hypothyroidism prevalence. Conclusions: Hypothyroidism, mostly subclinical, was prevalent in this clinical cohort. Age and systolic blood pressure were independent predictors, while BMI and gender were not. Biochemical assessment is essential for detection, and larger prospective studies are recommended.

Objectives: To evaluate the association between ICU-acquired infections and 28-day mortality in pneumonia-induced sepsis and to explore associated immune-related gene expression patterns. Methods: A secondary analysis was performed using the publicly available GSE65682 dataset, including adult ICU patients with sepsis secondary to community-acquired pneumonia (CAP) or hospital-acquired pneumonia (HAP). Patients were stratified based on the development of ICU-acquired infections. 28-day mortality and whole-blood leukocyte gene expression at ICU admission were compared between groups. Results: Among 144 patients, 20 developed ICU-acquired infections. In the CAP subgroup, ICU-acquired infections were associated with numerically higher 28-day mortality compared to those without infection (45.5% vs. 18.2%, p = 0.05), although this finding should be interpreted with caution given the retrospective study design and limited sample size. In HAP patients, a similar pattern was not observed (22.2% vs. 19.6%). Transcriptomic analysis showed significant downregulation of the interleukin-7 receptor (IL7R) in CAP patients who developed ICU-acquired infections, with PRKACB and CD3D also demonstrating a downward trend. Conclusion: These findings suggest that early immune dysregulation may be associated with an increased susceptibility to secondary infections and potentially worse outcomes among CAP patients. IL7R may represent a candidate signal of immune dysregulation and warrants further investigation and validation in future studies.

Polypharmacy, defined as the concurrent use of 5 or more medications, is prevalent among the older adults, particularly those with multiple chronic conditions. This study assesses the prevalence of polypharmacy among older adult patients in the Hail region of Saudi Arabia and explores its association with medication adherence. A cross-sectional study was conducted, involving 247 older adult patients (aged 60 and above) from an outpatient pharmacy in Hail between August and December 2023. Medication adherence was evaluated using the Simple Medication Adherence Questionnaire. Statistical analyses, including chi-square tests, were performed to assess the relationship between polypharmacy and adherence levels. The mean age of participants was 71.83 years, with females accounting for 51.4%. The study found that 52% of participants were engaged in polypharmacy, with hypertension (70.9%) and diabetes (60.3%) being the most prevalent chronic illnesses. A significant association was found between polypharmacy and medication adherence; 63.4% of individuals taking 5 or more medications reported nonadherence, compared to 36.6% among those taking fewer than 5 (χ² = 20.448, P < .001). Overall, 57.5% of participants demonstrated suboptimal adherence. The findings underscore the high prevalence of polypharmacy and its adverse effect on medication adherence among older adults in Hail. The significant association between the number of medications and adherence levels emphasizes the importance of targeted interventions, including educational programs and caregiver support, to enhance health outcomes in this vulnerable population.

This research explores the adsorption characteristics of allicin (ALC) molecules on the metal-organic framework (MOF) designated as AgH ₈ C ₆ N ₃ O ₂ , or Ag-MOF, focusing on three different surface orientations: 100, 010, and 001. The binding strength of ALC molecules on different facets of the Ag-MOF was systematically evaluated by using density functional theory with D3 dispersion corrections (DFT-D3) and molecular dynamics (MD) simulations. Results revealed that on the (001) surface, the molecule labelled as configuration D (001-D) demonstrated a binding energy (E _bin ) of -0.894 eV. This value reflects a significantly stronger binding affinity relative to other configurations, with configuration A (001-A) showing − 0.697 eV, configuration B (001-B) at -0.732 eV, and configuration C (001-C) measuring − 0.816 eV. This stronger interaction, assessed via the PBE-D3 functional, suggests that ALC (particularly 001-D) preferentially binds more robustly to the (001) facet through physisorption, whereas interactions on the (010) and (100) surfaces are comparatively weaker in adsorption process. Significant variations in the dipole moment were observed upon ALC adsorption onto the (001) surface, which could enhance its solubility. We used Time-Dependent Density Functional Theory (TDDFT) to analyze UV-Vis spectra, finding a modest red shift in the absorption peak when ALC was adsorbed onto the (001) surface, compared to the spectrum of the pristine MOF surface. Infrared (IR) spectra calculations for both ALC and the ALC-adsorbed MOF closely matched experimental data, validating the computational approaches. MD simulations, spanning 20 to 80 picoseconds, demonstrated that ALC interacts effectively with the porous surface of the MOF, highlighting its suitability for adsorption. The theoretical analysis revealed that the electronic structure of the Ag-MOF changes when ALC molecules are adsorbed onto it. These findings offer valuable insights into the properties of ALC when adsorbed onto Ag-MOFs, providing a foundation for further exploration of their role in biomedical applications.

The quick and accurate identification of the powerful psychoactive compound Dimethyltryptamine (DMT) is still a significant drawback in forensic science and investigation concerning clinical toxicology. To overcome the issues associated with classical analytical instrumentation, a novel class of innovative highly sensitive nanosensors based on pristine and doped C ₂₀ fullerenes is presented. Using a heavy computational workflow grounded in Density Functional Theory (DFT) at the computational level B3LYP-D3/6-311G(d, p) in the CPCM solvation model (water phase), we systematically examined the sensing properties of pristine C ₂₀ , and the boron (BC ₁₉ ), germanium (GeC ₁₉ ), and silicon-doped (SiC ₁₉ ) C ₂₀ fullerenes to DMT. We use calculated values based on theoretical properties relating to the performance (adsorption energy (Eads), HOMO-LUMO gap (HLG), electrical conductivity (σ), and recovery time (τ)). According to the data, each nanomaterial will have its own unique and promising applications. The BC ₁₉ and SiC ₁₉ nanostructures presented extremely strong adsorption energies for DMT of -40.78 kcal.mol ^− 1 and − 18.82 kcal.mol ^− 1 , respectively, and recovery times that indicated effectively irreversible binding. The combination of high Eads and negligible responsibility change in electrical conductivity of BC ₁₉ and SiC ₁₉ suggests they would work well as candidates for adsorption and removal applications where stable analyte capture is desired. On the other hand, the GeC ₁₉ nanosensor showed an unprecedented and selective response, with adsorption of DMT leading to remarkable increases in the electrical conductivity of the nanomaterial of over 16 orders of magnitude, from 3.4 × 10 ^− 15 S.m ^− 1 to 1.9 × 10 ² S.m ^− 1 while exhibiting a relatively strong adsorption energy of -25.75 kcal.mol ^− 1 . This unique alteration identifies GeC ₁₉ as the top-performing disposable electrochemical sensor for fast, sensitive, and selective detection of DMT. These interactions were also confirmed by NBO, NCI, and QTAIM analyses, which indicated strong charge transfer (NBO), attractive non-covalent interactions (NCI), and medium strength hydrogen bonding (QTAIM) in the BC ₁₉ @DMT, GeC ₁₉ @DMT, and SiC ₁₉ @DMT complexes, respectively. This work not only provides the first theoretical evidence for C ₂₀ -based DMT detection, but also provides a clear pathway for experimental imagining of novel, task-specific nanosensors with important implications for future applications in forensic and point-of-care diagnostics.

P-xylene is a type of aromatic hydrocarbon that has growing biomedical and environmental importance. It has been identified as a putative biomarker for prostate cancer and its fast and selective detection in biological fluids (especially urine and blood) is critical in the diagnosis and monitoring of the disease. Similarly, removal of p-xylene from industrial effluents and wastewater is an important environmental consideration due to its toxicity and persistence. These reasons emphasize the importance of developing and calculating the performance of efficient adsorbent and electrochemical sensors for p-xylene. Here, the adsorption and sensing performance of three fullerene-based nanostructures (C ₆₀ , BC ₅₉ , NC ₅₉ ) were computationally studied for p-xylene using electronic structure calculations, charge transport analysis, dipole moment calculations, and non-covalent interaction (NCI/RDG) maps. There were significant changes in electrical conductivity induced by adsorption and transduction was strongly analyte-dependent: C ₆₀ from 1.92 × 10 ^− 5 to 2.93 × 10 ^− 4 S/m (C ₆₀ @ p-xylene), BC ₅₉ from 1.18 × 10 ^− 2 to 1.81 × 10 ^− 1 S/m (BC ₅₉ @ p-xylene), NC ₅₉ from 3.68 × 10 ^− 3 to 2.07 × 10 ^− 3 S/m (NC ₅₉ @ p-xylene). Recovery times were ultrafast for all three complexes with the fastest recovery time being for NC ₅₉ @ p-xylene (3.5 × 10 ^− 7 s), C ₆₀ = 9.8 × 10 ^− 7 , and BC ₅₉ @ p-xylene (1.9 × 10 ^− 5 s). Dipole-moment analysis showed significant polarization upon adsorption for the doped systems. The dipole moment increased from 1.50 to 3.91 D in BC ₅₉ and from 1.39 to 3.05 D in NC ₅₉ . NCI and RDG analyses found that C ₆₀ @p-xylene is mainly affected by weak van der Waals forces. BC ₅₉ @p-xylene shows stronger π-π interactions. NC ₅₉ @p-xylene has intermediate but improved attractive interactions because of nitrogen doping. These trends are consistent with the adsorption energy ranking BC ₅₉ > C ₆₀ > NC ₅₉ and highlight the changes in sensor response and recovery behavior. This study shows that BC ₅₉ has the strongest adsorption and is suitable for environmental adsorption and removal of p-xylene. NC ₅₉ , on the other hand, has extremely high conductivity modulation and ultrafast recovery, along with reasonable adsorption. This makes NC ₅₉ the most promising candidate for detecting p-xylene in early prostate cancer detection.

Numerical simulation of mass transfer in membrane contactors for water purification was carried out in this study. The models were developed to estimate permeate volume, a critical performance variable in membrane desalination. We analyzed and compared the accuracy of models: Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and a Stacking ensemble method combining MLP and XGBoost. Data preprocessing techniques were applied to improve model performance, including outlier detection using Z-scores and log transformation for normalization. A distinctive contribution of this work is the innovative application of the Jellyfish Optimizer (JO) for hyperparameter tuning, which enhanced model robustness and set it apart from conventional optimization approaches. Unlike conventional optimization algorithms, JO’s biologically inspired dynamics offers a novel balance between exploration and exploitation, leading to superior tuning of model hyperparameters. The models’ predictive accuracy was evaluated using R², RMSE, and MAE with an 85%–15% train–test split. The Stacking model outperformed single models, achieving R² values of 0.99060 (training), 0.97627 (cross-validation), and 0.99043 (testing), along with the lowest test errors (RMSE = 6.17921), confirming its efficiency in predicting permeate volume. The results show that stacking significantly improves prediction accuracy in complex tasks like membrane desalination.

A new computational framework based on machine learning was developed for prediction of Hansen solubility parameters in preparation of pharmaceutical cocrystals with improved properties. The models of Kernel Ridge Regression (KRR), Multi-Linear Regression (MLR), and Orthogonal Matching Pursuit (OMP) were optimized in prediction of three Hansen solubility parameters. Each model's performance was assessed via measuring Root Mean Square Error (RMSE), R², Mean Absolute Error (MAE), and Monte Carlo Cross-Validation (CV) scores using a Tabu Search method for optimization. The results demonstrated that KRR outperformed other models for predicting solubility parameters in the formulation. This comparative evaluation offers valuable perspectives on selecting models for similar regression assignments, stressing the significance of choosing the right algorithm according to particular output demands. The results are useful for design of medicines and screening coformers with solubility enhancement in pharmaceutical co-crystallization.