North Khorasan University of Medical Sciences

The clinical utility of doxorubicin (DOX) has been widely hampered by a dose-dependent systemic toxicity, in particular cardiotoxicity. While nanocarrier systems represent encouraging solutions, their optimization is not an easy task due to complex, nonlinear relationships between physicochemical properties and biological outcomes. This study presents a hybrid computational framework that incorporates both classical machine learning and physics-informed machine learning to predict and optimize DOX-loaded nanocarrier cytotoxicity toward normal cells. For this purpose, we compiled an extensive dataset of 77 unique nanocomposite systems with their detailed physicochemical characterizations and biological evaluations. Several ML models were trained and compared, whereas a Physics-Informed Neural Network implemented domain knowledge such as drug release kinetics, colloidal stability constraints, and diffusion limitations. The proposed PINN model showed better predictive capability (R2 = 0.89, RMSE = 0.14) compared to conventional ML methods. SHAP analysis revealed that zeta potential and size are the most governing features on cytotoxicity. Bayesian optimization revealed an optimal design space: sizes of 120-150nm, zeta potentials between - 25 and - 35 mV, loading efficiency of 5-10%, and encapsulation efficiency > 85%. Experimental validation on independent studies confirmed the model's accuracy with prediction errors < 3%. The proposed PIML framework offers a robust yet interpretable method for rational nanocarrier design that significantly improves the development of safer chemotherapeutic delivery systems by reducing the reliance on empirical optimizations.

Colorectal cancer (CRC) is a genetically varied malignancy noted for its metabolic flexibility, which enables cancer cells to adapt to different energy sources. Beta-hydroxybutyrate (BHB), a significant ketone body that is elevated during ketogenic diets, has attracted considerable attention for its potential therapeutic benefits. ​However, recent evidence indicates that BHB may paradoxically facilitate the advancement of CRC by acting as an alternative energy source, especially in cancer cells with mutations in critical genes such as APC, KRAS, and TP53. This review investigates the mechanisms through which CRC cells utilize BHB for their survival, focusing on enhanced metabolic plasticity, resistance to apoptosis, and modified responses to chemotherapy and immunotherapy. It also explores the interaction between BHB and the tumor microenvironment (TME), emphasizing how BHB can influence immune responses and tumor progression. Given the complexity of BHB’s role in CRC, the review underscores the necessity for personalized approaches that consider the tumor’s genetic and metabolic characteristics. Understanding the dual role of BHB in CRC is essential for devising more effective therapeutic strategies that can either harness or counteract its effects, thereby guiding the application of ketogenic diets and other metabolic interventions in the treatment of CRC.

Background: Epilepsy is a neurological disorder associated with anxiety, depression, lethargy. The condition itself and antiepileptic medication may cause alterations in brain waves demonstrated by quantitative electroencephalogram(QEEG) study in epileptic patients. Objective: To evaluate brain waves using power spectrum analysis of QEEG in female epileptic patients. Methods: This observational study was conducted on 40 female epileptic patients (aged 20–40 years) and 40 healthy female volunteers of similar age were control. EEG of all participants were recorded by 22 scalp electrodes for five minutes in eye-close state using an EEG data acquisition device, EEG Traveler Brain Tech 32+ CMEEG-01 to assess cortical electrical activity. EEG data was subjected to power spectral analysis using BT40 analysis software. Independent-Samples T Test was used for statistical analysis. Results: When compared to controls, epileptic patients displayed considerably lower absolute power of alpha and beta brain waves and stronger delta and theta waves in almost all electrodes across all cortical regions. Conclusion: This study concluded increased slow frequency and decreased high frequency brain waves indicating high cortical excitability along with lack of relaxing power, were associated with epilepsy patients. J Bangladesh Soc Physiol 2025;20(2): 45-52

Background Gold nanoparticles (AuNPs) are promising therapeutic-delivery agents for neuro-nanomedicine, but their cytotoxicity from conventional chemical synthesis remains a challenge. Green synthesis using ginger extract offers a safer, neuroprotective alternative. Methods PEGylated AuNPs were prepared through chemical (CS) and green (GS) methods with and without ginger extract. They were analyzed using SEM, TEM, DLS, and zeta potential. Encapsulation efficiency (EE), loading capacity (LC), drug release profile (pH 7.4, 37 °C), and cytotoxicity in PC12 cells (MTT assay, IC₅₀) were measured. Results Spherical nanoparticles (14–19 nm) were confirmed. GS-PEG-Ginger-AuNPs exhibited superior EE (80.7% vs. 61.8%) and LC (90.7% vs. 77.9%) compared to the CS counterpart. The GS formulation also showed a significantly higher cumulative drug release (~ 85% vs. ~60% at 96 h), with release kinetics indicative of anomalous transport (n = 0.52) versus Fickian diffusion (CS, n = 0.45). Critically, GS formulations were highly biocompatible (IC₅₀ > 100 µg/mL; >70–80% cell viability at 100 µg/mL), whereas CS formulations were toxic (IC₅₀ ≈ 68.7–104.4 µg/mL). Conclusion The green-synthesized PEG-Ginger-AuNPs demonstrate enhanced drug loading, sustained release, and exceptional neuronal biocompatibility, underscoring their strong potential as a translatable and safe nanoplatform for clinical neuro-nanomedicine.

Lipoprotein apheresis (LA) is an established therapy for severe, drug-refractory dyslipidemias. Nevertheless, clinicians often monitor hepatic and muscle enzymes during treatment because transient biochemical shifts can be misinterpreted as injury. In this study, we synthesize the evidence on how repeated apheresis sessions affect circulating enzymes. We systematically searched PubMed, Scopus and Web of Science from inception to August 2025 for observational human studies reporting pre- and post-apheresis values (mean and SD) for aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), and creatine phosphokinase (CPK). Effects were expressed as weighted mean differences (WMD, IU/L). Of 267 records screened, 9 articles (published 1998-2006) met eligibility for quantitative synthesis, yielding 9 ALT, 8 AST, 7 ALP, 6 GGT, 9 LDH and 6 CPK study estimates. Pooled analyses showed significant decreases in AST (WMD = -0.87; 95% CI: -1.51 to -0.23; p = 0.007), ALP (WMD = -6.28; CI: -11.75to -0.80; p = 0.02), LDH (WMD = -11.12; CI: -17.01 to -5.24; p = 0.001) and CPK (WMD = -6.91; CI: -13.06 to -0.75; p = 0.02). However, levels of ALT (WMD = -0.78; CI: -2.10 to 0.53; p = 0.29) and GGT (WMD = -2.64; CI: -8.71 to 3.44; p = 0.39) were unchanged. Across repeated-session studies, LA does not elevate hepatic or muscle injury markers and is associated with modest reductions in AST, ALP, LDH, and CPK. These findings support the biochemical safety profile of apheresis and suggest that routine enzyme monitoring should be interpreted in context.

ABSTRACTBackground and AimsStroke is recognized as a significant global health concern. The design and implementation of remote patient monitoring systems necessitate the identification of relevant data elements to effectively address the needs of individuals following a stroke. Identification and validation of these data elements can contribute to the successful design and implementation of remote patient monitoring systems. This study aimed to identify the requirements for a remote monitoring system designed for poststroke patients.MethodsThis descriptive, cross‐sectional study was conducted in three steps, including a literature review, expert panel discussions, and the Delphi technique. The literature was reviewed on electronic databases including the Cochrane Library, Wiley, Scopus, ProQuest, IEEE, PubMed, and Web of Science, using the keywords “stroke,” “secondary stroke,” “remote care,” “telehealth,” “remote patient monitoring,” and “remote monitoring” from 2000 to 2023. Three expert panel sessions were conducted to review and categorize the extracted data elements, with content validity confirmed (CVR 0.89, CVI 0.97). Finally, the Delphi technique involving 20 neurologists was used to validate the finalized data elements and system functionalities.ResultsThirty‐six studies were selected based on the inclusion criteria. A total of 75 data elements were extracted from the literature review. Finally, 61 data elements in three main categories (demographic information, clinical information, and system functionality) were classified and validated by experts as essential data elements for the design of a remote patient monitoring system for poststroke patients.ConclusionThe findings of this study encompass a range of fundamental capabilities of remote monitoring systems for poststroke patients. Consequently, this study can guide researchers interested in this field in identifying and selecting the appropriate path for developing a remote patient monitoring application for this group of patients.

Purpose: This study aimed to examine the association between personality traits and the occurrence of psychosis among methamphetamine users. Methods and Materials: A descriptive, cross-sectional study was conducted among 200 adults with confirmed methamphetamine dependence recruited from addiction treatment centers in North Khorasan Province, Iran. Methamphetamine-induced psychosis was diagnosed using structured clinical interviews based on DSM-5 criteria, initially by a clinical psychologist and then confirmed by a psychiatrist. Personality characteristics were assessed with the Millon Clinical Multiaxial Inventory-III (MCMI-III), adapted and validated for the Iranian population. Demographic and background data—including age, gender, education, marital and employment status, housing, and concurrent substance use—were collected via structured questionnaires. Statistical analyses were performed using independent-samples t-test, Mann–Whitney U test, and chi-square test at a significance level of p &lt; 0.05. Findings: Significant differences were observed between psychotic and non-psychotic methamphetamine users across several personality domains. Psychotic individuals scored higher on schizoid, schizotypal, paranoid, avoidant, dependent, depressive, sadistic, negativistic, masochistic, and borderline traits (p &lt; 0.05), while histrionic traits were significantly lower in the psychotic group (p = 0.016). Demographically, psychotic users were more likely to have lower education (p = 0.043) and to be unmarried or divorced (p = 0.048). Polysubstance use was common, with concurrent use of methamphetamine with opium (55.5%) and heroin (55%) being most frequent. Conclusion: Specific personality patterns, particularly within Clusters A and B, are strongly associated with methamphetamine-induced psychosis. Incorporating personality assessment into addiction treatment may improve risk detection and enable personalized interventions to prevent or mitigate psychotic outcomes among methamphetamine users.