Journal of Basic and Clinical Physiology and Pharmacology最新文献

Concerns regarding contrast-induced acute kidney injury (CI-AKI) remain significant due to the increasing use of iodinated contrast agents (ICM) in diagnostic and medical procedures. Although the frequency of CI-AKI has decreased over the decades, it continues to be a major cause of hospital-acquired acute kidney damage, thereby elevating the risk of mortality, morbidity, and prolonged hospital stays. The complex pathophysiology of CI-AKI involves vasoconstriction, oxidative stress, renal medullary hypoxia, inflammatory responses, and direct tubular toxicity. Recent discoveries have identified ferroptosis and neutrophil extracellular traps (NETs) as additional mechanisms contributing to endothelial and tubular damage. Furthermore, microRNAs such as miR-30c, miR-21, and miR-141-3p have emerged as preliminary biomarkers and therapeutic targets due to their regulatory effects on cellular apoptosis and inflammatory pathways. Procedural controversies persist regarding the risk differences between intravenous and intra-arterial contrast administration; however, evidence suggests that patient comorbidities and procedural complexity, rather than the route of administration alone, determine the risk of CI-AKI. Risk stratification tools, such as the Mehran and ACEF scores, provide frameworks for identifying high-risk patients and guiding preventive strategies. This review integrates an understanding of the molecular pathogenesis of CI-AKI, clarifies procedural debates, and highlights emerging biomarkers and risk models.

Objectives: Chronic kidney disease (CKD) is a global health issue with significant morbidity and mortality, particularly due to cardiovascular events. Early identification and management of risk factors are crucial to prevent CKD progression and complications. CKD is heterogeneous with diverse etiologies and presentations, generalizing across populations challenging. This study aims to develop accurate predictive models for cardiovascular events in CKD patients.

Methods: Biosensors capture key parameters, including SpO₂ (Oxygen saturation), PR (Pulse rate), Pi (Perfusion index), RRp (Respiration rate), and PVi (Pleth variability index), enabling comprehensive evaluation of physiological dynamics in CKD patients. Stacked Auto-Encoders (SAEs) are applied for diagnostics. Genetic risk score (GRS) and nongenetic risk score (NGRS) models are developed using natural logarithms of odds ratios (OR) of risk factors.

Results: The models integrate properties of each factor with weighted contributions to create predictive models for CKD. A novel machine learning technique incorporates automatic machine learning (AutoML).

Conclusions: The models integrate properties of each factor with weighted contributions to create predictive models for CKD. A novel machine learning technique incorporates automatic machine learning (AutoML).

Objectives: This study compared urine output at 2 and 6 h post-furosemide and explored factors influencing diuretic efficacy in acute heart failure (AHF).

Methods: A prospective study (June-August 2023) included AHF patients (≥18 years) with NT-proBNP ≥300 pg/mL. Urinary spot sodium and urine output were measured at baseline, 2, and 6 h after 40 mg IV furosemide.

Results: Among 32 patients, 26 (81.25 %) had satisfactory urine output, and 30 (93.75 %) met the target urinary sodium concentration at 2 h. No significant difference in mean urine output was found between the 2 and 6 h intervals (267 ± 189 mL vs. 298 ± 149 mL, p=0.099). Urine output correlated strongly with urinary sodium concentration (p < 0.001, R²=0.683). Serum creatinine was a significant predictor of urine output (p=0.007).

Conclusions: The comparable urine output at 2 and 6 h suggests that a 2 h assessment may be sufficient to evaluate diuretic response and guide dose adjustments in AHF.

Objectives: Diabetes mellitus is a chronic metabolic condition marked by high blood sugar levels. As a primary treatment for type 2 diabetes (T2DM), metformin has demonstrated positive effects on antioxidant and anti-inflammatory biomarkers in newly diagnosed patients.

Methods: This was a prospective observational study. Blood samples were collected three times to analyse biomarkers. The study involved newly diagnosed T2DM patients who provided written informed consent. The assessments included plasma lipid peroxidation, superoxide dismutase (SOD), catalase, and Malondialdehyde (MDA) levels. Additionally, interleukin-6 (IL-6) was measured to evaluate inflammation.

Results: HbA1c levels significantly decreased at the 6-month follow-up. Analysis showed a reduction in SOD levels at each visit, with a statistically significant difference between the first and second follow-ups (p=0.03028). MDA levels also decreased at each visit, with a statistically significant difference between the first and second follow-ups (p<0.05). Catalase levels increased at each visit, but only between the first and second follow-ups (p=0.03124). IL-6 levels decreased at each visit, with a statistically significant difference between the first and second follow-ups (p<0.05).

Conclusions: Metformin reduces oxidative stress and inflammation in patients with type 2 diabetes, aiding in protecting cells and tissues.

Objectives: Hypothyroidism is common among females of reproductive age group. The prevalence is 10.95 % of which 3.47 % are unaware of the condition and its effect on lung function. To study the pulmonary function among hypothyroid females of reproductive age group and to identify the respiratory disease pattern and also to correlate free T3, T4 and TSH Levels with pulmonary function test parameters.

Methods: This is a case control study, comprised of 156 participants divided into two groups (n₁ and n₂). Group 1 consisted of normal healthy female controls and group 2 consisted of hypothyroid female patients of age group 18-45 years. After recording anthropometric variables, vital signs and BMI, spirometry was performed and following parameters, FVC, FEV1, FEV1/FVC, PEF, PIF and FEF_25-75 % were assessed and analyzed. Statistical analysis and correlation was done.

Results: Hypothyroid patients showed a statistically significant decline in FEV1, FEV1/FVC%. Rho and Pearson's correlation showed a positive correlation between free T4 and FEV1, FEV1/FVC%, PIF, FEF_25-75 %, with FEF_25-75 % statistically significant. TSH showed a negative correlation with FVC, PEF and PIF with PEF statistically significant.

Conclusions: Our study shows that patients with hypothyroidism are more prone to develop obstructive pattern of lung disease, with a decline in small airway and large airway function.

Objectives: Evaluate associations between serum copper (Cu), rubidium (Rb), selenium (Se), strontium (Sr), and zinc (Zn) and psychophysical health in adults from Italy's Sarno River Basin within the 2025 PREVES-STOP program.

Methods: Adults aged 30-65 completed validated questionnaires plus clinical evaluation and blood sampling. Elements were quantified by collision/reaction-cell inductively coupled plasma mass spectrometry (ICP-MS). Associations were evaluated using Spearman and partial Spearman correlations.

Results: Significant associations included Zn and Rb associated with lower odds (odds ratio, OR) of severe fatigue - Recognizing and Estimating Signs of Tiredness (REST): Zn OR=0.38, 95 % confidence interval (CI) 0.21-0.68, q=0.02; Rb OR=0.33, 95 % CI 0.15-0.71, q=0.03 - while Sr was associated with higher well-being - the World Health Organization-5 Well-Being Index (WHO-5) OR=1.36, 95 % CI 1.12-1.65, q=0.02.

Conclusions: Findings support broader trace-element panels to inform psychophysical and cardiometabolic risk beyond classical toxic metals, complementing prior PREVES-STOP evidence on lead (Pb) and cadmium (Cd). Further investigation is warranted.

Objectives: To assess the efficacy and safety of Flower Pollen Extract with Vitamins (PEV) in improving urinary and sexual function in men diagnosed with chronic prostatitis (CP).

Methods: A retrospective controlled study was conducted involving 145 men with CP. Participants were divided into two groups: the intervention group received PEV (n=71) and the control group received ibuprofen (n=74). Clinical and functional outcomes were evaluated at baseline and at 1, 3, and 12 months post-treatment.

Results: Both groups exhibited comparable baseline characteristics. At 3 months, the PEV group demonstrated significantly greater improvement in IPSS (9.4 ± 2.7 vs. 10.3 ± 2.6, p=0.04), NIH-CPSI (11.5 ± 3.7 vs. 13.6 ± 4.2, p=0.002), and IIEF-5 (19.8 ± 3.6 vs. 17.7 ± 3.3, p<0.001). These benefits were maintained and further enhanced at 12 months, with mean IPSS 7.6 ± 2.1, NIH-CPSI 6.5 ± 2.4, and IIEF-5 21.6 ± 3.3 in the PEV group (all p<0.005). No significant differences were observed in Qmax.

Conclusions: Long-term PEV therapy significantly improves urinary and sexual function in men with CP compared with ibuprofen treatment, demonstrating sustained, time-dependent efficacy and excellent safety.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by progressive joint inflammation and structural damage. Two primary treatment strategies - step-up therapy and early aggressive DMARD therapy have been widely debated in clinical practice. The step-up approach begins with NSAIDs or corticosteroids, progressing to DMARDs based on disease severity, whereas early aggressive therapy prioritizes immediate DMARD initiation to prevent irreversible joint damage. This comprehensive review evaluates the effectiveness of these strategies by analyzing randomized controlled trials (RCTs), meta-analyses, and cohort studies assessing treatment outcomes, remission rates, and radiographic progression. A literature search was conducted across PubMed, Scopus, and Web of Science, including studies published between 2010 and 2024. Key clinical trials, including TICORA, COBRA, BeSt, and CAMERA, were critically analyzed to assess the comparative benefits and limitations of both treatment approaches. Evidence suggests that early aggressive therapy is associated with higher remission rates, reduced radiographic progression, and improved long-term physical function, whereas the step-up approach often leads to delayed disease control and cumulative joint damage. However, concerns related to treatment adherence, cost-effectiveness, and safety profiles necessitate further investigation. Future research should focus on biomarker-driven personalized treatment strategies, optimizing drug sequencing, and long-term comparative effectiveness studies to refine RA management.

Introduction: Esophageal achalasia is a rare motility disorder characterized by impaired lower esophageal sphincter relaxation and absent peristalsis. Diagnostic tools such as high-resolution manometry (HRM) and functional lumen imaging probe (FLIP) have improved disease recognition; however, interpretation remains complex and highly operator dependent. Artificial intelligence (AI) has emerged as a promising approach to automate data analysis and enhance diagnostic accuracy, but its specific role in achalasia is not yet clearly defined.

Content: A narrative review was conducted using PubMed, Scopus, and Web of Science, searching for studies published up to June 2025 that investigated AI applications in esophageal motility disorders, with particular attention to achalasia. Search terms included "artificial intelligence," "machine learning," "achalasia," "esophageal motility," and "high-resolution manometry." Although no prospective or interventional studies directly evaluating AI in achalasia were identified, several retrospective proof-of-concept studies applied AI algorithms to HRM and FLIP data. These studies demonstrated the feasibility of automated classification of esophageal motility disorders, with high accuracy in differentiating motility subtypes potentially applicable to achalasia. Exploratory research on AI-assisted imaging and outcome prediction also showed encouraging results.

Summary: Current evidence suggests that AI-based models can accurately analyze complex esophageal motility data and reduce interobserver variability. While direct clinical evidence in achalasia remains limited, existing studies provide a solid methodological foundation for AI-assisted diagnosis, classification, and clinical decision support in this condition.

Outlook: Future research should focus on prospective validation, multicenter data collection, and multimodal integration of clinical, physiologic, and imaging data. With targeted development and ethical governance, AI has the potential to enhance diagnostic precision, support personalized treatment strategies, and advance precision motility care in patients with achalasia.