Nexus of Integrated Clinical Science (NICS)

Abstract Biomedical science is rapidly expanding, generating increasingly complex evidence across clinical research, diagnostics, therapeutics, and health systems science. However, this growth has not been accompanied by a proportional improvement in translating evidence into consistent, context-sensitive, and patient-centered care. This disconnect reflects not only implementation failure but also a structural limitation in how evidence is generated, interpreted, and integrated into clinical decision-making. Clinical practice occurs in heterogeneous, resource-constrained environments where evidence must be interpreted within real-world complexity. The Nexus of Integrated Clinical Science (NICS) addresses this gap by reframing the evidence–practice relationship as a continuous, integrated scientific process rather than a linear translational step. NICS emphasizes clinical relevance, methodological rigor, and real-world impact, positioning evidence as actionable knowledge embedded within clinical reasoning. By promoting integration across disciplines and aligning research with decision-making needs, NICS aims to improve patient outcomes and healthcare system performance through a unified model of evidence generation, interpretation, and application.

Abstract Postoperative pain following laparoscopic cholecystectomy remains a clinically important factor affecting recovery, patient satisfaction, and healthcare utilization. Despite being a minimally invasive procedure, pain is often multifactorial, involving incisional, visceral, and referred components, which makes single-agent analgesia insufficient. This review aimed to provide an updated and integrated overview of pharmacologic and non-pharmacologic strategies for postoperative pain management with an emphasis on multimodal and opioid-sparing approaches. Current evidence supports multimodal analgesia as the most effective strategy for pain control. Pharmacologic management typically includes acetaminophen combined with non-steroidal anti-inflammatory drugs or COX-2 inhibitors, supplemented by selective use of adjuvant agents and local or regional anesthesia techniques. Opioids should be reserved for breakthrough pain and used at the lowest effective dose to reduce the risk of adverse effects, including respiratory depression, nausea, and long-term dependence. Non-pharmacologic interventions such as early mobilization, breathing exercises, transcutaneous electrical nerve stimulation, and structured patient education further enhance recovery by reducing pain perception, anxiety, and functional limitations. Evidence also highlights the importance of individualized pain management based on patient-specific factors such as age, comorbidities, and surgical risk profile. Integration of these strategies within Enhanced Recovery After Surgery (ERAS) pathways improves clinical outcomes, shortens hospital stay, and enhances patient satisfaction. From a clinical perspective, postoperative pain management should shift toward a coordinated, multidisciplinary, and patient-centered approach that prioritizes opioid stewardship and functional recovery. The combination of pharmacologic and non-pharmacologic interventions represents the current standard of care and supports improved perioperative outcomes in laparoscopic cholecystectomy.

Abstract Background: Postmenopausal bleeding (PMB) is a clinically important symptom associated with a significant risk of endometrial malignancy. Although transvaginal ultrasonography (TVUS) is widely used for initial evaluation, its limited specificity highlights the need for additional biomarkers to improve diagnostic accuracy. This study aimed to evaluate the association between serum estradiol levels, endometrial thickness, and histopathological characteristics in postmenopausal women with PMB and confirmed endometrial malignancy.
Methods: This retrospective cross-sectional study included 55 postmenopausal women with abnormal uterine bleeding and histopathologically confirmed endometrial cancer. Clinical, demographic, laboratory, hormonal, imaging, and pathological data were extracted from medical records. Serum estradiol levels were categorized as ≤54 pg/mL and >54 pg/mL. Endometrial thickness was assessed by transvaginal ultrasonography and classified as <4 mm or ≥4 mm. Statistical analysis was performed using SPSS version 26, and a p-value <0.05 was considered significant.
Results: The majority of patients were aged 60–69 years and obese. Most cases were endometrioid adenocarcinoma grade 1. No significant associations were observed between serum estradiol levels and age, BMI, age at menopause, interval between menopause and diagnosis, or histopathological subtype. However, a significant association was found between serum estradiol levels and endometrial thickness (P = 0.022), with all patients in the elevated estradiol group demonstrating an endometrial thickness ≥4 mm.
Conclusion: Serum estradiol levels were significantly associated with endometrial thickness but not with demographic or tumor-related characteristics. These findings suggest that estradiol may serve as a complementary biomarker alongside ultrasonographic evaluation in the risk stratification of postmenopausal women with abnormal uterine bleeding.
Implications for Patient Care: Combining serum estradiol assessment with transvaginal ultrasonography may improve risk stratification in postmenopausal women with abnormal uterine bleeding. This approach can help identify higher-risk patients for endometrial pathology, guide appropriate use of biopsy, and support conservative management in low-risk cases, enhancing individualized clinical decision-making.

Abstract Background: Frozen section (FS) examination is a widely used intraoperative diagnostic technique that provides rapid histopathological assessment to guide surgical decision-making. However, its diagnostic performance across different tissue types remains an important clinical consideration. This study aimed to evaluate the diagnostic accuracy of FS in thyroid, breast, and ovarian specimens by comparing its results with permanent histopathological diagnoses.
Methods: This analytical cross-sectional study included 199 pathology records of thyroid, breast, and ovarian specimens collected from 2021 to 2024 at Shahid Rahimi Hospital, Khorramabad, Iran. Cases were selected using stratified simple random sampling. Frozen section results were compared with permanent histopathology, which was considered the gold standard. Diagnostic performance indices including sensitivity, specificity, positive predictive value, and negative predictive value were calculated using SPSS version 22.
Results: The mean age of patients was 47.35 ± 12.04 years, and most cases were female (96%). Breast specimens accounted for 67.8%, followed by thyroid (30.7%) and ovarian (1.5%) samples. FS showed complete concordance with permanent histopathology, with no false-positive or false-negative cases identified. Accordingly, sensitivity, specificity, PPV, and NPV were all 100% across all specimen types.
Conclusion: Frozen section examination demonstrated high diagnostic accuracy and complete concordance with permanent histopathology in thyroid, breast, and ovarian specimens. FS remains a reliable intraoperative tool that supports surgical decision-making, although its limitations in specific tumor types highlight the continued importance of permanent section diagnosis.
Implications for Patient Care: Frozen section (FS) examination may support intraoperative decision-making by providing rapid histopathological information, helping guide the extent of surgery and potentially reducing reoperations. FS results should be interpreted with clinical, radiological, and permanent histopathological findings, especially in challenging cases. Overall, FS may enhance surgical efficiency and support individualized patient care.

Abstract Artificial intelligence (AI) is increasingly reshaping modern healthcare by enhancing clinical decision-making, improving diagnostic accuracy, and optimizing healthcare delivery systems. This perspective explores the expanding role of AI across clinical decision support systems, predictive analytics, and personalized medicine, highlighting its potential to support clinicians in managing complex and data-intensive healthcare environments. Evidence from recent studies suggests that AI algorithms can achieve high performance in specific diagnostic tasks, particularly in radiology, dermatology, and pathology; however, their real-world clinical effectiveness remains dependent on robust validation, workflow integration, and generalizability across diverse populations.
Despite these promising developments, the integration of AI into clinical practice raises important ethical, practical, and regulatory challenges. Key concerns include limited explainability of complex models, data privacy and security risks, and the potential for algorithmic bias that may exacerbate existing healthcare disparities. In addition, gaps between retrospective model performance and prospective clinical impact continue to limit the translation of AI tools into routine care. These challenges underscore the need for a more holistic evaluation framework that extends beyond technical performance metrics to include clinical usability, transparency, and ethical robustness.
A critical appraisal of current AI applications suggests that successful implementation in healthcare depends not only on algorithmic accuracy but also on trust, interpretability, and seamless integration into clinical workflows. Furthermore, clinicians must remain central to decision-making processes, ensuring that AI functions as an assistive technology rather than a replacement for human judgment. Overall, while AI holds substantial promise for improving patient outcomes and healthcare efficiency, its safe and effective adoption requires careful attention to ethical principles, regulatory oversight, and real-world clinical validation.

Abstract Pathology is undergoing a major transformation, shifting from a discipline primarily focused on morphology-based diagnosis toward a central role in outcome-driven clinical decision-making. Advances in molecular diagnostics, digital pathology, and artificial intelligence are enabling pathologists to provide more precise, individualized, and clinically actionable information. Beyond diagnostic accuracy, modern pathology increasingly contributes to guiding treatment selection, predicting prognosis, and improving patient outcomes. In oncology, biomarker-driven classification and next-generation sequencing have enhanced personalized therapy, while integration of multi-omics data offers further potential for tailored interventions. Digital pathology and AI-based tools are supporting high-resolution imaging, automated detection, and predictive modeling, yet their clinical utility depends on real-world validation, workflow integration, and alignment with patient-centered outcomes. Multidisciplinary team approaches highlight the evolving role of pathologists as active contributors to therapeutic decisions, linking laboratory findings with clinical strategies. Despite technological progress, challenges remain, including standardization of reporting, variability across laboratories, and systematic demonstration of impact on long-term patient outcomes. To fully realize its potential, pathology must be embedded within integrated healthcare systems, leveraging structured reporting, electronic health records, and clinical decision-support systems. Future directions include prospective evaluation of AI tools, multi-omics incorporation into routine diagnostics, and stronger linkage of laboratory data with patient-reported outcomes. Ultimately, the value of pathology will be measured not only by analytical precision but also by its contribution to meaningful improvements in patient care, clinical decision-making, and healthcare system performance. This perspective underscores the importance of outcome-oriented diagnostic strategies, highlighting how advances in pathology can directly translate into more effective, personalized, and patient-centered care.

Abstract Background: The clinical heterogeneity of Polycystic Ovary Syndrome (PCOS) complicates risk stratification, particularly when phenotypic presentations mismatch endocrine severity. This study investigated clinical, anthropometric, and biochemical variations in Iranian PCOS patients to evaluate if physical traits reliably reflect underlying gonadotropin and androgenic disruptions.
Materials and Methods: In this cross-sectional study, 211 women diagnosed with PCOS (Rotterdam criteria) at a regional referral center were stratified based on ultrasonographic evidence of polycystic ovarian morphology (PCOM) into PCOM-Negative (n = 147) and PCOM-Positive (n = 64) groups. Anthropometric parameters, clinical signs (hirsutism, acne), and serum biochemical profiles were compared.
Results: The mean age was 22.92 ± 2.63 years. Clinical hirsutism and acne affected 73.5% and 47.9% of the total population, respectively. Paradoxically, peripheral clinical features and anthropometric distributions showed structural dissociation; clinical hirsutism (p = 0.316), acne (p = 0.231), and BMI categories (p = 0.230) were uniformly distributed regardless of ovarian morphology. Conversely, the biochemical profile revealed profound divergence; PCOM-Positive patients exhibited significantly higher LH levels (6.79 ± 0.95 vs. 4.93 ± 0.49 mIU/mL, p < 0.001) and marked hyperandrogenemia than their PCOM-Negative counterparts.
Conclusion: Peripheral phenotypic traits are poor predictors of internal endocrine severity in PCOS. Normal ovarian morphology can mask severe gonadotropin derangements. Objective biochemical stratification must be prioritized over subjective clinical scoring.
Implications for Patient Care: Clinicians must implement multidimensional screening protocols granting equal weight to biochemical profiling (testosterone spikes and LH/FSH inversions) alongside imaging. This eliminates diagnostic blind spots for high-risk patients with normal morphology, optimizing resource allocation and facilitating earlier targeted metabolic therapies in resource-limited regional settings.

Abstract Background: Snakebite envenomation is a significant global health concern associated with substantial morbidity and mortality, particularly due to hematologic and coagulation disturbances. Venom-induced coagulopathy and other systemic effects may persist despite antivenom therapy, and the temporal pattern of laboratory recovery remains incompletely understood. This study aimed to evaluate clinical features and longitudinal hematologic and coagulation changes following antivenom administration in patients with snakebite envenomation.
Methods: This retrospective observational study included 154 patients with confirmed snakebite envenomation admitted to a tertiary care center between 2016 and 2022. Demographic data, clinical manifestations, treatment characteristics, and laboratory parameters were extracted from medical records. Hematologic and coagulation indices included white blood cell count, hemoglobin, platelet count, prothrombin time (PT), international normalized ratio (INR), and activated partial thromboplastin time (aPTT). Patients were analyzed in full, paired (pre- and post-antivenom), and longitudinal groups. Statistical analyses included paired t tests, Wilcoxon signed-rank tests, and repeated-measures ANOVA.
Results: The mean age was 37.5 ± 17.2 years, and 77.3% were male. Most bites involved the lower extremities (64.9%). Grade 1 envenomation accounted for 55.2% of cases, while 44.8% had Grade ≥2. Local manifestations were predominant. Hemoglobin levels significantly decreased after antivenom administration (P < 0.001), while no immediate changes were observed in coagulation parameters. Longitudinal analysis showed a significant decline in white blood cell count and gradual improvement in PT and INR during hospitalization (P < 0.05).
Conclusion: Snakebite envenomation is characterized by predominant local effects with dynamic hematologic alterations during hospitalization. Coagulation recovery appears gradual rather than immediately following antivenom therapy, emphasizing the importance of serial laboratory monitoring.
Implications for Patient Care: Continuous monitoring of hematologic and coagulation parameters is recommended even after antivenom administration. Serial assessment may improve early detection of ongoing or delayed coagulopathy and support informed clinical decision-making in inpatient management of snakebite envenomation.

Abstract Background: The global emergence of antimicrobial resistance has been accelerated by the inappropriate use of antimicrobials. Hospitals are major reservoirs for the transmission of multidrug-resistant (MDR) pathogens and healthcare-associated infections (HCAIs). This study aimed to evaluate hospital staff knowledge of HCAI prevention and control and disinfection practices in Tehran, Iran.
Methods: A cross-sectional descriptive study was conducted in 51 hospitals in Tehran, Iran. Between March and June 2019, data were collected through face-to-face interviews with infection prevention and control (IPC) supervisors using a structured questionnaire assessing disinfection practices and healthcare-associated infection (HCAI) prevention and control.
Results: The study included 51 hospitals in Tehran, with one infection prevention and control (IPC) supervisor from each hospital completing the questionnaire. Regular Infection Control Committee (ICC) meetings and HCAI training programs were reported in most hospitals. Multidrug-resistant (MDR) infections were reported in 72.5% of hospitals, with antibiotic overuse and misuse identified as the leading contributing factor. Overall, respondents rated their hospitals' HCAI prevention and control practices as satisfactory; however, 13% reported being unaware of an HCAI surveillance program. Disinfectant selection was primarily guided by Infection Control Committee (ICC) recommendations, national guidelines, product quality, supplier agreements, and product availability.
Conclusion: Although most hospitals reported established IPC programs, gaps in HCAI surveillance, microbiological monitoring, and disinfectant management remain. Strengthening IPC and antimicrobial stewardship programs may improve infection prevention and patient safety.
Implications for Patient Care: Strengthening infection prevention and control programs, improving HCAI surveillance, expanding routine microbiological monitoring, and optimizing antimicrobial stewardship may reduce healthcare-associated infections, improve patient safety, and support higher-quality healthcare delivery.

Abstract Sepsis remains a major cause of morbidity and mortality worldwide despite substantial advances in antimicrobial therapy and critical care. Early recognition and accurate risk stratification are essential for improving patient outcomes, yet current diagnostic approaches based on clinical assessment and organ dysfunction scores often fail to capture the complex biological processes underlying disease progression. In recent years, increasing attention has focused on integrating circulating biomarkers with established clinical scoring systems to support more precise and individualized management. Among emerging biomarkers, soluble urokinase plasminogen activator receptor (suPAR) and presepsin have demonstrated considerable promise because they reflect complementary aspects of the host response. While suPAR primarily represents sustained immune activation and overall disease burden, presepsin is closely associated with early innate immune activation following pathogen recognition. This review summarizes the current evidence regarding the biological characteristics, diagnostic performance, and prognostic value of these biomarkers and discusses their integration with established organ dysfunction models, including SOFA, qSOFA, NEWS2, APACHE II, and SAPS II. Available evidence suggests that combining suPAR and presepsin with clinical severity scores improves risk stratification, particularly among patients with intermediate clinical risk, and may facilitate earlier recognition of disease progression, more accurate prognostic assessment, and better-informed therapeutic decisions. The review also explores emerging developments in precision medicine, including artificial intelligence–assisted predictive models, electronic health record–based clinical decision support systems, and dynamic multimodal risk assessment. Furthermore, we propose the Integrated Sepsis Risk Pyramid (ISRP) as a conceptual framework that combines clinical evaluation, organ dysfunction scoring, and biomarker assessment into a stepwise strategy for bedside decision-making. Collectively, these advances support a transition from isolated biomarker interpretation toward integrated, biologically informed, and data-driven approaches that have the potential to improve personalized sepsis management and optimize outcomes in critically ill patients.