Medical Science Monitor最新文献

BACKGROUND Postoperative delirium (POD) is a clinically significant complication in elderly orthopedic patients that is associated with adverse outcomes and may be differentially affected by transfusion type. However, systematic comparisons in hip arthroplasty (HA) patients remain limited. This study evaluated the effect of 2 types of intraoperative blood transfusions on the incidence of POD in elderly patients undergoing HA. MATERIAL AND METHODS We retrospectively analyzed the medical records of 544 elderly patients (age ≥65 years) who underwent HA with intraoperative transfusion at our orthopedic center between 2018 and April 2025. Baseline characteristics were balanced between groups using inverse probability of treatment weighting (IPTW) derived from propensity score matching. The primary outcome was the incidence of POD, while secondary outcomes included postoperative complications such as deep vein thrombosis and pulmonary infection, as well as the length of hospital stay. RESULTS Following IPTW adjustment, generalized linear regression analysis revealed that the autologous transfusion group had a significantly lower incidence of POD compared to the allogeneic transfusion group (4.2% vs 11.9%; adjusted OR=0.32, 95% CI: 0.13-0.79, P=0.014). No significant differences were found between the groups in secondary outcomes. CONCLUSIONS This study suggests that autologous transfusion is associated with a significantly reduced risk of POD compared to allogeneic transfusion in elderly patients undergoing HA. These results indicate that autologous transfusion may be the best alternative for this patient population, although further large-scale studies are necessary to validate its definitive clinical benefits.

BACKGROUND Patellofemoral pain (PFP) syndrome is a common cause of anterior knee pain. Excessive tibial external rotation during knee flexion increases patellofemoral stress, and uncontrolled tibiofemoral rotation is associated with PFP syndrome. Assessing knee flexion with and without tibiofemoral joint rotation control (TFRC) may help clinicians detect impaired rotational control and guide interventions. However, few studies have compared knee flexion angles under both conditions between individuals with and without PFP syndrome. This study compared active knee flexion angles with and without TFRC and evaluated tibial external rotation using the distance between the examination table and the lateral malleolus (DBTL). MATERIAL AND METHODS A total of 24 male participants (12 with PFP and 12 without PFP) underwent knee flexion angle assessments under both TFRC and non-TFRC conditions. To minimize hip movement, the examiner manually stabilized the femoral epicondyle of the test leg to keep the patella facing forward. DBTL was measured during knee flexion without TFRC to assess tibial external rotation. Data were analyzed using a 2-way repeated-measures analysis of variance (ANOVA) for knee flexion angles and independent t-tests for DBTL comparisons. RESULTS No significant differences in knee flexion angles were observed without TFRC. However, with TFRC, the PFP group exhibited significantly reduced knee flexion angles (P<0.01). The PFP group also showed significantly higher DBTL (P<0.01), indicating greater tibial external rotation. CONCLUSIONS These results suggest that uncontrolled tibiofemoral joint rotation may represent a characteristic movement pattern in individuals with PFP and underscore the importance of considering TFRC in clinical assessments in PFP syndrome.

BACKGROUND We suggest that testing a large language model (LLM) chatbot in terms of the accuracy of the references it provides could be a powerful, quantifiable means of rating its inherent degree of misinformation, since the accuracy of the bibliographic data can be directly verified. Given the growing reliance on artificial intelligence (AI) tools in academic research and clinical decision-making, such a rating could be extremely useful. MATERIAL AND METHODS In this study, we compared 3 versions of ChatGPT and 3 versions of Gemini by asking them to provide references about 25 highly cited topics in otorhinolaryngology (those with "guidelines" in the title). Answers were sought on 3 consecutive days to assess the variability and consistency of responses. In total, the 6 chatbots returned 1947 references, which were carefully checked against PubMed, Web of Science, and Google Scholar, and rated according to accuracy. Ratings were given based on correct authorship, complete bibliographic details, and proper DOI numbers. RESULTS Common discrepancies noted were wrong author names and erroneous DOI numbers. Across the 6 chatbots, ChatGPT-4.1 (with web search enabled) achieved the best accuracy, with a score of 51%, with Gemini 2.5 Pro being second at 41%. The 2 versions with a web search facility performed better than the 4 versions without. Topics having higher citation counts were associated with lower error rates, suggesting that more widely disseminated scientific findings result in more accurate references. CONCLUSIONS Our findings provide a solid benchmark for rating AI-driven bibliographic retrieval and underline the need for further refinement before these tools can be reliably integrated into academia and clinical applications.

The human body acquires D-amino acids from multiple sources, with intestinal microbiota being the most significant contributor, followed by dietary intake and limited synthesis within the nervous system. Microbiota-derived D-amino acids can either be excreted directly in feces or absorbed into the systemic circulation, thereby influencing host metabolism and immune responses. Dietary intake, particularly from fermented or processed foods, also contributes to circulating levels of D-amino acids. With advances in analytical methodologies such as chiral chromatography and high-resolution mass spectrometry, it is now possible to detect trace concentrations of D-amino acids in plasma, urine, and tissue samples, allowing their clinical relevance to be more fully appreciated. The kidney plays a critical role in regulating systemic D-amino acid balance, as it exhibits stereoselective handling by actively reabsorbing some and excreting others into urine. This selective regulation means that alterations in D-amino acid profiles can provide valuable insight into renal physiology and pathophysiology. Clinical studies have demonstrated that specific D-amino acid patterns, including increased fractional excretion of D-serine, are associated with diseases such as diabetic nephropathy, IgA nephropathy, and chronic kidney disease. Beyond serving as biomarkers, emerging evidence suggests that D-amino acids directly influence renal outcomes: excessive D-serine has been shown to cause tubular injury, while other microbiota-derived D-amino acids may modulate immunity or confer protection in acute kidney injury. This review aims to summarize the sources and metabolism of D-amino acids and their roles as biomarkers in kidney disease.

Ophthalmology is undergoing rapid transformation through the integration of smart technologies such as artificial intelligence (AI), big data analytics, and clinical decision support systems (CDSS). With increasing pressure to improve clinical efficiency and manage growing patient volumes, the potential for smart technologies to streamline ophthalmic care warrants more exploration. To date, smart technologies have demonstrated potential as practical adjunctive tools that support ophthalmic referrals and clinical practice in ophthalmology. Smart technologies that support ophthalmic referrals now include CDSS that contain algorithms with the capacity to more efficiently identify suspected ophthalmic diseases that may be urgent or require prompt treatment in the primary care setting, compared with traditional referral models. These approaches also include installation of AI-powered ophthalmic imaging machines and electronic health records-analytical packages in primary care offices, where they can be used to screen for structural, historical, or symptomatic manifestations of ophthalmic diseases requiring ophthalmologist evaluation. Meanwhile, smart technologies that support ophthalmology practices include AI and big data simulations for optimized patient encounter schedules and chatbot-facilitated appointment confirmations. Amidst a smart technology renaissance, review is needed to capture existing smart technologies to inform integration in the practices of ophthalmic and general practitioners. This article aims to review the clinical utility of emerging smart technology relevant to ophthalmic referrals and ophthalmology practice.

High-altitude pulmonary hypertension (HAPH) is a debilitating condition caused by chronic hypobaric hypoxia at high altitudes, leading to progressive pulmonary vascular remodeling and right heart failure. The altitude-adapted immune microenvironment plays a pivotal but underappreciated role in HAPH progression: innate immune cells mediate early endothelial damage and proinflammatory signaling, while adaptive immune dysregulation sustains chronic inflammation. This review focuses on hypoxic-induced altitude-adapted immune microenvironment alterations - including immune cell phenotypic reprogramming, metabolic shifts, and spatial reorganization - and their roles in driving endothelial dysfunction, smooth muscle cell proliferation, and fibroblast activation. It also explores immune-cell crosstalk with vascular cells via paracrine signaling/extracellular vesicles, and highlights therapeutic strategies. This article aims to review the roles of hypoxia, inflammation, and oxidative stress in vascular remodeling in HAPH.

BACKGROUND Transcatheter tricuspid edge-to-edge repair (T-TEER) has expanded treatment options for tricuspid regurgitation (TR). The procedure was initially performed using systems designed for mitral valve repair, which were replaced by tricuspid-dedicated devices. This retrospective study compared outcomes of tricuspid-dedicated devices (TriClip or PASCAL) with a non-dedicated device (MitraClip) used for TR treatment. MATERIAL AND METHODS We analyzed data from 44 consecutive patients (mean age 74.7±7.5 years) who underwent T-TEER (2018-2024): 22 with non-dedicated (MitraClip) and 22 with dedicated systems (TriClip or PASCAL). The primary endpoint was technical success. Secondary endpoints included post-procedural TR≤ moderate, ≥2-grade TR reduction, peri-procedural complications, and 6-month mortality. RESULTS Baseline characteristics were largely balanced, although the non-dedicated group showed greater left-ventricular dysfunction. Technical success was significantly higher with dedicated systems (100% vs 77.3%, risk ratio 1.29; 95% CI 1.03-1.63; P=0.048) Post-procedural moderate or less TR and a reduction of ≥2 grades was achieved in 68.2% of patients with dedicated devices vs 31.8% with non-dedicated devices (risk ratio 2.14, 95% CI 1.09-4.21; P=0.02). Mortality during a 6-month follow-up was comparable between both cohorts (0.0% vs 9.1%; risk ratio 1.10; 95% CI 0.96-1.26; P=0.23). CONCLUSIONS Tricuspid-dedicated systems (TriClip or PASCAL) were associated with higher procedural success rates and greater TR reduction. They improved early outcomes and enabled the application of T‑TEER in a broader population of patients with less-advanced heart failure. Given the limited sample size and retrospective design, these findings should be interpreted with caution and regarded as exploratory and hypothesis-generating.

BACKGROUND Sepsis-associated acute kidney injury (SA-AKI) exhibits distinct clinical outcomes across age groups, yet current prognostic methods seldom consider age-related pathophysiologic differences. This multicenter study explored age-specific prognostic models for patients with SA-AKI using real-world critical care data. MATERIAL AND METHODS We analyzed 3662 patients with SA-AKI from the MIMIC-IV and eICU databases, stratified into 3 age cohorts: under 65, 65-80, and over 80. For each cohort, we constructed clinical prediction models. Model performance was evaluated using receiver operating characteristic curve analysis, along with sensitivity and specificity at optimal thresholds. RESULTS Age-specific clinical models demonstrated superior predictive performance compared with conventional severity scores. For patients younger than 65 years, the optimal model - incorporating urinary infection, catheter-related infection, lactate, and norepinephrine use - achieved an area under the curve (AUC) of 0.753 (95% confidence intervals [CI], 0.721-0.785) with 67.0% sensitivity and 73.1% specificity. In the 65-80-year cohort, the optimal model - incorporating urinary infection, blood urea nitrogen, lactate, and vasopressor use - achieved an AUC of 0.769 (95% CI, 0.743-0.796) with 78.2% sensitivity. For patients older than 80 years, the optimal model - incorporating urinary infection, catheter-related infection, lactate, vasopressor use, and intensive care unit length of stay - achieved an AUC of 0.770 (95% CI, 0.737-0.803) with 79.7% sensitivity. Survival curves confirmed significant mortality risk stratification across all age groups. CONCLUSIONS Age-specific prognostic models incorporating clinically modifiable factors substantially improved mortality prediction in SA-AKI compared with conventional severity scores. These models facilitate personalized risk assessment and may guide age-tailored treatments for this high-risk population.

BACKGROUND The literature contains no standard management guideline for the treatment of frozen shoulder (FS). Our aim in this study was to increase the shoulder joint range of motion (ROM) by performing passive shoulder manipulation on the affected shoulder under local anesthesia in patients with FS. MATERIAL AND METHODS Thirty-two adult patients who applied to the orthopedics and traumatology clinic between 2019 and 2022 and were diagnosed with FS were included in the study. This was a retrospective study without a control group. Approximately 20 cc (19 cc 2% prilocaine +1 cc 40 mg methylprednisolone acetate) mixture was injected into the affected shoulder of the patients at 6 different points. After an average of 15 minutes, passive manipulation was applied to the affected shoulder in 4 different directions. After manipulation, the patients were kept under observation for a certain period of time to assess for complications. Patients were given home exercise programs. During the treatment process, the patients were called for control at regular intervals and were followed up for 3 months. In this time interval, visual analog scale (VAS), shoulder pain and disability index (SPADI), and ROM values were recorded before and after manipulation and at the last check (LC). RESULTS During the treatment follow-up, ROM (abduction, flexion, extension, external rotation) values increased (P<0.001). A significant improvement was observed in SPADI and VAS values (P<0.001). CONCLUSIONS The manipulation method under local anesthesia is a time-saving, cost-effective treatment that eliminates hospitalization, general analgesia, or sedoanalgesia in the treatment of FS patients.

BACKGROUND Diabetes is increasingly prevalent among older adults; mild cognitive impairment (MCI) comorbidity in this group represents a major concern. Existing MCI prediction methods are often inaccurate, but machine learning (ML) offers improved potential. This study aimed to identify factors associated with MCI through ML analysis of retrospective data from hospitalized older patients with type 2 diabetes mellitus (T2DM). MATERIAL AND METHODS This retrospective study analyzed data from 503 inpatients older than 60 years with T2DM. Patients were classified into MCI (n=102) and normal (n=401) groups based on Mini-Mental State Examination scores. To minimize overfitting and maximize data utilization, 5-fold cross-validation was used for model training and evaluation. Least absolute shrinkage and selection operator regression identified 8 core predictors from clinical data. Logistic regression, eXtreme Gradient Boosting (XGBoost), and random forest algorithms were employed to construct predictive models. Receiver operating characteristic (ROC) curves were used to compare model performance. RESULTS Key predictors of early MCI included age, body mass index, glycated hemoglobin, C-reactive protein, waist-to-height ratio, presence of diabetic complications, diabetes duration exceeding 5 years, and low education level. The XGBoost model outperformed other algorithms in ROC analysis: area under the curve, 0.892±0.032; accuracy, 0.851±0.028; sensitivity, 0.843±0.031; specificity, 0.859±0.029; and F1 score, 0.834±0.033. CONCLUSIONS The XGBoost model, incorporating these identified factors, demonstrated optimal predictive performance for MCI in older patients with T2DM. It may aid clinical risk stratification and provide a quantitative foundation for early intervention.