MedScience. 2026 May 30. doi: 10.1007/s11684-026-1253-8. Online ahead of print.
NO ABSTRACT
PMID:42223858 | DOI:10.1007/s11684-026-1253-8
Tag: pubmed
J Robot Surg. 2026 Jun 1;20(1):558. doi: 10.1007/s11701-026-03542-y.
ABSTRACT
INTRODUCTION: Imageless navigation is widely used in total hip arthroplasty (THA), yet evidence for procedures in the lateral decubitus position remains limited because pelvic orientation and registration differ from the supine position. This study evaluated the accuracy of imageless navigation for acetabular component positioning in lateral-position primary THA, using postoperative EOS-based 3D assessment as a postoperative reference method for agreement analysis.
METHODS: The study comprised in-vitro pretests and an in-vivo cohort. In vitro, a pelvic model was systematically rotated along all axes to assess effects on navigated cup inclination and anteversion. In vivo, 70 patients undergoing primary THA in lateral decubitus were included. Intraoperative imageless navigation values were compared with postoperative EOS-3D-measurements.
RESULTS: In vitro, z-axis (tilt) variations substantially altered both parameters. In vivo, inclination showed a small but statistically significant inter-method difference (mean 1.4°, p = 0.036, Cohen’s d = 0.26), whereas anteversion demonstrated a larger systematic underestimation by imageless navigation (mean -7.5°, p < 0.001, Cohen’s d = -0.78) with poor inter-method agreement (ICC = 0.168).
CONCLUSION: Imageless navigation demonstrated acceptable inclination agreement with postoperative EOS assessment, whereas anteversion showed a larger systematic deviation and poor inter-method agreement; sagittal pelvic tilt and positional frame-of-reference differences appear to be major contributing factors.
CLINICAL TRIAL REGISTRATION: The study was registered in the German Clinical Trials Register with the registration number DRKS00026749.
PMID:42223835 | DOI:10.1007/s11701-026-03542-y
J Robot Surg. 2026 Jun 1;20(1):559. doi: 10.1007/s11701-026-03530-2.
ABSTRACT
This study aimed to evaluate and compare perioperative outcomes of robotic versus laparoscopic cholecystectomy in patients with acute cholecystitis within an Acute Care Surgery setting. A retrospective cohort study was conducted including patients who underwent cholecystectomy for acute cholecystitis between January 1, 2023, and March 1, 2026. Patients were stratified by operative approach (robotic vs. laparoscopic). Baseline demographics, comorbidities, and postoperative outcomes were analyzed. Continuous variables were compared using Mann-Whitney U test, and categorical variables were assessed with Pearson χ². Statistical significance was defined as p < 0.05. A total of 322 patients were included, with 107 undergoing robotic and 215 laparoscopic cholecystectomies. Baseline characteristics were similar between groups. Median operative time did not differ significantly (78.0 [63.0-107.0] minutes robotic vs. 77.0 [60.0-95.0] minutes laparoscopic, p = 0.31). Conversion to open surgery occurred in one laparoscopic case (0.5%) and none in the robotic group. Length of hospital stay was comparable. Early postoperative complications were similar (10.3% robotic vs. 12.6% laparoscopic, p = 0.97), including comparable rates of severe (Clavien-Dindo III-IV) complications. Readmission and reintervention rates did not differ significantly. Two postoperative bile leaks occurred, one in each group. Subgroup analysis of gangrenous cholecystitis showed no significant differences in operative time, length of stay, or postoperative outcomes. Robotic cholecystectomy demonstrates comparable safety and efficacy to laparoscopic cholecystectomy for acute cholecystitis, including in severe cases, supporting its feasibility in acute care settings.
PMID:42223833 | DOI:10.1007/s11701-026-03530-2
Sports Med. 2026 Jun 1. doi: 10.1007/s40279-026-02461-0. Online ahead of print.
NO ABSTRACT
PMID:42223829 | DOI:10.1007/s40279-026-02461-0
J Robot Surg. 2026 Jun 1;20(1):566. doi: 10.1007/s11701-026-03524-0.
ABSTRACT
Robotic surgery is increasingly adopted across surgical specialties because of advantages in visualisation, dexterity, and ergonomics. However, data on its use in upper gastrointestinal (UGI) and hepatopancreatobiliary (HPB) surgery in Australia remain limited. This study characterises trends in robotic UGI and HPB surgery in Australia using data from the da Vinci System and Medicare Benefits Schedule (MBS) Item Reports website. Robotic procedure counts between 2013 and 2023 were obtained from Device Technologies Australia, local distributor of the da Vinci System by Intuitive Surgical in Australia. Corresponding MBS item numbers were used to determine UGI and HPB procedure volumes. Descriptive statistics, Poisson regression, and linear models were used to analyse trends over time. Robotic UGI and HPB surgery volume increased on average by 33% annually (95% CI 31%, 35%) over the study period, adjusted for surgery type. The proportion of MBS-claimed UGI and HPB procedures performed robotically increased on average by 0.16% (95% CI 0.12%, 0.20%) and 0.11% (95% CI 0.07%, 0.15%) per year, respectively. Cholecystectomy was the most common HPB procedure performed robotically but accounted for only 0.6% of MBS-claimed procedures. A considerable proportion of MBS-claimed left-sided pancreatectomies (34.4%) and pancreatoduodenectomies (18.1%) were performed robotically in 2023. Robotic bariatric procedures were the most commonly performed UGI procedure, although procedure counts plateaued after 2022. Robotic surgery for UGI and HPB procedures increased significantly over the study period. Notably, there has been a recent rise in the adoption of robotic approaches for complex non-bariatric procedures, particularly pancreatic surgery.
PMID:42223813 | DOI:10.1007/s11701-026-03524-0
Int Urol Nephrol. 2026 Jun 1. doi: 10.1007/s11255-026-05220-2. Online ahead of print.
ABSTRACT
PURPOSE: Behavioral therapy is the established first-line treatment for overactive bladder (OAB), followed by pharmacotherapy as the second-line intervention. Mirabegron, a β3-adrenoceptor agonist, has demonstrated comparable efficacy to antimuscarinics. This study aims to evaluate the real-world efficacy of behavioral therapy, both as a monotherapy and in combination with mirabegron, for male patients with OAB.
METHODS: This pooled analysis from three studies involved 280 adult male OAB patients assigned to behavioral therapy alone or combined with mirabegron (25 mg or 50 mg) for 12 weeks. The primary outcome was the change in the Overactive Bladder Symptom Score (OABSS) from baseline to week 12. Secondary outcomes included changes in the International Prostate Symptom Score (IPSS), Patient Perception of Bladder Condition (PPBC), Quality of Life (QOL) score, maximum flow rate (Qmax), and post-void residual (PVR) volume.
RESULTS: At week 12, all groups exhibited significant within-group improvements in total OABSS, with no statistically significant inter-group differences. Significant improvements were also observed in the IPSS, QOL, PPBC, urge urinary incontinence (UUI), and nocturia across all groups. Notably, behavioral therapy demonstrated substantial therapeutic potential for storage symptoms (IPSS storage sub-score), particularly regarding UUI and nocturia. No negative impacts on PVR or Qmax were observed across the three treatment arms at week 12.
CONCLUSION: In real-world clinical practice, both behavioral therapy and combination therapy with varied dosages of mirabegron effectively alleviate OAB symptoms in male patients without compromising voiding function. Beyond conventional pharmacotherapy, optimizing the role of behavioral therapy remains a fundamental component of comprehensive OAB management.
PMID:42223809 | DOI:10.1007/s11255-026-05220-2
Nat Prod Bioprospect. 2026 Jun 1;16(1):67. doi: 10.1007/s13659-026-00614-2.
ABSTRACT
Medicinal plants have long served as an important asset in the treatment of diseases. Recent developments in computer science have enabled the rise of specialized databases cataloging medicinal plant knowledge. However, a systematic comparison of available region-specific medicinal plant databases is lacking. This review summarizes globally available medicinal plant databases that focus on specific geographical regions, aiming to inspire and guide people from specialists to the general public toward fostering innovation and making informed decisions. Through a systematic search of literature and digital resources, 81 regional medicinal plant databases established or updated between 2013 and 2025 were identified. From this pool, 40 core platforms were subjected to detailed statistical characterization regarding their data categories and volume. Our analysis reveals a geographical concentration in Asia (48.1%), dominated by China and India, alongside a notable proliferation of universal databases with a global scope. These databases facilitate critical applications in drug discovery, quality control, biodiversity conservation, and policy-making. By identifying current research gaps and emphasizing the need for interdisciplinary standardization, this review serves as a strategic roadmap for bridging traditional wisdom with modern therapeutic innovation.
PMID:42223802 | DOI:10.1007/s13659-026-00614-2
Front Med (Lausanne). 2026 May 14;13:1834636. doi: 10.3389/fmed.2026.1834636. eCollection 2026.
ABSTRACT
OBJECTIVES: Artificial intelligence (AI)-enhanced virtual patient simulations are increasingly used in health professions education to improve clinical communication and diagnostic reasoning. However, the effectiveness of these technologies for psychiatric interview training has not been systematically quantified. This study aimed to systematically review and meta-analyze the existing literature evaluating the impact of AI-enhanced virtual patients on psychiatric interview performance, knowledge acquisition, and learner confidence in health professions education.
MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following the PRISMA 2020 guidelines. Electronic database searches were performed in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar to identify relevant studies published between January 2000 and March 2026. Studies were included if they evaluated AI-enhanced virtual patient simulations for psychiatric interview training among medical students, psychiatry residents, clinicians, or other health professions trainees. Data extraction included study characteristics, participant populations, intervention types, and educational outcomes. Risk of bias was assessed using the Cochrane Risk of Bias Tool. Quantitative synthesis was performed using random-effects meta-analysis models, and effect sizes were calculated as standardized mean differences (SMD) with 95% confidence intervals (CI) using R statistical software.
RESULTS: A total of 560 records were identified through database searches and additional sources. After removal of duplicates and screening procedures, 10 studies met the inclusion criteria and were included in the final analysis. The studies involved approximately 450 participants, including medical students, psychiatry residents, clinicians, nursing students, and psychology trainees. AI-enhanced virtual patient interventions included conversational AI systems, virtual human simulations, large language model-based simulated patients, and AI-virtual reality training environments. The pooled analyses indicated improvements in psychiatric interview performance, knowledge acquisition, and learner confidence following AI-supported virtual patient training. Subgroup analysis demonstrated positive educational outcomes across both student and clinician populations. Risk-of-bias assessment revealed variable methodological quality across studies, with several pilot and non-randomized designs.
CONCLUSION: AI-enhanced virtual patient simulations appear to be effective educational tools for improving psychiatric interview training in health professions education. These technologies provide scalable and standardized simulation environments that support communication skill development, diagnostic reasoning, and learner confidence. Although the findings suggest promising educational benefits, further large-scale randomized controlled trials and standardized outcome assessments are needed to confirm the long-term educational impact of AI-supported virtual patient training in psychiatry.
PMID:42221128 | PMC:PMC13215799 | DOI:10.3389/fmed.2026.1834636
Front Med (Lausanne). 2026 May 14;13:1830071. doi: 10.3389/fmed.2026.1830071. eCollection 2026.
ABSTRACT
BACKGROUND: Type 2 diabetes mellitus (T2DM) is increasing rapidly in low- and middle-income countries, including Lao People’s Democratic Republic (Lao PDR). Although behavioral self-management is widely considered essential in diabetes care, evidence linking psychosocial determinants to glycemic outcomes among older adults remains inconsistent.
OBJECTIVE: This study examined the associations between diabetes-related knowledge, attitudes, self-care behaviors, and glycemic control among older adults with T2DM receiving tertiary hospital care in Lao PDR.
METHODS: A cross-sectional study was conducted among 88 adults aged ≥60 years with diagnosed T2DM attending the outpatient diabetes clinic at Setthathirath Hospital in Vientiane Capital. Structured interviews were used to assess diabetes knowledge, attitudes, and self-care practices. Glycemic control was defined as HbA1c < 7%. Pearson correlation and multivariable regression analyses were performed to examine associations between psychosocial factors and glycemic outcomes.
RESULTS: A total of 19.3% of participants achieved glycemic control (HbA1c < 7%), with a mean HbA1c level of 9.03 ± 2.47%, indicating generally poor glycemic control. Diabetes knowledge levels were low, with 98.9% of participants classified as having low knowledge. Attitudes toward diabetes management were predominantly low (60.2%), while overall self-care behaviors were largely moderate (83.0%). Pearson correlation analysis showed no statistically significant associations between knowledge (r = -0.134, p = 0.213), attitudes (r = 0.108, p = 0.318), or self-care behaviors (r = 0.046, p = 0.671) and HbA1c levels. Multivariable regression analysis likewise identified no significant predictors of glycemic control.
CONCLUSION: Despite substantial psychosocial vulnerabilities, no statistically significant associations between psychosocial factors and glycemic control were observed in this sample. These findings may indicate a potential mismatch between psychosocial factors and glycemic outcomes; however, this interpretation should be approached with caution, given the study’s methodological limitations. Further research with larger samples and longitudinal designs is needed to better understand these relationships. This study contributes context-specific evidence from Lao PDR to the limited literature on psychosocial determinants of diabetes management in low- and middle-income countries.
PMID:42221121 | PMC:PMC13215864 | DOI:10.3389/fmed.2026.1830071
Front Med (Lausanne). 2026 May 14;13:1804544. doi: 10.3389/fmed.2026.1804544. eCollection 2026.
ABSTRACT
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by persistent respiratory symptoms and progressive airflow limitation. Acute exacerbations of COPD (AECOPD) are significant causes of hospitalization and death among COPD patients. This study aims to identify risk factors for AECOPD exacerbations and develop a highly accurate and interpretable predictive model using various statistical and machine learning methods.
METHODS: We retrospectively analyzed data from 2,102 COPD patients admitted between 1 January 2019 and 31 December 2024. The primary outcome was AECOPD severity, defined as the need for treatment escalation. Initial feature selection was performed using LASSO regression to identify potential risk factors. To validate the model’s effectiveness and explore its superior predictive performance, the dataset was partitioned by time period and proportion: The first 70% of observations in chronological order were used as the training set, with the remaining 30% as the test set. Multiple machine learning algorithms were then employed for model construction and comparison. To enhance model interpretability, we utilized SHapley Additive exPlanations (SHAP) to illustrate the contribution of each variable to the prediction outcomes.
RESULTS: Among the six machine learning models, the extreme gradient boosting (XGBoost) model demonstrated the optimal predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.960 (95% confidence interval (CI): 0.940-0.980) in the training set and 0.824 (95% CI: 0.804-0.844) in the test set. In the test set, the evaluation metrics were as follows: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.805, 0.65, 0.872, 0.669, and 0.859, respectively. SHAP analysis revealed that creatinine (CREA), neutrophil percentage (NEU%), D-dimer, brain natriuretic peptide (BNP), white blood cell count (WBC), and hypertension (HTN) were important factors influencing the model output.
CONCLUSION: The XGBoost model developed in this study demonstrates robust performance in predicting AECOPD risk using routinely collected clinical and laboratory data. The integration of SHAP analysis enhances model transparency, supporting its potential utility in clinical risk stratification and early intervention.
PMID:42221119 | PMC:PMC13216505 | DOI:10.3389/fmed.2026.1804544