Frontiers in Medicine

Introduction: Infectious and wound-healing complications after colorectal surgery often increase the complexity of local care and the need for specialized enterostomal therapy follow-up after hospital discharge. Despite the growing use of predictive models in digestive surgery, a translational gap remains between perioperative prediction and the practical organization of specialized care. Therefore, the aim of this study was to develop and temporally validate a machine-learning-based risk stratification model to estimate the probability of post-discharge outcomes associated with greater demand for enterostomal therapy after colorectal surgery. Methods: This was a retrospective observational study including 7,908 patients who underwent colorectal surgery between 2005 and 2014. The outcome was defined as the occurrence of superficial surgical site infection, delayed wound healing, or abdominal sinus formation. Routinely available preoperative and intraoperative variables were used as predictors. The primary model was based on gradient boosting with isotonic calibration. Temporal validation was performed by separating cohorts according to year of surgery. Performance was assessed using ROC-AUC, PR-AUC, Brier score, calibration, and decision-oriented clinical metrics. Clinical utility was examined through percentile-based risk stratification and Decision Curve Analysis (DCA). Results: The outcome prevalence in the test set was 6.6%. The calibrated model achieved a ROC-AUC of 0.64 and a PR-AUC of 0.11, with a Brier score of 0.061. The Top-10% risk stratum concentrated approximately twice the baseline event rate ({approx}14% vs. 6.6%), with a number needed for intensified follow-up of 7 patients to identify one event. Decision curve analysis showed greater net benefit than strategies of following all or no patients, particularly for threshold probabilities between 3% and 13%. Models based exclusively on preoperative or intraoperative variables performed worse than the combined model. Conclusion: STOMAPY demonstrated the ability to organize patients along a continuous gradient of risk for post-discharge outcomes associated with greater demand for enterostomal therapy. Although discriminatory performance was moderate, the adequate calibration, temporal validation, and net benefit observed across clinically plausible thresholds support its usefulness as a tool for proportional care prioritization rather than as an individual diagnostic test. Prospective studies and external validations are needed to confirm direct clinical impact.

Accurate risk stratification of pigmented skin lesions is critical for early melanoma detection and for reducing unnecessary excisions. Artificial intelligence (AI) is increasingly applied to dermoscopic image analysis, but its diagnostic performance relative to standard dermoscopy in real-world clinical settings remains uncertain. To address this gap, we conducted a systematic review and meta-analysis of prospective clinical studies directly comparing AI alone, dermoscopy, and AI-assisted clinicians for malignancy risk assessment of pigmented skin lesions. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library from inception to January 2026. Ten studies with 17 diagnostic arms (10 dermoscopy arms, 6 AI-alone arms, and 1 AI-assisted clinician arm) were included. Pooled sensitivity and specificity were 0.773 (95% CI, 0.648-0.863) and 0.793 (95% CI, 0.673-0.877) for dermoscopy, and 0.757 (95% CI, 0.428-0.928) and 0.859 (95% CI, 0.619-0.958) for standalone AI. Summary ROC curves showed overlapping performance, indicating that autonomous AI is broadly comparable to dermoscopy but does not demonstrate a consistent advantage. Heterogeneity in AI performance was driven almost entirely by threshold effects rather than by differences in inherent model capacity. AI-assisted clinicians showed promising results (sensitivity 1.000, specificity 0.837) in a single study, but more evidence is needed. Our findings suggest that, at present, AI should be viewed as a complementary decision-support tool rather than a replacement for dermoscopic evaluation. The study provides valuable evidence for clinicians, guideline developers, and researchers working on AI integration into melanoma diagnostic pathways.

This study mapped the age- and region-specific risks of eye diseases in the Brazilian population, evaluating temporal trends and geographical inequalities in access to healthcare. Secondary data from DATASUS, covering the 27 Brazilian federative units from 2010 to 2024, were used, employing hierarchical negative binomial regression. A significant national increase in hospital admission rates was observed during the studied period, with increases of 160.8% for retinopathy, 126.4% for eye and appendage diseases, and 122.8% for glaucoma. State-level heterogeneity was extreme, with variations spanning from -93.1% to +3588% for glaucoma, for example. Even so, regional disparities were observed throughout the period; the South region reported an average 43.2% higher than the national average for retinopathies, and the Southeast 28.5% higher for eye and adnexal diseases, while the North region reported the lowest rates. Projections up to 2036 predict a further national increase of up to +377.0% for retinopathies, with interventions covering more than an order of magnitude. In addition to the temporal projection, rates in state, age, and year components on a logarithmic scale with calibrated uncertainty were verified. Out-of-sample tests show that the chosen modeling outperforms the last observed value maintenance method and naive linear extrapolation in all three diseases considered. Thus, the escalating, age-driven burden of ophthalmological diseases and profound geographic disparities highlight an urgent need to decentralize specialized care and target resource allocation within the public health system.

Background: Multi-drug resistant Bacterial (MDRB) Infections in the intensive care units (ICUs) substantially elevate patient mortality, prolong hospital stays, and impose heavy healthcare cost burdens. Existing predictive models for ICU-acquired MDRB infection predominantly focus on static admission-risk assessment, lacking the capacity to leverage longitudinal treatment data for dynamic risk re-stratification during the ICU stay. Meanwhile, most models suffer from poor clinical interpretability, overreliance on hard-to-collect biomarkers, or absence of deployable clinical tools, limiting real-world translation. Therefore, there is an urgent need to develop a parsimonious, interpretable tool based on routine cumulative data to guide timely intervention. This study aimed to develop a interpretable model with a web calculator to improve clinical applicability. Methods: In this study, we conducted a retrospective analysis of ICU inpatients at the First Affiliated Hospital of Dali University between January 1, 2023, and January 1, 2026. Using the create Data Partition function in R software (random seed = 42), the dataset was stratified and divided into a training group and a validation group in a 7:3 ratio. Feature selection was performed using the Boruta algorithm to validate variable rationality. A multivariable logistic regression model was constructed and visualized as a nomogram, and its performance was compared with six machine learning algorithms (Random Forest, XG Boost, Neural Network, etc.). Model validation was conducted using receiver operating characteristic curves (ROC), Decision Curve Analysis (DCA), and SHAP value interpretation. Finally, an online R Shiny calculator was developed based on the final model. Results: A total of 3,631 patients were enrolled and divided into a training group (n=2,543) and a validation group (n=1,088) using stratified random sampling. Five independent predictors were identified in the training group, which were hypertension combined with diabetes, antibiotic types, ventilator days, urinary catheter days, and PCT abnormality times. The Logistic regression model achieved an AUC of 0.772 (95%CI: 0.733-0.812) in the validation group, outperforming XG Boost (0.763) and Random Forest (0.703). The model demonstrated excellent calibration (Hosmer-Leme show {chi}{superscript 2} = 1.94, P = 0.9829) and positive net clinical benefit across threshold probabilities of 0%-40%. SHAP analysis aligned with regression-derived variable importance rankings, confirming predictor contributions. An open-access online calculator was successfully deployed (https://dongfangshao666.shinyapps.io/MDR_shiny2/), enabling real-time individualized risk stratification at the bedside. Conclusion: This study developed and validated a dynamic, interpretable multi-drug-resistant bacterial infection risk prediction model requiring only five routinely collected clinical indicators. The model balances robust predictive performance with high transparency, overcoming key limitations of prior tools. The accompanying web calculator supports dynamic risk reassessment throughout the ICU stay, facilitating precise antimicrobial stewardship, targeted infection control interventions, and optimized resource allocation, bridging the gap between statistical modeling and frontline clinical decision-making.

Background: Microbiota dysbiosis of the skin has been implicated in ulcer formation. Individuals with diabetes remain at high risk for diabetic foot ulcers (DFUs) even after ulcer healing. Topical chlorhexidine gluconate (CHG) is a broad-spectrum antiseptic commonly used to reduce microbial burden. In a prior randomized clinical trial comparing daily CHG foot treatment with soap-and-water treatment, no statistically significant reduction in new DFUs was observed, prompting evaluation of whether CHG produced durable changes in the skin microbiota. Objective: To compare changes in foot skin microbiota (including bacterial bioburden, diversity, and community composition) associated with daily CHG versus soap-and-water use over one year in people with diabetes and prior foot complications. Methods: In a single-center, double-blind, placebo-controlled randomized trial, 87 participants were randomized to daily CHG wipes or soap-and-water wipes for 12 months. Foot swabs were collected at baseline, 3 and 12 months, and 4 weeks post-treatment. Bacterial bioburden was quantified. Microbiota composition was assessed using 16S rRNA and ITS amplicon sequencing. Key Results: CHG treatment significantly reduced bacterial bioburden, increased microbial diversity, and altered community composition, including sustained reductions in Staphylococcus abundance. Several microbiota changes persisted more than 4 weeks after treatment cessation. Soap-and-water treatment showed similar but smaller and largely nonsignificant trends. Conclusions: Daily CHG use durably modifies foot skin microbiota in high-risk individuals with diabetes. However, this alone may be insufficient to prevent new foot complications, highlighting the need for additional interventions. These findings have implications for long-term CHG use in populations at risk for staphylococcal infections.

BACKGROUND & AIMS Conventional reusable endoscopes incur significant expenses in the form of purchase, maintenance, reprocessing, and disinfection. Reprocessing is frequently ineffective even following the use of high-level disinfectants (HLDs). Disposable gastroscopy might be a strategy to decrease infectious outbreaks associated with reusable endoscope. The aim of this study was to analyze and evaluate the performance, efficiency and safety in gastroscopy observation and subsequent potential EMR procedure via the disposable gastroscope in a clinical setting. METHODS Patients who required gastroscopies and met the criteria were recruited to this prospective, open-label, non-inferiority study. After obtaining the written informed content, the enrolled subjects selected themselves independently to the disposable group or reusable group. The primary measure was to evaluate the acceptable image quality and whether the disposable endoscope devices could meet the basic clinical demands with a noninferiority margin of -8%. The second measures were to analyze and evaluate the image conditions, accepted endoscopic maneuverability, efficiency and safety of observation and advanced potential EMR procedure. Appropriate statistical methods were conducted via PASS software and SAS 9.4. A two-tailed P value < 0.05 was considered statistically significant. RESULTS A total of 90 individuals (the number of those in disposable group and reusable group was both 45) were recruited to this study. The success rate of acceptable image quality via photographing iconic anatomical sites between two groups was 100.0% (45/45, 95% confidence interval (CI): 0.9213,1.0000) and the lower limit of the 95%CI (-7.8654%, 7.8654%) was larger than the noninferiority margin of -8% (Newcombe-Wilson score method). Significant differences were showed in the measures of image conditions (image acquisition, image quality, brightness, contrast and sharpness) and accepted endoscopic maneuverability (endoscopy body rigidity). No significant differences were observed in the field of knob operation, sharp angle adaptability, and the auxiliary features including air supply, water supply and suction. In terms of efficiency, the total operating time, insertion time and withdrawal time were longer in the disposable group. The En-bloc resection rate of those observed polyps and required to EMR procedure due to relatively larger diameter (5mm-15mm) was the same 100% in both groups (26/26 vs 23/23, 95%CI: 0.8713,1.0000). Nevertheless, the procedure time of EMR for each polyp was significantly longer in the disposable group. This study showed no intraoperative bleeding, delayed bleeding, perforation or other study-related adverse events among 90 patients. No dramatic fluctuations in vital signs were showed in perioperative period. CONCLUSIONS In consideration of the efficiency, efficacy and safety evaluation, the disposable gastroscopes might represent an alternative to conventional reusable gastroscopes in routine examination and endoscopic mucosal resection.

Introduction Aquablation for surgical treatment of benign prostatic enlargement (BPE) causing bladder outflow obstruction (BOO) has demonstrated good functional outcomes, even for large glands, with high rates of ejaculatory preservation reported. This is a protocol for a study that aims to review real-world outcomes of ejaculatory preservation or restoration post-Aquablation in an unselected cohort and compare to published clinical trial outcomes. Methods Retrospective data will be collected from a prospectively maintained consecutive case series of patients who underwent Aquablation, in a single UK centre. The primary outcome is ejaculatory function subjectively reported by men post-operatively, and classified as: antegrade ejaculation, retrograde/low volume ejaculation, anejaculation or not sexually active. Secondary outcomes are International Prostate Symptom Severity (IPSS), Quality of Life (QoL) Score, post-void residual (PVR), and incontinence. Descriptive and comparative statistical tests will be performed. Conclusions This study will review real-world ejaculatory function and clinical outcomes following robotic Aquablation for prostatic bladder outflow obstruction and compare this to published clinical trial outcomes.

Robot-assisted hematoma puncture has seen significant development in primary hospitals across the country. Sino Plan software system is the core of the intelligent surgical robot, independently developed by Sinovation.We conducted a comparative study of imaging indicators, such as residual hematoma volume and hematoma clearance rate, as well as prognostic indicators, in patients who underwent hematoma puncture at our hospital over a 9-year period, before and after the introduction of Sino Plan.The results indicated that following the application of Sino Plan, the hematoma clearance rate was significantly enhanced, and the residual hematoma volume was markedly reduced. Regarding patient prognosis, there was no significant difference in GCS scores between the two groups, but the incidence of adverse prognostic events was lower in patients where Sino Plan was utilized.In conclusion, this 9-year retrospective analysis at our hospital reveals that Sino Plan offers distinct advantages. However, its application in certain special cases suggests that further improvements to the software are warranted to better meet the demands of more specific clinical scenarios.

Abstract: Background: More than half of metastatic melanomas arise from patients initially diagnosed with early-stage melanoma. Objective biomarkers are needed to better identify high-risk patients. Objective: To evaluate the prognostic value of multiple histopathological characteristics in predicting distant metastasis risk, in early-stage melanoma. Methods: Using data from discovery set (n=442) and a population-based validation cohort (n=306, sampled from 5,815 patients) of the Dutch Early-Stage Melanoma (D-ESMEL) study, we investigated 14 histopathological characteristics of melanoma and their tumor micro-environment (TME) in an unprecedented integration, by expert pathologist scoring and automated quantitative measurements derived from a validated automated segmentation. Results: Increased immune infiltrates (40% in cases vs. 50% in controls) were associated with lower risk of metastasis. Automated immune cell density was predictive in both the discovery set and the validation cohort, outperforming the manual pathological tumor infiltrating lymphocytes. The remaining histopathological features, including mitotic activity, did not retain independent value after controlling for current staging variables. Limitations: TME evaluation in standard Hematoxylin-Eosin slides. Conclusion: TME reaction is an important determinant of melanoma progression. The automated quantification of immune cell density appears to be a biomarker for distant metastasis risk. Further investigation into specific immune cell subtypes is required to facilitate clinical integration.

ObjectiveRecurrent urinary tract infections (rUTIs) significantly decrease quality of life and antibiotics are becoming increasingly less effective due to antimicrobial resistance. Alternative effective treatment strategies are urgently needed for rUTIs. Prior studies have indicated that women can experience resolved or improved rUTI following electrofulguration (EF). To further investigate these findings, we report on the design and methodology behind a randomized trial examining two treatment arms: standard prolonged antibiotic treatment with nitrofurantoin (NF) alone or in combination with EF. Patients and MethodsThe aim of this randomized trial is to determine, at two institutions, the efficacy of two interventions for rUTI associated with early stages of chronic cystitis (stages 1 and 2): conventional 6 months low-dose (100mg) NF daily antibiotic suppression alone (NF) or conventional NF with EF (EF + NF). The study is also designed to analyze changes in the urinary microbiomes in the two different treatment arms and to determine the durability of clinical outcomes in both treatment arms at 2 years after the end of each intervention. The primary outcomes will be obtained from 6 to 18 months, as well as 18 - 30 months following completion of the original 6-month intervention. Failure is defined based on UTI symptoms documented by a validated questionnaire with a documented urine culture confirming a bacterial strain at each UTI episode following the end of the 6-month intervention. ConclusionsThis randomized trial is designed to examine the efficacy and durability of treating women with rUTIs using the standard of care of NF alone, or an EF procedure with NF.

Abstract Background: Mean platelet volume (MPV) is a simple, low-cost biomarker that reflects platelet activation. Its prognostic value in septic shock remains controversial. We aimed to determine whether MPV at intensive care unit (ICU) admission is associated with hospital mortality in patients with septic shock. Methods: Retrospective cohort study of consecutive adults with septic shock (Sepsis-3 criteria) admitted to a single ICU. MPV, severity scores (SOFA, APACHE II, SAPS II), procalcitonin, and clinical data were collected. The primary outcome was in-hospital mortality. Spearman correlation, univariate and multivariate logistic regression (with Firth's correction), ROC curves, and subgroup analyses were performed. Results: Fifty-eight patients were included; mortality was 58.6%. MPV did not differ between non-survivors and survivors (13.09 {+/-} 1.37 vs. 12.66 {+/-} 1.45 fL, p = 0.259). MPV showed a weak correlation with procalcitonin ({rho} = 0.394, p = 0.002) but not with severity scores. In multivariate analysis adjusting for age, sex, SOFA and comorbidity count, MPV was not an independent predictor of mortality (OR 1.075, 95% CI 0.682-1.755, p = 0.749). The area under the ROC curve for MPV was 0.598 (95% CI 0.444-0.752), significantly lower than that of SOFA (0.837) and procalcitonin (0.836). Subgroup analyses showed no significant association between MPV and mortality in any stratum. Conclusions: In this cohort of septic shock patients, MPV at ICU admission was not associated with hospital mortality and had poor discriminative ability. Widely used severity scores and procalcitonin remain superior prognostic markers. MPV should not be used as a prognostic tool in septic shock. Keywords: Septic shock, Mean platelet volume, Mortality, SOFA, Procalcitonin, Biomarker

BACKGROUND Erythema nodosum leprosum (ENL) is a severe inflammatory complication of leprosy associated with disability, morbidity and mortality. Impairment of health-related quality of life (HRQoL) in ENL has been reported using the Dermatology Life Quality Index (DLQI) and the 36-Item Short Form Health Survey (SF-36), the latter validated in people affected by leprosy. Understanding the correlation between these measures is important to determine whether the shorter dermatology-specific DLQI provides a valid and practical measure of HRQoL in ENL. OBJECTIVES To examine the relationship between DLQI and SF-36 scores in individuals with ENL using data from the Methotrexate and Prednisolone study in ENL (MaPs in ENL). METHODS A post-hoc analysis of prospectively collected HRQoL data from the trial sites in India, Indonesia, and Nepal of the MaPs in ENL multicentre randomised clinical trial was performed. HRQoL was assessed using the DLQI and SF-36 at enrolment and at weeks 24, 48 and 60. Associations between DLQI and SF-36 physical (PCS) and mental (MCS) component summary scores were evaluated using correlation analyses and multivariable linear regression at enrolment, and linear mixed-effects models during follow-up adjusted for age, sex, recruiting centre and enrolment SF-36 scores. RESULTS A total of 383 paired HRQoL assessments from 129 participants were analysed. At enrolment, HRQoL impairment was substantial (median DLQI 19, IQR 15-21; mean PCS 30.3 + - 7.3; mean MCS 33.3 + - 8.4). DLQI scores improved markedly during follow-up. Across all timepoints, DLQI was strongly inversely correlated with PCS and MCS (both p<0.001). In adjusted analyses, higher DLQI scores were consistently associated with lower PCS and MCS. At enrolment, each 1-point increase in DLQI was associated with a 0.66-point reduction in PCS and a 0.51-point reduction in MCS (both p<0.001). These associations remained strong during follow-up, with no evidence that they varied over time. CONCLUSIONS DLQI scores were strongly and consistently associated with SF-36 physical and mental health scores. These findings support the use of the DLQI as a practical patient reported outcome measure to assess the HRQoL associated with ENL and its change following treatment.

Abstract: Objective This study aimed to systematically screen for potential candidate biomarkers and identify therapeutic targets associated with gouty arthritis (GA) through integrated analyses of single-cell and bulk RNA sequencing (RNA-seq) data. Methods The single-cell dataset GSE211783 and the bulk RNA-seq dataset GSE160170 were analyzed using a series of bioinformatic approaches, including cell clustering, differential expression analysis, immune cell infiltration assessment, protein-protein interaction network construction, gene set enrichment analysis, as well as drug sensitivity evaluation. To establish an animal model of GA, monosodium urate crystals were injected intra-articularly into experimental mice. Joint swelling was evaluated, and morphological changes in joint tissues were analyzed through hematoxylin-eosin staining. The presence of TREM1-positive cells was detected by immunohistochemistry and the level of TREM1 protein expression in joint tissues were assessed by Western blotting. Results We identified 102 differentially expressed genes (DEGs) and 14 signaling pathways associated with GA. The PPI network revealed 25 hub genes, of which 17 (including TREM1, TNF, PTGS2, and NLRP3) were highly expressed and 8 (including FCGR3B and CXCR6) showed low expression in the GA samples. These genes correlated significantly with the infiltration levels of macrophages. Among the hub genes, TREM1 was selected for further validation because it correlated significantly with all 14 differential pathways. In animal experiments, GA mice developed marked joint swelling and inflammatory tissue injury, along with a significant increase in TREM1-positive cells and TREM1 protein expression. Conclusion Integrative analysis of single-cell and bulk RNA-seq data identified 102 GA-related DEGs and 14 key pathways, from which 25 hub genes were screened. TREM1 is significantly upregulated in GA and may be linked to macrophage function, providing new insights into biomarker and therapeutic target discovery for GA.

Background: Accurate early identification of sepsis remains a major clinical challenge due to its heterogeneous presentation and overlap of clinical signs with the non-infectious systemic inflammatory response syndrome (SIRS). Timely differentiation is crucial for improving patient outcomes, meeting sepsis bundle requirements and reducing inappropriate antimicrobial use. We hypothesized that clinical and laboratory data available within the first 3 hours of patient presentation could be used to identify patients with sepsis to an actionable level of accuracy, in lieu of traditional microbiology results which would not become available until at least 12-24 hours. Data from two independent studies were used to quantify the diagnostic value of demographic, vital, clinical-laboratory, and microbiological data available at three time points for distinguishing retrospectively diagnosed critically ill patients with either sepsis or non-infectious SIRS. A particular focus of this work was an assessment of the utility of SeptiCyte RAPID (Immunexpress Inc., Seattle, Washington, USA) as an aid to sepsis diagnosis, producing actionable data within 1 hour. Methods: Data from two independent study cohorts were analysed. The 510k cohort consisted of 419 adult patients in intensive care (ICU) (MARS, VENUS, and NEPTUNE trials). The Andalusian cohort consisted of 353 ICU patients from the PANGEA study. Logistic regression models, selected by a greedy search algorithm and validated by repeated cross-validation, were used to determine the contributions of different variables to diagnostic accuracy. Diagnostic performance was quantified by area under the receiver operating characteristic curve (AUC). Results: For the 510k cohort, a baseline AUC of 0.69-0.73 was observed using 5-7 vital and demographic variables assessed immediately upon presentation (time T1). The addition of clinical-laboratory variables, in particular SeptiCyte RAPID, within 1-3 hours post-presentation (time T2) increased the AUC to 0.83-0.85). Finally, the addition of microbiological data 12-24 hours post-presentation (time T3) further improved the AUC to 0.90-0.91. Similar results were obtained for the Andalusian cohort. AUC values at the three time points were as follows: At time T1, AUC = 0.67 based solely on vital signs and demographics; at time T2, AUC = 0.87 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data; at time T3, AUC = 0.93 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data + microbiology results). For both cohorts, the most significant variables included temperature, mean arterial pressure, respiratory rate, suspected infection site; SeptiCyte RAPID, procalcitonin, confirmed bacterial infection and positive blood culture confirmation. Conclusions: Accuracy of identification of sepsis increases markedly as demographics and vital signs are supplemented with clinical-laboratory information, and ultimately with microbiological culture results. The fastest improvement occurs within the first 3 hours when laboratory data, and in particular SeptiCyte RAPID results, become available. Integrating rapid host-response testing with SeptiCyte RAPID into time-based diagnostic frameworks may enhance early sepsis recognition, improve antimicrobial stewardship, and support guideline-driven clinical decisions.

Background. The intrapulmonary pharmacokinetics of antimicrobial agents used to treat nontuberculous mycobacterial (NTM) pulmonary disease remain poorly characterized, limiting the optimization of dosing regimens. This study characterized the plasma and intrapulmonary pharmacokinetics of azithromycin, ethambutol, rifampicin, clofazimine, and amikacin, as well as their penetration into pulmonary lesion sites. Methods. We prospectively enrolled patients undergoing guideline-based treatment for NTM pulmonary disease who were indicated for surgical resection at a single center in Japan. Drug concentrations were measured in the plasma and lung samples, and analyzed using a population pharmacokinetic model. The lung lesion site, cavity, or nodule/bronchiectatic were evaluated as covariates of the plasma-to-lung partition ratios. Results. Twenty-four patients were enrolled in the study. Antimicrobial agents other than rifampicin and amikacin accumulate in the lungs at concentrations > 40-fold higher than those in the plasma. Notably, the intrapulmonary half-life of ethambutol, which has not been well-characterized to date, is estimated to be approximately 2 months, indicating prolonged retention within the lungs. Evaluation of drug penetration into cavities and nodular/bronchiectatic lesions showed no clearly reduced concentration compared to that of normal lung tissue. However, in the single case where the caseum was obtained, azithromycin, ethambutol, and rifampicin levels exhibited clearly lower concentrations. Conclusions. Ethambutol shows a prolonged intrapulmonary half-life, suggesting sustained lung exposure even with intermittent dosing. The absence of clearly reduced drug penetration into lesion sites suggests that lesion phenotype alone may have limited value in guiding drug selection.

Accurate and objective evaluation of surgical skill and performance is critical for advancing training and improving patient outcomes. Current assessment methods increasingly rely on video analytics and depend on labor-intensive, frame-by-frame manual annotation by experts. In this work we developed a surgical video annotation platform (AnnotX) that used a Python backend running a pretrained promptable video segmentation foundation model, i.e., Segment Anything 3 (SAM 3) for per frame segmentation and temporal segment propagation. With a few interactions per class, the model generated a high-quality mask on a key frame and propagated it through the sequence. The platform automatically exported per-class binary masks and color overlays for every frame, together with deterministic metadata and a standardized study folder structure to support auditability and downstream analysis. On deidentified laparoscopic surgery videos, the system processed typical clips in minutes and reduced expert annotation time from hours to minutes without task-specific fine-tuning. We also benchmarked multiple SAM variants (SAM 2, MedSAM 2, and SAM 3) on the CholecSeg8K dataset, and showed AnnotX with a SAM 3 backbone outperformed alternatives. It exhibited a mean IoU of 0.884 and mean Dice of 0.924 across 101 annotated sequences. By being free, practical, and lightweight to deploy, AnnotX aims to accelerate reproducible surgical dataset creation and provides a step toward scalable, video-based performance evaluation in training and quality-improvement settings.

Background Depression and anxiety are prevalent among cardiovascular disease (CVD) patients and significantly worsen clinical outcomes, increasing complications, recurrent events, and healthcare costs. Evidence shows that psychological stress, depression, and anxiety elevate CVD risk, while post-discharge nurse-led telephone follow-up has demonstrated benefits in patient support and symptom management. Little is known about its impact on mental health. Objective The aim of this study was to evaluate the effects of implementing the "nurse telephone follow-up" project on depression, anxiety and stress levels among cardiovascular patients. Methods An experimental study was conducted with 60 randomly selected patients from the Coronary Care Unit (CCU) department of a hospital in Iran, who were divided into two groups: an intervention group and a control group. The educational intervention was administered within two weeks after discharge. Data were collected via the Depression Anxiety Stress Scale (DASS-21). Descriptive analysis, Mann?Whitney and Wilcoxon tests, Generalized Estimating Equations (GEE) regression, and Spearmans correlation were used for data analysis. Results The mean age of the patients was 57.43 {+/-} 15.33 years. While no significant difference was found between the intervention and control groups in terms of depression, anxiety, or stress (p>0.05), the depression score decreased by 1.53 points, and the anxiety score decreased by 1.18 points after the intervention. Furthermore, an increase in patients ejection fraction (EF) score was associated with a 0.1 decrease in both depression and anxiety levels. No significant relationship was found between stress and any variables. Conclusions The results of this study suggest that psychological and therapeutic interventions may help reduce depression and anxiety in patients with cardiovascular diseases. However, this requires further detailed evaluation and additional studies. The potential link between improved cardiac function and reduced psychological symptoms could be effective in designing more comprehensive treatments for these patients.

Background Postoperative sleep disorder, a frequently observed complication, is associated with heightened pain sensitivity, exacerbated inflammatory reactions, and compromised tissue repair. Sufentanil, a highly selective -opioid receptor agonist, is widely used in patient-controlled intravenous analgesia (PCIA) and has been associated with reduced sleep efficiency. Oxycodone, as a /{kappa} dual receptor agonist, has shown a lower incidence of adverse effects in clinical practice. Despite these pharmacological differences, the comparative effects of oxycodone- versus sufentanil-based PCIA on postoperative sleep remain poorly characterized. Recent advances in wearable devices demonstrate strong agreement with polysomnography (PSG) in intergroup comparisons of sleep efficiency and total sleep time, enabling continuous, non-invasive, multi-night sleep monitoring and offering a viable alternative for clinical postoperative sleep research. Hence, we design this clinical trial to compare postoperative sleep efficiency between patients receiving oxycodone-based versus sufentanil-based PCIA under wearable sleep monitoring. Methods This study is a randomized, double-blind, placebo-controlled trial that was conducted at a single center. A sample size of 68 patients was determined through calculation, and these patients will be randomly assigned to either the oxycodone group or the sufentanil group. Sleep monitoring was initiated using a wristband device one day before surgery after recruitment. The sleep quality data at different setting time will be monitored. All patients will be followed up by blinded evaluators at baseline and 1, 2, and 30 days after the intervention. The follow-up included pain scores, postoperative complications and adverse events, etc. Discussion By integrating a modern photoelectric device with first-line analgesics, we hope the result of the study will inform perioperative sleep management, guide clinical analgesic selection, and improve patient recovery quality.

Purpose: Polypharmacy is highly prevalent among critically ill patients, yet it's independent impact on intensive care unit (ICU) outcomes in sepsis remains critically unexplored. We aimed to evaluate whether pre-admission polypharmacy independently predicts ICU mortality and provides incremental prognostic value using the medication reconciliation module of the MIMIC-IV-ED linked database. Materials and Methods: We conducted a retrospective cohort study of 3,347 adults admitted to the ICU who met Sepsis-3 criteria. Pre-admission polypharmacy was categorized as none (0-4), standard (5-9), or high (>=10 medications). Multivariable logistic regression, propensity score matching, and reclassification analyses (NRI/IDI) were performed. The primary outcome was in-hospital ICU mortality. Results: High polypharmacy was present in 58.9% of patients. Crude ICU mortality increased sequentially: 18.5% (none), 26.0% (standard), and 27.5% (high; p < 0.001). After multivariable adjustment, high polypharmacy independently predicted in-hospital ICU mortality (aOR 1.45, 95% CI (1.10-1.91)), and 28-day mortality (aOR 1.47). Drug-class analysis identified statins as significantly protective (aOR 0.56), whereas RAS blockers combined with diuretics increased acute kidney injury risk (aOR 1.49). Propensity matching confirmed the primary mortality association (matched aOR 1.28). Conclusions: By utilizing the ED medication reconciliation table, this study proves high polypharmacy represents a distinct 'pharmacologic frailty', independent of acute severity. Available instantly at triage, this zero-latency metric provides significant early prognostic value (SOFA NRI = 0.24) and identifies actionable high-risk interactions (e.g., RAS blockers plus diuretics) for immediate, targeted pharmacist-led intervention upon ICU admission.

PurposeIn this study, we aimed to develop and evaluate an artificial intelligence-based diagnostic model for the diagnosis of acute cholecystitis (AC) using non-contrast CT images and clinical data. Materials and MethodsThis retrospective study included 199 patients (100 AC, 99 non-AC) treated between January 2016 and December 2025 at a single center. Patients were randomly divided into training (n=139) and test (n=60) datasets. Three models were constructed: an imaging-based deep learning model, a clinical data-based machine learning model, and a hybrid machine learning model integrating deep learning-derived imaging features with clinical data. CT images were preprocessed, and gallbladder regions were segmented. Clinical variables included white blood cell counts and levels of C-reactive protein and liver function markers. Model performance was evaluated using accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Statistical comparisons were performed using Welchs t-test and Chi-square test. ResultsThe imaging-based model achieved accuracy 0.883, precision 0.848, recall 0.933, specificity 0.833, and AUC 0.916. The blood-based model achieved accuracy 0.917, precision 0.931, recall 0.900, specificity 0.933, and AUC 0.949. The hybrid model showed the highest performance, with accuracy 0.950, precision 0.909, recall 1.000, specificity 0.900, F1 score 0.952, and AUC 0.986. ConclusionA hybrid model integrating CT imaging and clinical data improved diagnostic performance for AC compared with single-modality models.