PurposeWhile diabetic nephropathy (DN) is a known risk factor for poor outcomes in major surgery, its specific impact on perioperative complications following primary total knee arthroplasty (TKA) has not been well-quantified in large-scale studies. This study aimed to determine the correlation of pre-existing DN on perioperative complications, mortality, and healthcare resource utilization in patients undergoing primary TKA.MethodsA retrospective cohort study utilized the US National Inpatient Sample (2016-2019). Patients undergoing primary TKA were stratified into DN and non-DN cohorts using ICD-10 codes, excluding end-stage renal disease or revision cases. A 1:1 propensity score-matched analysis was performed to balance demographics, hospital characteristics, and 30 comorbidities between cohorts. Outcomes included surgical/medical complications, mortality, length of stay (LOS), and hospitalization costs. Multivariable logistic regression analyzed associations pre- and post-PSM.ResultsDN prevalence increased significantly from 1.2% (2016) to 3.4% (2019). After PSM, DN patients had significantly higher adjusted risks of multiple complications. Surgical complications included increased transfusion (aOR=1.64), intubation/mechanical ventilation (aOR=1.52), periprosthetic infection (aOR=1.38). Medical complications included pulmonary edema (aOR=3.40), acute myocardial infarction (aOR=2.13), stroke (aOR=1.43), atrial fibrillation/flutter (aOR=1.09), postoperative delirium (aOR=1.66), urinary retention (aOR=1.26), and urinary tract infection (aOR=1.25). Resource utilization significantly increased, with higher odds of extended LOS (aOR=1.44) and elevated hospitalization costs (aOR=1.19).ConclusionThese findings underscore that DN is significantly associated with a distinct pattern of TKA complications, advocating for targeted perioperative strategies to mitigate risks in this growing population.

The human body is a complex and irregular system that generates diverse physiological signals. In the era of wearable technology, an increasing number of physiological signals can be continuously monitored, generating rich time series data that capture the dynamic physiological fluctuations and may reveal subtle signs of dysregulation. For example, continuous glucose monitoring (CGM) systems generate glucose time series data. As such, new metrics harnessing the time series nature of physiological signals may serve as sensitive markers of health. Of them, complexity focuses more on the dynamical characteristics of physiological signals and can reveal hidden information that linear methods may miss, allowing for the handling of nonstationary data and providing a more precise capture of individual differences. Importantly, the complexity of physiological signals may reflect the regulatory capacity, adaptability, and functional reserves of the body, which are often impaired in chronic diseases and during aging. Using CGM-derived glucose complexity as a representative example, this review discusses the clinical significance and applications of complexity in chronic diseases and aging. Finally, we outline future directions for complexity-related research, particularly regarding the roles of artificial intelligence (AI), wearable devices, and clinical translation.

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) worldwide. In South Korea, DKD accounts for nearly half of all ESKD cases, yet prospective data reflecting the clinical characteristics of Korean patients remain limited. This study aims to investigate the clinical and biological features of DKD in Koreans and identify long-term prognostic indicators.

The Korean Diabetic Kidney Disease (K-DKD) Cohort is a nationwide prospective multicenter study investigating the clinical, biochemical, and molecular determinants of DKD progression in Korean adults with type 2 diabetes mellitus. Approximately 1,300 participants will be enrolled from 15 university-affiliated hospitals in South Korea. Demographic, clinical, laboratory data and outcome data are collected using a standardized electronic case report form at baseline and annually for up to 10 years. Blood and urine biospecimens are obtained at baseline and every 2 years thereafter for centralized storage and biospecimen analyses. Clustering analyses will identify the distinct clinical phenotypes of DKD, and long-term prognostic models will be developed on the basis of renal and cardiovascular outcomes using traditional and artificial intelligence-based approaches. Additionally, treatment patterns and management behaviors will be evaluated to understand the real-world clinical practices in Korean DKD.

The K-DKD Cohort will expand Korea's national big data and biobank platform into a DKD-focused prospective study that integrates standardized clinical and biospecimen data. It aims to facilitate biomarker discovery, data-driven risk prediction, and precision research in Korean patients with DKD.

Finerenone reduces Cardiovascular-Kidney-Metabolic (CKM) risk in type 2 diabetes mellitus (T2DM)-related chronic kidney disease (CKD). Evidence on its combined use with sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) in routine care remains limited. This study aimed to evaluate 6-month estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and adverse outcomes among adults with T2DM-related CKD receiving finerenone + SGLT2i, with or without concomitant GLP-1RA.

This retrospective cohort evaluated electronic medical records (EMR) from a UAE-based teaching hospital (November 2023 - January 2025). Adults with T2DM-related CKD receiving finerenone for ≥6 months were categorized at initiation into dual-therapy (Finerenone+SGLT2i), and triple-therapy (Finerenone+SGLT2i+GLP-1 RA), Patients receiving finerenone + GLP-1RA alone were excluded. Outcomes were 6-months eGFR, UACR and adverse events. Via Statistical Package for the Social Sciences (SPSS), means were analysed using paired t-tests, and between-group comparisons using adjusted analysis.

Eighty-three patients were included after excluding 6 patients receiving finerenone + GLP-1RA only. Within-group ln(UACR) decreased in both regimens [dual-therapy (50.6%, 28.3-65.9; P < 0.001) and triple-therapy (46.9%, 27.0-61.3; P < 0.001)]. Mean 6-month eGFR changes were variable [dual-therapy (-6.12, -10.66 to -1.59; P = 0.010) and triple-therapy (-1.49, -5.19 to +2.21; P = 0.424)]. On adjusted analysis, triple-therapy was associated with higher 6-month eGFR compared with dual therapy (+6.407 mL/min/1.73 m²; 95% CI: +0.297 to +12.52; P = 0.040), whereas for ln(UACR) was not statistically significant (-0.323; 95% CI: -0.912 to +0.266; P = 0.277). Interpretation was limited by baseline imbalance and the retrospective observational design.

Triple-therapy was associated with higher adjusted 6-month eGFR than dual-therapy, while UACR decreased without significant adjusted between-group differences. Findings were interpreted as associative rather than comparative superiority, given the observational design, baseline imbalance, and short follow-up. These results should be interpreted as hypothesis-generating associations, and future multicenter longitudinal studies with longer follow-up and assessment of medication continuation, and time-updated exposure are warranted.

Development of ectodermal tissues in humans is a complex process regulated by interacting signaling pathways. This review adopts an evolutionary medicine framework to examine how genetic variation in components of the ectodysplasin A (EDA) signaling pathway may have conferred human evolutionary fitness during the last ice age, but today may present an evolutionary mismatch, contributing to modern chronic diseases. A missense substitution at amino acid 370 in the human EDA receptor (EDAR) gene (EDARV370A), which is very common in East Asian, Native American, and Latino populations, produces pleiotropic effects in ectodermal tissues such as hair, teeth, mammary glands, and skin appendages. Several hypotheses have been offered to explain the high frequency of EDARV370A, proposing selective advantages related to thermal regulation, mother-infant vitamin D transmission, and/or tooth shape. However, new insight suggests that the selective advantage could have involved EDAR's systemic and epithelial immune effects rather than embryonic effects alone. We explore the possibility that inflammatory effects associated with EDARV370A were the target of selection approximately 20 000 years ago, but that this selective advantage could now represent an evolutionary mismatch in the context of abundant caloric supply, resulting in chronic inflammation and a higher risk of metabolic syndrome. Finally, we outline directions for future research.

PD-1 inhibitor-based immunotherapy is increasingly used for locally advanced or metastatic esophageal cancer, yet the impact of metabolic factors-including the emerging "obesity paradox", wherein high-body mass index (BMI) patients demonstrate a lower risk of all-cause cancer mortality and superior immunotherapy efficacy compared to those with a low-to-normal BMI-on treatment efficacy remains largely unknown. Patients were stratified by BMI and metabolic syndrome (MetS) to assess survival and treatment response; a prognostic nomogram was subsequently developed via LASSO and multivariable Cox regression and validated internally and externally using the respective institutional cohorts. A total of 286 eligible patients with locally advanced or metastatic esophageal cancer from two medical centers were included. Patients with high BMI or MetS demonstrated significantly superior overall survival, progression-free survival, and objective response rates to anti-PD-1 therapy. These groups exhibited favorable immune profiles, characterized by a lower pan-immune inflammation value (PIV) and a higher combined positive score (CPS). A prognostic nomogram was successfully constructed and validated, incorporating four independent predictors: gender, BMI, TNM stage, and PIV. The model demonstrated high predictive accuracy for 24- and 36-month overall survival in both internal and external validation cohorts, with well-fitted calibration curves. Elevated BMI serves as an independent predictor of improved outcomes following immunotherapy in locally advanced or metastatic esophageal cancer, whereas the prognostic trend of MetS appears largely dependent on body mass. Furthermore, our validated nomogram integrates these nutritional and metabolic profiles to enable refined prognostic assessment.

Mechanical stress is a fundamental aspect of soft tissues that influences cellular behavior and tissue integrity, yet noninvasive imaging of stress in vivo remains a major challenge. Conventional magnetic resonance elastography (MRE) measures tissue stiffness but not stress. Employing a physics-informed model, we analyze reverberant shear wave fields recorded by MRE to capture the 3D mapping of tissue stress. By decomposing reverberant waves into anisotropic traveling wave components, we establish a direct relationship between wave speed, polarization, and local stress, without requiring wave direction knowledge. This method was validated with numerical simulations and phantom experiments. Applied to patients with meningiomas and pituitary adenomas, it generated high-resolution stress maps consistent with anatomy and physiology. Cortical stress measurements enabled intracranial pressure estimation, uncovering trends related to patient age and pathology. Our results demonstrate that noninvasive stress imaging provides a novel quantitative biomarker for mechanobiological research and suggests new possibilities for clinical applications.

Prompt-driven vision foundation models, such as the Segment Anything Model, have shown adaptability in computer vision, but their use in medical imaging remains challenging due to heterogeneous anatomy, artefacts, and low-contrast tumour boundaries. This is particularly difficult in whole-body tumour analysis, where models must transfer across modalities, anatomies, and tumour appearances. Here, we present Segment Any Tumour 3D (SAT3D), a lightweight volumetric foundation model for generalisable tumour segmentation across diverse medical imaging modalities, organs, and cohorts. SAT3D integrates a shifted-window vision transformer with critic-guided uncertainty-aware training, using confidence maps as dense prompts to guide boundary prediction in ambiguous regions. We benchmark SAT3D against vision foundation models, prompt-driven and task-specific methods across 11 public datasets. Trained on 17,075 three-dimensional volume-mask pairs, SAT3D shows robust generalisation, including in out-of-distribution settings, and is supported by a 3D-Slicer plugin for interactive segmentation, underscoring SAT3D's potential as a scalable foundation model for medical image analysis.

Intraductal papillary mucinous neoplasms (IPMNs) are common pancreatic cystic neoplasms with malignant potential. Current evidence on IPMN management is derived largely from retrospective surgical cohorts that over-represent high-risk cases and provide limited insight into the long-term outcomes of conservatively managed lesions. Australia currently lacks a prospective national dataset to evaluate surveillance pathways, practice patterns, guideline adherence and long-term outcomes in individuals with IPMN. Variation between international guideline recommendations may also contribute to differences in surveillance intensity and thresholds for intervention. The Australian Pancreatic Cyst Registry (APCR) was established to address these evidence gaps.

The APCR is a prospective, multicentre clinical quality registry that collects clinical, imaging, management and outcome data for individuals with IPMN across public and private healthcare settings in Australia. Historical information is extracted from existing clinical documentation where available, with prospective data collection continuing during routine follow-up. The registry operates under an opt-out model and aims to enrol up to 10 000 participants over a 5-year recruitment period. Participants are followed through routine clinical care until discharge from surveillance, diagnosis of pancreatic cancer, withdrawal or death. Standardised electronic case report forms capture clinical and imaging variables aligned with risk features described in the 2024 Kyoto guidelines. This analysis will describe participant characteristics, cyst features, management and clinical outcomes, with comparative, time-to-event and multivariable analyses undertaken where appropriate. Guideline adherence and the predictive performance of established risk stratification frameworks will also be evaluated.

The study protocol was approved by the Monash Health Human Research Ethics Committee (Project ID: RES-24-0000-634A). Findings will be disseminated through peer-reviewed publications, conference presentations and updates to participating sites.

To evaluate the accuracy of contrast-enhanced mammography (CEM) in the diagnosis and local staging of invasive lobular carcinoma (ILC) of the breast.

A single-centre retrospective study was conducted between September 2019 and June 2024, including patients with a histological diagnosis of pure invasive lobular carcinoma (ILC) who underwent primary surgery and contrast-enhanced mammography (CEM) as part of preoperative planning. Tumour characteristics and multifocality were analysed using conventional mammography, CEM and breast magnetic resonance imaging (MRI) when available. Diagnostic accuracy was analysed based on the detection rates of the imaging techniques. Local staging was evaluated by comparing the preoperative tumour size and presence of multifocality with the pathological findings.

A total of 16 patients with pure ILC were included. Conventional mammography detected the tumour in only 56.2% of cases (9/16). CEM detected 100% of ILCs, and MRI detected 100% of the cases evaluated (14/14). Accuracy in tumour size assessment among detected cases showed mean tumour sizes of 13.87 mm with conventional mammography, 36.06 mm with CEM, and 38.71 mm with MRI, compared to 35.1 mm on pathological examination. A statistically significant difference was observed between tumour size measurements by conventional mammography and the histological size (P = .0075). No statistically significant differences were found between tumour sizes measured by CEM (P = .85) or MRI (P = .75) and histological size. CEM demonstrated a higher detection rate of multifocality compared to conventional mammography, and a rate similar to that of MRI.

CEM is an emerging radiological technique that has shown high accuracy for the diagnosis and staging of ILC of the breast, with a diagnostic performance comparable to that of MRI.