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Background Immune checkpoint inhibitors (ICIs) are effective for advanced hepatocellular carcinoma (HCC). However, there are few reports on the correlation between the clinical efficacy of ICIs and the development of immune-related adverse events (irAEs) in patients with HCC. The aim of this study was to investigate the association between irAE development and survival in patients with HCC treated with atezolizumab plus bevacizumab. Patients and Methods We enrolled 150 patients with advanced HCC treated with atezolizumab plus bevacizumab between October 2020 and October 2021 at 5 territorial institutions. We compared the efficacy of atezolizumab plus bevacizumab between patients who experienced irAEs (irAE group) and those who did not (non-irAE group). Results Thirty-two patients (21.3%) developed irAEs of any grade. Grade 3/4 irAEs were observed in 9 patients (6.0%). The median progression-free survivals (PFS) in the irAE and non-irAE groups were 273 and 189 days, respectively (P = .055). The median overall survivals (OS) in the irAE and non-irAE groups were not reached and 458 days, respectively (P = .036). Grade 1/2 irAEs significantly prolonged PFS (P = .014) and OS (P = .003). Grade 1/2 irAEs were significantly associated with PFS (hazard ratio [HR], 0.339; 95% confidence interval [CI], 0.166-0.691; P = .003) and OS (HR, 0.086; 95% CI, 0.012-0.641; P = .017) on multivariate analysis. Conclusion The development of irAEs was associated with increased survival in a real-world population of patients with advanced HCC treated with atezolizumab plus bevacizumab. Grade 1/2 irAEs were strongly correlated with PFS and OS. Immune checkpoint inhibitors (ICIs) are effective for advanced hepatocellular carcinoma (HCC), but the correlation between the clinical efficacy of ICIs and the development of immune-related adverse events (irAEs) in patients with HCC is unclear. This article reports on the association between irAE development and survival in patients with HCC treated with atezolizumab plus bevacizumab.

Objective Parkinson's disease (PD) affects both motor and non‐motor functions, including facial expressivity. While subthalamic nucleus deep brain stimulation (STN‐DBS) is effective for motor symptoms, its impact on facial expression remains unclear. This study aimed to objectively assess the impact of DBS on facial movement timing and amplitude using objective facial landmark‐based motion analysis. Methods We analyzed 10 PD patients with STN‐DBS (≥ 3 years) and 10 age‐ and sex‐matched controls. Standardized video recordings were obtained using a fixed camera setup. Facial movements were analyzed using objective facial landmark‐based motion analysis implemented via a pre‐trained facial landmark detection framework (Mediapipe), extracting 468 facial landmarks across approximately 1800 frames per recording. Key facial parameters, including smile completion time and region‐specific ranges of motion (ROM), were quantified. Results DBS significantly increased oral commissure and frontal ROM (p < 0.05) while reducing smile completion time (p = 0.003). However, PD patients' smiles remained slower than those of controls (0.64 ± 0.21 s vs. 0.49 ± 0.09 s; p = 0.019), indicating incomplete normalization of facial expressivity. Medial eyebrow and palpebral movements showed no significant differences (p > 0.05). Conclusion STN‐DBS enhances the amplitude of some facial movements but does not fully restore temporal dynamics of facial expression. Persistent delays in smile execution suggest that aspects of facial motor control may remain impaired despite effective stimulation. Objective facial landmark‐based motion analysis provides a sensitive tool for detecting subtle alterations in facial motor dynamics and may support future studies investigating complementary strategies to optimize motor expressivity in PD. Schematic illustration of facial expression analysis in patients with Parkinson's disease during deep brain stimulation (DBS) ON and OFF states. DBS ON is associated with improved facial expressivity compared with the OFF state, as assessed by automated facial movement metrics.

Background Eligibility criteria and endpoints for cancer cachexia trials—and whether weight loss should be included—remain controversial. Although most cachexia trials enrol patients after initial cancer diagnosis, few studies have addressed whether weight loss well after a cancer diagnosis is prognostic. Methods We pooled data from non‐small cell lung cancer patients from prospectively conducted trials within the Alliance for Clinical Trials in Oncology (1998–2008), a nationally funded infrastructure. We examined (i) weight data availability and weight changes and (ii) survival. Results A total of 822 patients were examined. Of these, 659 (80%) were on treatment at the beginning of Cycle 2 of chemotherapy; weight was available for 656 (80%). By Cycles 3 and 4, weight was available for 448 (55%) and 384 (47%), respectively. From baseline to immediately prior to Cycle 2, 208 (32%) gained weight; 225 (34%) lost <2% of baseline weight; and 223 (34% of 656) lost 2% or more. Median survival from the beginning of Cycle 2 was 13.0, 10.9, and 6.9 months for patients with weight gain, weight loss of <2%, and weight loss of 2% or more, respectively. In multivariate analyses, adjusted for age, sex, performance score, type of treatment, and body mass index, weight loss of 2% or more was associated with poor overall survival compared with weight gain [hazard ratio (HR) = 1.66; 95% confidence interval (CI): 1.33–2.07; P < 0.001] and compared with weight loss of <2% (HR = 1.57; 95% CI: 1.27–1.95; P < 0.001). Although weight loss of <2% was not associated with poorer overall survival compared with weight gain, it was associated with poorer progression‐free survival (HR = 1.24; 95% CI: 1.01–1.51; P = 0.036). Similar findings were observed in a separate 255‐patient validation cohort. Conclusions Weight should be integrated into cancer cachexia trials because of its ease of frequent measurement and sustained prognostic association.

Background In people with prediabetes, the link between developing type 2 diabetes (T2D) and cancer risk among those with impaired glucose tolerance (IGT) remains uncertain. We examined this association in IGT individuals from primary care in South and West Auckland, New Zealand, spanning 1994–2019, assessing 5- and 10-year cancer risks. Methods Study cohorts were extracted from the Diabetes Care Support Service in Auckland, New Zealand, linking it with national registries for death, cancer, hospital admissions, pharmaceutical claims, and socioeconomic status. We compared cancer risks in individuals with IGT newly diagnosed with or without T2D within a 1–5-year exposure window. Employing tapered matching and landmark analysis to address potential confounding effects, we formed comparative IGT cohorts. Weighted Cox regression models were then employed to assess the association between T2D onset and 5- and 10-year cancer risks. Results The study included 26,794 patients with IGT, with 629 newly diagnosed with T2D within 5 years and 13,007 without such a diagnosis. Those progressing to T2D had similar 5-year cancer risk but significantly higher 10-year risk (HR 1.35; 95% CI 1.09–1.68). This association was stronger in older individuals, the socioeconomically deprived, current smokers, those with worse metabolic measures, and lower renal function. Patients with IGT of NZ European ethnicity had lower 10-year cancer risk. Conclusions T2D diagnosis influences cancer risk in individuals with IGT. Developing risk scores for high-risk IGT individuals and implementing cancer screening and structured diabetes prevention, especially in deprived or minority ethnic populations, is essential.

INTRODUCTION We aimed to investigate the association between the onset of type 2 diabetes (T2D) and dementia incidence rates (IR) in the population with impaired glucose tolerance (IGT) identified in primary care in New Zealand (NZ) over 25 years. METHODS Tapered matching and landmark analysis (accounting for immortal bias) were used to control for potential effects of known confounders. The association between T2D onset and 5‐ and 10‐year IR of dementia was estimated by weighted Cox models. RESULTS The onset of T2D was significantly associated with the 10‐year IR of dementia, especially in the socioeconomically deprived, those of non‐NZ European ethnicity, those currently smoking, and patients with higher metabolic measures. DISCUSSION Our findings suggest that the onset of T2D is a significant risk factor for dementia in individuals with IGT. Dementia screening and structured diabetes prevention are vital in the population with IGT, particularly those from deprived or ethnic minority backgrounds. Highlights Increased dementia incidence rate links with T2D onset in people with IGT. Significant incidence varied by ethnicity, socioeconomic status, and health factors. Results emphasize the diabetes manage and socioeconomic factors on dementia risk. Secondary analysis highlights the key role of vascular health in dementia prevention.

Local consolidative radiotherapy in the treatment of metastatic malignancies has shown promising results in several types of tumors. The objective of this study was to assess consolidative radiotherapy to the bladder and to residual metastases in metastatic urothelial bladder cancer with no progression following first-line systemic therapy. Materials/methods: Patients who received first-line therapy for the treatment of metastatic urothelial bladder cancer (mUBC) and who were progression-free following treatment with no more than five residual metastases were retrospectively identified through the database of four Comprehensive Cancer Centers, between January 2005 and December 2018. Among them, patients who received subsequent definitive radiotherapy (of EQD2Gy > 45Gy) to the bladder and residual metastases were included in the consolidative group (irradiated (IR) group), and the other patients were included in the observation group (NIR group). Progression-free survival (PFS) and overall survival (OS) were determined from the start of the first-line chemotherapy using the Kaplan–Meier method. To prevent immortal time bias, a Cox model with time-dependent covariates and 6-month landmark analyses were performed to examine OS and PFS. Results: A total of 91 patients with at least stable disease following first-line therapy and with no more than five residual metastases were analyzed: 51 in the IR group and 40 in the NIR group. Metachronous metastatic disease was more frequent in the NIR group (19% vs. 5%, p = 0.02); the median number of metastases in the IR group vs. in the NIR group was 2 (1–9) vs. 3 (1–5) (p = 0.04) at metastatic presentation, and 1 (0–5) vs. 2 (0–5) (p = 0.18) after completion of chemotherapy (residual lesions), respectively. Two grade 3 toxicities (3.9%) and no grade 4 toxicity were reported in the IR group related to radiotherapy. With a median follow up of 85.9 months (95% IC (36.7; 101.6)), median OS and PFS were 21.7 months (95% IC (17.1; 29.7)) and 11.1 months (95% IC (9.9; 14.1)) for the whole cohort, respectively. In multivariable analysis, consolidative radiotherapy conferred a benefit in both PFS (HR = 0.49, p = 0.007) and OS (HR = 0.47, p = 0.015) in the whole population; in the landmark analysis at 6 months, radiotherapy was associated with improved OS (HR = 0.48, p = 0.026), with a trend for PFS (HR = 0.57, p = 0.082). Conclusion: Consolidative radiotherapy for mUBC patients who have not progressed after first-line therapy and with limited residual disease seems to confer both OS and PFS benefits. The role of consolidative radiotherapy in the context of avelumab maintenance should be addressed prospectively.

Background: Almost 30% of patients with rectal cancer (RC) who submit to comprehensive treatment experience relapse. Surveillance plays a leading role in early detection. The landmark approach provides a more flexible and dynamic framework for survival prediction. Objective: This large retrospective study aims to develop a machine learning algorithm to profile the patient prognosis, especially the risk and the onset of RC relapse after curative resection. Methods: A cohort of 2450 RC patients were analyzed using landmark analysis. Model A applied a classical cause-specific Cox approach with a landmarking approach, while Model B implemented a landmarking-based RSF (random survival forest) competing risk algorithm. The two models were compared in terms of predictive and interpretative ability. A bootstrapped validation strategy was employed to validate the model’s performance and prevent overfitting. The best-performing hyperparameters were selected systematically, ensuring the model’s robustness within the landmark approach. The study assessed these factors’ importance and interactions using RSF and compared the predictive accuracy to that of the classical Cox model. Results: Model B outperformed Model A (mean C-index 0.95 vs. 0.78), capturing complex interactions and providing dynamic, individualized relapse predictions. Clinical factors influencing survival outcomes were identified across time with the landmark approach allowing for more accurate and timely predictions. Conclusions: The landmark approach offers an improvement over traditional methods in survival analysis. By accommodating time-dependent variables and the evolving nature of patient data, this approach provides a precise tool for profiling RC survival, thereby supporting more informed and dynamic clinical decision-making.

The genus Culicoides is the best known of the family Ceratopogonidae. Hematophagous females of the genus typically feed on the blood of vertebrate animals and in the Brazilian Amazon often on the blood of human beings. Amazon region anthropophilic Culicoides bites can provoke allergic reactions and transmit Mansonella ozzardi as well as the Oropouche virus. Past integrated taxonomy studies, combining morphometric and molecular analyses, have revealed hidden disease vector biodiversity and cryptic species with epidemiological and disease control relevance and have provided new tools to assist with vector identification. For this study we used light traps set in 12 distinct sites from three different Amazon states: Rondonia (1 site), Amazonas (3 sites) and Para (8 sites). We captured 12 different species of Culicoides representing seven different subgenera: C. fox/, C. fu-sipalpis, C. hylas, C. insignis, C. plaumanni, C. pseudodiabolicus, C. ruizi, C. debilipalpis, C. glabrior, C. jurutiensis, C. paraensis, C. paucienfuscatus. Between two and nine specimens were barcoded of each species. Neighbor joining and Maximum likelihood phylogenetic analysis with these COI barcodes showed the utility of these barcode sequences for species identification by clustering the barcode sequences into bootstrap-supported, species-specific monophyletic groups. Although this barcoding analysis did not resolve relationships between the species studied, it did reveal cryptic diversity within C. paucienfuscatus, C. glabrior, C. plaumanni, C. insignis and C. pseudodiabolicus. Two-dimensional geometric morphometries, using eight wing-vein landmarks, robustly separated the analyzed species and raised questions about the validity of the subgenus Haematomyidium. Importantly, our GM wing landmark analysis separated C. paraensis from all the other analyzed species suggesting this type of analysis could be harnessed for epidemiological monitoring of this key Amazon-region vector species.

Incompleteness in fossil specimens is the main obstacle that has to be overcome with the identification of fossils and the analyses using them. Landmark analysis is a tool that can be used to understand the variations in different organismal factors based on morphology with adequate preparation and properly aligned photographs of specimens. The goal is to apply these methods to assess generic level identification of Miocene Astriclypeidae, including Astriclypeus and Echinodiscus, based on incomplete onsite specimens from the Yehliu Geopark, Taiwan; a locality in where the removal of fossil specimens is not permitted. Two datasets were chosen utilizing the available fossil specimens from the site: a three-point dataset and a seven-point dataset. Linear measurements of onsite specimens were also recorded for comparison. Results from Principal Component Analysis (PCA) showed that samples of Astriclypeus and Echinodiscus formed distinct clusters based on three-point dataset. A similar trend with distinct clusters for the two genera was also evident with the seven-point dataset. Furthermore, the effectiveness of this method is reinforced by the results of the independent study using the traditional method with linear measurements. Geometric morphometrics can specifically identify where morphological variation occurs and is concentrated based on the chosen landmark arrays, and such morphological variations cannot be detected easily with the linear and angle measurements alone. This study shows that the landmark analysis can be used efficiently for a generic level identification based on incomplete specimens.

Background In recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability. Methods A joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model, and regression calibration) were illustrated using data from a large multi-center randomized phase III trial, the Ocular Hypertension Treatment Study (OHTS), regarding the association between the variability of intraocular pressure (IOP) and the development of primary open-angle glaucoma (POAG). The model performance was also evaluated in terms of bias using simulated data from the joint model of longitudinal IOP and time to POAG. The parameters for simulation were chosen after OHTS data, and the association between longitudinal and survival data was introduced via underlying, unobserved, and error-free parameters including subject-specific variance. Results In the OHTS data, joint modeling and two-stage methods reached consistent conclusion that IOP variability showed no significant association with the risk of POAG. In the simulated data with no association between IOP variability and time-to-POAG, all the two-stage methods (except the naïve approach) provided a reliable estimation. When a moderate effect of IOP variability on POAG was imposed, all the two-stage methods underestimated the true association as compared with the joint modeling while the model-based two-stage method (regression calibration) resulted in the least bias. Conclusion Regression calibration and joint modelling are the preferred methods in assessing the effect of biomarker variability. Two-stage methods with sample-based measures should be used with caution unless there exists a relatively long series of longitudinal measurements and/or strong effect size (NCT00000125).