Accurate assessment of liver function is essential for selecting patients with hepatocellular carcinoma (HCC) undergoing stereotactic body radiotherapy (SBRT). In this study, we examined whether the albumin-bilirubin (ALBI) grade and the Child-Pugh classification provide independent prognostic information when modeled concurrently. A retrospective analysis was conducted on 102 patients with HCC who received initial SBRT (2013-2020) and were classified as ALBI grades 1-2 and Child-Pugh classes A-B. Both classifications were entered simultaneously into a multivariable Cox proportional hazards model adjusted for age, sex, viral hepatitis status, time since HCC diagnosis, and prior local treatments. Model discrimination was evaluated using the concordance index (C-index) and the Akaike information criterion (AIC). The median follow-up was 52 months, and the median overall survival (OS) was 71 months. In the multivariable analysis, both ALBI grade 2 [hazard ratio (HR)=2.20; 95%confidence interval (CI)=1.07-4.51; p=0.032] and Child-Pugh class B (HR=3.70; 95%CI=1.77-7.72; p<0.001) were independently associated with poorer OS. The combined model incorporating both classifications showed improved discrimination compared with either classification alone (C-index, 0.7283 vs. 0.6976 and 0.7104; ΔAIC ≥2.85), indicating a complementary prognostic value. ALBI grade worsening at six months identified nonclassic radiation-induced liver disease in 21.5% of patients versus 7.5% identified by a ≥2-point increase in the Child-Pugh score, with 90% nonoverlap between criteria. ALBI grade 2 and Child-Pugh class B were independently associated with OS, with neither exhibiting clear superiority. Their combined use - ALBI grade for objective biochemical stratification and Child-Pugh for clinical assessment incorporating ascites and encephalopathy - may provide a more comprehensive evaluation than either system alone.

Lung injury risk curves for behind armor blunt trauma using the abbreviated injury scoring system.

This study aimed to investigate the feasibility of MR diffusion imaging of fresh prostate needle biopsy cores in a 14 Tesla vertical bore NMR system. Biopsies sampled from the index tumor and a presumed tumor-free area of eight prostatectomy specimens underwent diffusion-weighted imaging using conventional pulsed gradient spin echoes (PGSE) and stimulated echo acquisition mode (STEAM) with different gradient separation times between 10 and 400 ms at nominal b values ranging from 0 up to 5000 s/mm2. The apparent diffusion coefficient (ADC) was determined over the b-value range 0-1000 s/mm2. Kurtosis and biexponential models were fitted to measured signals at effective b values within the range 0-2000 and 0-5000 s/mm2, respectively. Moreover, monoexponential models were fitted over the same b-value ranges. After imaging, all biopsies underwent histopathological evaluation. For all diffusion gradient separation times evaluated, the ADC of histopathologically confirmed tumor and normal tissue was not significantly different (p = 0.140 at 10 ms gradient separation time). Irrespective of the investigated tissue type, for an increasing gradient separation time up to 150 ms ADC decreased. For longer times up to 400 ms no further change was evident. For short diffusion gradient separation times, the Akaike information criterion (AIC) favored the biexponential model in almost all voxels, but for longer diffusion gradient separation times, in a majority of voxels the AIC favored the monoexponential model over either the kurtosis or biexponential model. Imaging of fresh prostate biopsies is feasible. At short diffusion times, in agreement with the presence of at least two distinct compartments, the signal decay is well described by a biexponential model. At long diffusion times, the ADC is lower, and a monoexponential model tends to be more appropriate, indicating the presence of exchange between compartments.

Determining the long-term risk of arthritis in patients with anterior cruciate ligament (ACL) injury treated nonoperatively versus those who undergo ACL reconstruction (ACLR) remains an important and unanswered question for patients and surgeons. (1) To define the cumulative arthritis rate and severity after nonsurgical management of ACL injury-the chronically ACL-deficient (ACLD) knee; (2) to compare rates and severity of arthritis in patients who have ACLD knee with similar patients who underwent ACLR; and (3) to identify clinically relevant risk factors for arthritis. Systematic review; Level of evidence, 3. Three databases (Medline, Embase, PubMed) were searched for primary studies examining radiographic outcomes in patients with chronic ACL deficiency (>12 months of ACL deficiency). Studies with a matched ACLR control group were included. Quality assessment was performed with the MINORS (Methodological Index for Nonrandomized Studies) tool. Arthritis prevalence over time was plotted and modeled to best-fit using the Akaike information criterion. Data were extracted for meta-analysis for the primary outcome of osteoarthritis. The cumulative odds ratio of prognostic factors was calculated where appropriate. Nineteen full-text studies met inclusion criteria (11 matched cohort studies comparing ACLD and ACLR) including 1432 patients with a mean 11.1 years of follow-up after injury. The methodological quality of included studies was moderate. The pooled rate of radiographic arthritis in ACLD patients was 37.8%; the rate of moderate to severe arthritis was 18.1% (compared with 35.2% and 12.8% in patients with ACLR, respectively, and 5.0% in the nonoperated knee). An increase in the rate of arthritis was observed, accelerating sharply at 10 years after injury. ACLR and ACLD knees had similar prevalence of mild arthritis (P = .60), irrespective of activity level. Joint degeneration was significantly accelerated by meniscectomy in ACLD patients in most studies. Patients with a chronically ACLD knee may be at an increased predisposition for developing moderate to severe arthritis but not mild arthritis compared with matched patients who undergo ACLR. Meniscectomy is a key predictor of worsened severity of osteoarthritis.

Major adverse cardiovascular events (MACE) are a leading cause of morbidity and mortality in patients with end-stage renal disease (ESRD). However, risk stratification and prognostic prediction remain limited. To assess the incremental prognostic value of combined left atrial (LA) and left ventricular (LV) strain in predicting MACE among ESRD patients receiving renal replacement therapy. Prospective. Three hundred thirteen ESRD patients (mean age: 53.8 ± 14.0 years; 202 males) undergoing maintenance dialysis. Balanced steady-state free precession (bSSFP) cine sequence at 3.0 T. Myocardial strain was analyzed from bSSFP cine images using feature-tracking software (CVI42). LA strain components were reservoir (LARS), conduit (LAScd), and contractile (LASct) strain, and LV strain included global longitudinal (GLS), radial (GRS), and circumferential (GCS) strain. Patients were followed up via clinical records and MACE were documented. Prognostic models were constructed using multivariable Cox proportional hazards regression. The baseline prediction model of conventional cardiovascular risk factors was then compared with models incorporating LARS and GLS to assess incremental prognostic value. Cox proportional hazards regression identified predictors of MACE, and model performance was evaluated using C-index, Akaike and Bayesian information criteria (AIC/BIC), and Kaplan-Meier analysis. p < 0.05 was considered significant. During a median follow-up of 16.93 months, 61 patients developed MACE. LARS (hazard ratio (HR) 0.90, 95% confidence interval (CI) 0.87-0.94) and LV GLS (HR 1.21, 95% CI 1.08-1.36) were independent predictors. The Cox model incorporating both LARS and LV GLS showed improved discrimination compared with the clinical risk factor model (C-index 0.79 vs. 0.70). Stratification by both LA and LV strain markers significantly improved MACE prediction (log-rank: p < 0.001). The integration of LA and LV strain offered superior prognostic value for MACE prediction in ESRD patients, enabling refined risk stratification beyond traditional measures. 2. Stage 3.

Sustainable utilization of wild medicinal plant resources requires reproducible approaches for locating and managing natural populations. In Japan, exploration and resource planning for wild medicinal plants rely heavily on expert knowledge, and reproducible approaches remain scarce. We developed a species distribution model (SDM) for Uncaria rhynchophylla (Rubiaceae), a botanical source of the crude drug Uncaria Hook used in Kampo medicine, to estimate its potential distribution and identify major environmental correlates in the Kyushu region, southern Japan. Using 122 occurrence records collected from 2021 to 2023 and nine environmental predictors, MaxEnt models were trained with background points and bootstrap replicates. Because mean minimum winter temperature in January and mean maximum summer temperature in August were highly correlated, three candidate models were compared using the small-sample corrected Akaike information criterion (AICc), test omission rates, and test area under the receiver operating characteristic curve (AUC). The model including winter minimum temperature was best supported (test AUC = 0.82; test omission rate = 24%), whereas adding summer maximum temperature provided limited improvement (ΔAICc = + 75.75). Variable importance and jackknife tests ranked winter minimum temperature as the most influential predictor, followed by slope angle and distance to the nearest rivers. The suitable area, defined using the maximum training sensitivity plus specificity (MTSS) threshold, covered 10,595km² (25.9% of the terrestrial area) and formed continuous zones across hilly and low-montane regions. Targeted surveys in high-suitability areas confirmed three additional sites in the Kirishima Mountains. Overall, integrating SDM and geographic information systems provides a reproducible, data-driven, decision-support framework for the exploration, planning of sustainable harvesting, and resource management of wild medicinal plants.

Coronary heart disease(CHD) is an important cause of cardiovascular diseases and, under adverse conditions such as arteriosclerosis, will raise the risk of death significantly. Due to the shortcomings of traditional adiposity indices when assessing aerobic capacity (AC), this paper intended to explore whether the novel cardiometabolic index (CMI) could predict lower AC in patients with coronary heart disease. A retrospective analysis was performed on 378 hospitalized patients with coronary heart disease who underwent treatment and completed cardiopulmonary exercise testing. Patients were grouped according to AC levels and CMI levels, and the baseline characteristics of both groups were assessed.Three multivariable binary logistic regression models were constructed to evaluate the associations of the CMI, body mass index (BMI), and waist-to-height ratio (WHtR) with reduced AC. Receiver operating characteristic (ROC) curve analysis was performed to assess and compare the predictive performance of the three models, including the area under the curve (AUC). Model fit was further evaluated using the - 2 log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Calibration of each model was assessed using the Hosmer-Lemeshow test, and DeLong's test was used to compare differences in AUC values between models. Three multivariable logistic regression models were constructed, each incorporating different core variables (BMI, WHtR, and CMI) along with other relevant covariates. Variance inflation factor (VIF) analysis confirmed the absence of significant multicollinearity among variables. Model performance evaluation demonstrated that Model 3, which included CMI, had the best fit, with the lowest - 2 log-likelihood, AIC, and BIC values, good calibration (Hosmer-Lemeshow test, P = 0.441), and the highest AUC (0.671, 95% CI: 0.605-0.736). However, DeLong's test revealed that the difference in AUC between Model 3 and Model 1, as well as between Model 3 and Model 2, did not reach statistical significance (P = 0.062 and P = 0.110, respectively). CMI demonstrates clinical potential in evaluating the decline of AC in patients with CHD.

Role of balance and physical fitness in assessing the effectiveness of cardiac rehabilitation on functional recovery

Psoriatic arthritis (PsA) and low physical activity (PA) negatively influence body composition. We examined the relative strength of associations of PA and PsA with body composition in individuals with PsA compared to controls, and comparing PA assessment by self-report versus a device. We analyzed data from 142 individuals with PsA and 23858 controls who participated in the fourth survey of the Trøndelag Health study (HUNT4, 2017-2019). Standardized multivariable linear regression models were used to assess the relative strength of associations of PsA (CASPAR criteria) and PA with visceral fat mass and percentage body fat measured by bioelectrical impedance analysis. PA was either self-reported (exercise frequency, duration and intensity) or device-measured using accelerometers. Model fit was compared using Akaike's Information Criterion and the Bayesian Information Criterion. Standardized regression coefficients for PsA had much smaller absolute values than those for performing PA in explaining variations in body composition (visceral fat mass: PsA: 0.017 to 0.019, PA: -0.14 to -0.31; percentage body fat: PsA 0.013 to 0.015, PA: -0.12 to -0.29). Unstandardized models showed that performing moderate to high levels of PA could compensate for the unfavorable body composition associated with PsA. Models with device-measured PA had substantially better fit than those with self-report, indicating that they better captured the variations in body composition. PA explained larger variations in body composition than PsA. Individuals with PsA should be informed that engaging in PA has a greater positive impact on body composition than the negative consequences of having PsA. Key Points • Low physical activity was more strongly associated with an unfavourable body composition than psoriatic arthritis. • Device-measured physical activity more accurately captured the associations between physical activity and body composition than self-reported measures. • Promoting physical activity and perhaps use of wearable devices to record it may potentially help individuals with psoriatic arthritis mitigate the unfavorable body composition associated with their arthritis.

Temporal dynamics of atrial fibrillation follow power-laws: evidence for self-organised criticality

This work presents a unified framework for the parametrization and comparison of microbial survival models under UVC exposure. Four dose-response models-the single-target, multi-target, linear-quadratic, and two-stage decay models-are analyzed with respect to their mathematical structure, parameter identifiability, and biological interpretability, while ensuring physically meaningful parameter estimates. To address the limited size and lack of replication in published datasets, parameter uncertainty is quantified using a parametric bootstrap approach under multiplicative dose uncertainty. Model comparison combines goodness-of-fit in logarithmic survival space, the Akaike Information Criterion corrected for small samples, and identifiability considerations. Application to data for 32 microorganisms shows that no single model is universally optimal, highlighting that reliable model selection requires combining statistical performance with physical and biological consistency, and providing a robust basis for UVC survival analysis and disinfection modeling.

Which Carcinogenicity Study Should I use? Automated Identification of Reliable Studies.

This study investigated the relationship between Corvis ST tonometry parameters and mean blur rate in the optic nerve head tissue area (MBR-T) in healthy eyes. Corneal biomechanical properties and MBR-T were measured in 100 eyes from 56 healthy participants in Japan using Corvis ST tonometry and laser speckle flowgraphy, respectively. Axial length, central corneal thickness, and intraocular pressure were also recorded. Linear mixed-effects models were applied to evaluate the associations between corneal biomechanical properties and MBR-T. Model selection was performed using the second-order bias-corrected Akaike Information Criterion (AICc). The optimal predictive model for MBR-T was as follows: MBR-T = 21.1607 (intercept) – 1.6317 × Sex (Male) – 4.8626 × A2 deformation amplitude – 5.7932 × HC deformation amplitude (AICc = 409.9). In healthy eyes, greater corneal deformability was associated with lower MBR-T.

Prognostic Value of Coronary Sinus Flow and Aortic Pressure Gradient Quantified by 4D Flow CMR in AMI.

The International Prognostic Index (IPI) and IPI related prognostic indexes are widely used for risk-stratification in lymphoma. However, identifying poor prognostic group patients remains a significant objective. This study aimed to develop a predictive prognostic model for DLBCL treated with R-CHOP immunochemotherapy. A cohort comprising 167 individuals newly diagnosed with DLBCL between January 2016 and June 2021 at Jiangsu cancer hospital, Nanjing Medical University were enrolled for investigation. Univariate and multivariate Cox analysis were used for variable selection. The Akaike information criterion (AIC) guided the selection of factors for constructing the nomograms, along with a novel prognostic index for assessing both progression-free survival (PFS) and overall survival (OS). Internal validation was performed with the bootstrap method(B = 1000). Age, Lactate dehydrogenase (LDH), stage, extra-nodal sites and absolute CD4 + T cell counts (ACD4C) were associated with both PFS and OS. These discerned prognostic factors were subsequently employed in constructing nomograms for PFS and OS, respectively. The C-indexes of Internal validation performed with the Bootstrap method were 0.76 (PFS: 95%CI 0.67-0.80),0.81(OS:95%CI 0.72-0.85), respectively. The calibration plots, alongside internal bootstrap resampling, demonstrated commendable consistency between predictions and observations. For enhanced clinical applicability, we devised a novel immune prognostic index, categorizing DLBCL patients into four distinct risk groups: low, low-intermediate, high-intermediate, and high risk, corresponding to 0, 1-2, 3, 4-5 risk factors respectively. Kaplan-Meier analysis underscored the superior discriminatory capacity of the immune index in assessing the prognosis across various risk groups. The proposed immune index is a useful tool to predict the prognosis of DLBCL patients treated with R-CHOP immunochemotherapy in this study.

Colorectal neuroendocrine neoplasms (CR-NENs) are traditionally classified into midgut and hindgut derivatives, a binary classification that potentially oversimplifies the underlying developmental continuum. We investigated whether CR-NEN phenotype and prognosis follow a continuous proximal-to-distal gradient to identify the optimal stratification strategy. We analyzed 1,158 patients from the RGETNE registry. Anatomical location was modeled as a continuous variable using restricted cubic splines to dissect spatial trends in proliferation (Ki-67), differentiation, and survival. Prognostic performance of the continuous spatial model was formally compared with categorical approaches (right/left/rectum and midgut/hindgut) using the Akaike Information Criterion (AIC). A distinct proximal-to-distal gradient was observed. The continuous spline-based model provided the superior fit for Ki-67 (R2=0.494), outperforming categorical approaches. Anatomical position was an independent prognostic factor, with risk increasing distally in localized disease (HR 1.79; 95% CI 1.31-2.45 for rectum vs. cecum) and significant mortality differences in metastatic disease (p<0.001). Adjuvant benefit was also heterogeneously distributed, being concentrated in distal segments. Despite the biological continuum, the embryological midgut-hindgut dichotomy yielded the most efficient statistical fit (lowest AIC) for predicting clinical recurrence. CR-NENs exhibit a topographical biological continuum. Continuous modeling is optimal for molecular research and characterizing tumor aggressiveness. However, for clinical risk stratification and trial design, the traditional discrete classifications (midgut-hindgut or anatomical segments) remain the most robust, parsimonious, and practically applicable surrogates.

The Cox proportional hazards (PH) model is the most common approach for analyzing time-to-event data in public health. However, its reliance on the proportional hazards' assumption is a key limitation, often violated in demographic research. This study illustrates a comprehensive model selection framework, comparing the performance of various parametric accelerated failure time (AFT) and frailty models as robust alternatives when the PH assumption fails, using data on the timing of first birth as a case study. We conducted a secondary analysis of time to first birth among 9,955 women from the 2015 Zimbabwe Demographic and Health Survey. After confirming violations of the PH assumption using Schoenfeld residuals, we systematically compared the fit of several parametric models' exponential, Weibull, log-normal, and log-logistic with and without gamma-shared frailty terms. Model selection was based on the Akaike (AIC) and Bayesian (BIC) Information Criteria. The log-logistic AFT model with gamma frailty showed improved relative fit for this dataset, accommodating a non-monotonic hazard and accounting for unobserved regional heterogeneity (θ = 0.671, p<0.001). The model yielded interpretable time ratio (TR) estimates, where higher education (TR=1.11) and later cohabitation (TR=1.25) were associated with delayed first birth, while marriage (TR=0.98) and contraceptive use (TR=0.97) were associated with earlier timing. In contrast, standard parametric models and the Cox model were suboptimal for this dataset. This study provides an empirical demonstration of how AFT models with frailty terms can be applied in settings where the proportional hazards assumption is violated. The log-logistic frailty model showed improved relative fit for this dataset and offers an interpretable framework for analysing time-to-event outcomes with clustered data.

The belief that the moon disturbs sleep is widespread, but the factors associated with it remain poorly understood. I therefore examined how frequently poor sleep is attributed to moon phases, whether this varied across the lunar cycle, and which personal and environmental factors were associated with "moon blaming". Data were derived from an ongoing online survey. At the time of analysis, 1815 participants had completed a 16-item questionnaire assessing sleep quality, sleep duration, sleep timing on workdays and free days, alarm clock use, environmental and personal sleep-disturbing factors, residential setting, age, gender, attention to lunar phases, and whether the moon was perceived as a cause of poor sleep. The primary outcome was endorsement of the moon as a sleep-disturbing factor. Logistic regression with stepwise Akaike information criterion selection was used to identify the strongest predictors of attributing the moon for poor sleep. Questionnaire timing was also examined across the lunar cycle. Among environmental factors, the moon was the most frequently endorsed cause of poor sleep (36%), followed by outdoor temperature (31%), indoor noise (26%), and bad weather (22%). Rumination was the most commonly reported personal factor (73%), but it did not predict moon attribution. Instead, the strongest correlates were weather-related sleep complaints, tracking lunar phases, age, and gender, with endorsement increasing with age and being more common among women. Moon-related complaints also peaked during the week after the full moon. These findings suggest that perceived lunar effects on sleep are shaped, at least in part, by attributional and expectation-related processes.

Current approaches for preoperative cardiovascular risk prediction remain suboptimal. CT-derived body composition metrics may provide objective markers of cardiometabolic health, yet their predictive value for postoperative cardiovascular events remains unclear. We included patients with cardiovascular disease or risk factors undergoing major noncardiac surgery in the prospective, multicenter PREVENGE-CB (PREdiction of Vascular Events after Noncardiac surGEry with Cardiac Biomarkers) cohort and its Nanfang extension. Preoperative abdominal CT scans were analyzed to quantify the area and radiodensity of skeletal muscle and adipose tissues at the third lumbar vertebral level. The primary outcome was composite cardiovascular events within 30 days after surgery. We used logistic regression models to evaluate the added predictive value of body composition metrics beyond guideline-recommended predictors. In the Nanfang cohort, the optimal subset of body composition metrics was selected by minimizing the Akaike Information Criterion for the primary outcome. Nested models were compared using measures of model fit, discrimination, risk reclassification, and net benefit. The findings were validated in the PREVENGE-CB cohort. Among 1594 patients, 211 (13.2%) had the primary outcome. Larger skeletal muscle and adipose areas were generally associated with lower risk, whereas higher adipose radiodensity and lower muscle radiodensity indicated higher risk. In the Nanfang cohort, an optimal subset of three body composition metrics-skeletal muscle area, muscle radiodensity and subcutaneous fat radiodensity-improved discrimination of the primary outcome over the Revised Cardiac Risk Index (increase in area under the curve [ΔAUC] = 0.136; 95% CI, 0.083 to 0.188), the Gupta Myocardial Infarction and Cardiac Arrest risk calculator (ΔAUC = 0.032; -0.002 to 0.065), and a refitted clinical model (ΔAUC = 0.035; 0.008 to 0.062). These findings were validated in the PREVENGE-CB cohort. CT-derived body composition metrics improved prediction of postoperative cardiovascular events beyond conventional clinical predictors.

Neoadjuvant chemotherapy for gastric cancer is assessed by RECIST but overlooks microscopic changes; incorporating CEA dynamics may improve evaluation accuracy. Retrospective analysis of 600 locally advanced gastric cancer (LAGC) patients (training:457; validation:143) from nine tertiary hospitals. The Biochemical and Radiologic Response System (BRRS) integrated RECIST 1.1-stratified radiological responses and peri-NAT carcinoembryonic antigen (CEA) dynamics. Predictive performance was evaluated using receiver operating characteristic (ROC) curves(AUC), time-dependent ROC, Akaike(AIC) and Bayesian(BIC) information criteria. The BRRS was developed through Kaplan-Meier analysis of biochemical/radiologic responses to NACT, classifying 457 training-cohort patients into dual (DR, n = 168), single (SR, n = 185), and no responders (NR, n = 104).Three-year overall survival (OS) rates were 74.4% (DR), 60.6% (SR), and 35.0% (NR) (all P < 0.001). ypTNM 3-year OS rates were 96.4% (stage 0), 82.1% (stage I), 72.7% (stage II), 42.5% (stage III) (I vs. 0: P = 0.405; II vs. I: P = 0.122; III vs. II: P < 0.001), and tumor regression grade(TRG) 3-year OS rates were 93.6, 74.1, 45.9, 46.7% (TRG1 vs. 0:P = 0.060; TRG2 vs. 1:P = 0.001; TRG3 vs. 2:P = 0.583). BRRS (AUC = 0.708, AIC = 992.57, BIC = 995.20) outperformed ypTNM (AUC = 0.678, AIC = 1007.18, BIC = 1008.81) and TRG (AUC = 0.661, AIC = 1012.03, BIC = 1015.65) in OS prediction. Adjuvant chemotherapy improved OS/disease-free survival (DFS) in SR (OS:62.9 vs. 41.4%, P = 0.041; DFS:55.7 vs. 31.6%, P = 0.020) and NR groups (OS:39.1 vs. 16.7%, P = 0.030; DFS: 36.1 vs. 11.9%, P = 0.019) but not in DR (OS: 75.4 vs. 66.7%, P = 0.765; DFS: 66.7 vs. 71.4%, P = 0.455). The results from the validation cohort were consistent. BRRS outperformed conventional ypTNM and TRG systems in prognostication. Postoperative chemotherapy may be omitted in DR patients.