Evaluating Prognostic Value of Dynamics of Circulating Lactate Dehydrogenase in Colorectal Cancer Using Modeling and Machine Learning

Pretreatment serum lactate dehydrogenase (LDH) levels have been associated with poor prognosis in several types of cancer, including metastatic colorectal cancer (mCRC). However, very few models link survival to longitudinal LDH measured repeatedly over time during treatment. We investigated the prognostic value of on-treatment LDH dynamics in mCRC. Using data from two large phase III studies (2L and 3L+ mCRC settings, n = 824 and 210, respectively), we found that integrating longitudinal LDH data with baseline risk factors significantly improved survival prediction. Current LDH values performed best, enhancing discrimination ability (area under the receiver operating characteristic curve) by 4.5~15.4% and prediction accuracy (Brier score) by 3.9~15.0% compared with baseline variables. Combining all longitudinal LDH markers further improved predictive performance. After controlling for baseline covariates and other longitudinal LDH indicators, current LDH levels remained a significant risk factor in mCRC, increasing mortality risk by over 90% (P < 0.001) in 2L patients and 60-70% (P < 0.01) in 3L+ patients per unit increment in current log (LDH). Machine-learning techniques, like functional principal component analysis (FPCA), extracted informative features from longitudinal LDH data, capturing over 99% of variability and allowing prediction of survival. Unsupervised clustering based on the extracted FPCA features stratified patients into three groups with distinct LDH dynamics and survival outcomes. Hence, our approaches offer a valuable and cost-effective way for risk stratification and improves survival prediction in mCRC using LDH trajectories.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance

PURPOSE

To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment.

MATERIALS AND METHODS

Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed.

RESULTS

Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes.

CONCLUSION

Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers Using Sequencer Architecture

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. This study developed a novel and efficient digital pathology classifier called DPSeq to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizontal and vertical bidirectional long short-term memory networks. Using hematoxylin and eosin-stained histopathologic images of colorectal cancer from two international data sets (The Cancer Genome Atlas and Molecular and Cellular Oncology), the predictive performance of DPSeq was evaluated in a series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in colorectal cancer (microsatellite instability status, hypermutation, CpG island methylator phenotype status, BRAF mutation, TP53 mutation, and chromosomal instability), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. In addition, under the same experimental conditions using the same set of training and testing data sets, DPSeq surpassed four CNNs (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and two transformer (Vision Transformer and Swin Transformer) models, achieving the highest area under the receiver operating characteristic curve and area under the precision-recall curve values in predicting microsatellite instability status, BRAF mutation, and CpG island methylator phenotype status. Furthermore, DPSeq required less time for both training and prediction because of its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

[Clinical characteristics and prognosis of patients with therapy-related myelodysplastic syndrome and acute myeloid leukemia arising from malignant tumors]

Objective: To investigate the clinical characteristics, cytogenetics, molecular biology, treatment, and prognosis of patients with therapy-related myelodysplastic syndrome and acute myeloid leukemia (t-MDS/AML) secondary to malignancies. Methods: The clinical data of 86 patients with t-MDS/AML in West China Hospital of Sichuan University between January 2010 and April 2023 were retrospectively analyzed. The clinical characteristics, primary tumor types, and tumor-related therapies were analyzed. Results: The study enrolled a total of 86 patients with t-MDS/AML, including 67 patients with t-AML, including 1 patient with M(0), 6 with M(1), 27 with M(2), 9 with M(3), 12 with M(4), 10 with M(5), 1 with M(6), and 1 with M(7). Sixty-two patients could be genetically stratified, with a median overall survival (OS) of 36 (95% CI 22-52) months for 20 (29.9%) patients in the low-risk group and 6 (95% CI 3-9) months for 10 (14.9%) in the intermediate-risk group. The median OS time was 8 (95% CI 1-15) months in 32 (47.8%) patients in the high-risk group. For patients with non-acute promyelocytic leukemia (APL) and AML, the median OS of the low-risk group was 27 (95% CI 18-36) months, which was significantly longer than that of the non-low-risk group (χ(2)=5.534, P=0.019). All 9 APL cases were treated according to the initial treatment, and the median OS was not reached, and the 1-, 2-, and 3-year OS rates were 100.0%, (75.0±6.2) %, and (75.0±6.2) % respectively. Of the 58 patients with non-APL t-AML (89.7%), 52 received chemotherapy, and 16 achieved complete remission (30.8%) after the first induction chemotherapy. The 1-, 2-, and 3-year OS rates of the non-APL t-AML group were (42.0 ± 6.6) %, (22.9±5.7) %, and (13.4±4.7) %, respectively. The median OS of patients who achieved remission was 24 (95% CI 18-30) months, and the median OS of those who did not achieve remission was 6 (95% CI 3-9) months (χ(2)=10.170, P=0.001). Bone marrow CR was achieved in 7 (53.8%) of 13 patients treated with vineclar-containing chemotherapy, with a median OS of 12 (95% CI 9-15) months, which was not significantly different from that of vineclar-containing chemotherapy (χ(2)=0.600, P=0.437). In 19 patients with t-MDS, the 1-, 2-, and 3-year OS rates were (46.8±11.6) %, (17.5±9.1) %, and (11.7±9.1) % with a median OS of 12 (95% CI 7-17) months, which was not significantly different from that in t-AML (χ(2)=0.232, P=0.630) . Conclusions: Breast cancer, bowel cancer, and other primary tumors are common in patients with t-MDS/AML, which have a higher risk of adverse genetics. Patients with APL had a high induction remission rate and a good long-term prognosis, whereas patients without APL had a low remission rate and a poor long-term prognosis.

Waist-to-Height Ratio Is a Stronger Mediator in the Association between DASH Diet and Hypertension: Potential Micro/Macro Nutrients Intake Pathways

Several studies have demonstrated that adhering to the Dietary Approaches to Stop Hypertension (DASH) diet may result in decreased blood pressure levels and hypertension risk. This may be an effect of a reduction in central obesity. In the current study, we explored the mediation role of multiple anthropometric measurements in association with DASH score and hypertension risk, and we investigated potential common micro/macro nutrients that react with the obesity-reduction mechanism. Our study used data from the National Health and Nutrition Examination Survey (NHANES). Important demographic variables, such as gender, race, age, marital status, education attainment, poverty income ratio, and lifestyle habits such as smoking, alcohol drinking, and physical activity were collected. Various anthropometric measurements, including weight, waist circumference, body mass index (BMI), and waist-to-height ratio (WHtR) were also obtained from the official website. The nutrient intake of 8224 adults was quantified through a combination of interviews and laboratory tests. We conducted stepwise regression to filter the most important anthropometric measurements and performed a multiple mediation analysis to test whether the selected anthropometric measurements had mediation effects on the total effect of the DASH diet on hypertension. Random forest models were conducted to identify nutrient subsets associated with the DASH score and anthropometric measurements. Finally, associations between common nutrients and DASH score, anthropometric measurements, and risk of hypertension were respectively evaluated by a logistic regression model adjusting for possible confounders. Our study revealed that BMI and WHtR acted as full mediators between DASH score and high blood pressure levels. Together, they accounted for more than 45% of the variation in hypertension. Interestingly, WHtR was found to be the strongest mediator, explaining approximate 80% of the mediating effect. Furthermore, we identified a group of three commonly consumed nutrients (sodium, potassium, and octadecatrienoic acid) that had opposing effects on DASH score and anthropometric measurements. These nutrients were also found to be associated with hypertension in the same way as BMI and WHtR in univariate regression models. The most important among these nutrients was sodium, which was negatively correlated with the DASH score (β = -0.53, 95% CI = -0.56~-0.50, p < 0.001) and had a positive association with BMI (β = 0.04, 95% CI = 0.01~0.07, p = 0.02), WHtR (β = 0.06, 95% CI = 0.03~0.09, p < 0.001), and hypertension (OR = 1.09, 95% CI = 1.01~1.19, p = 0.037). Our investigation revealed that the WHtR exerts a greater mediating effect than BMI on the correlation between the DASH diet and hypertension. Notably, we identified a plausible nutrient intake pathway involving sodium, potassium, and octadecatrienoic acid. Our findings suggested that lifestyle modifications that emphasize the reduction of central obesity and the attainment of a well-balanced micro/macro nutrient profile, such as the DASH diet, could potentially be efficacious in managing hypertension.

A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations

Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpretability, but suffer from computation complexity. This paper presents a novel, lightweight Attention-based Multiple Instance Mutation Learning (AMIML) model to allow small-scale attention operations for predicting gene mutations. Compared to the standard self-attention model, AMIML reduces the number of model parameters by approximately 70%. Using data for 24 clinically relevant genes from four cancer cohorts in TCGA studies (UCEC, BRCA, GBM, and KIRC), we compare AMIML with a standard self-attention model, five other deep learning models, and four traditional machine learning models. The results show that AMIML has excellent robustness and outperforms all the baseline algorithms in the vast majority of the tested genes. Conversely, the performance of the reference deep learning and machine learning models vary across different genes, and produce suboptimal prediction for certain genes. Furthermore, with the flexible and interpretable attention-based pooling mechanism, AMIML can further zero in and detect predictive image patches.

[Repair methods of complex facial defect wounds involving paranasal sinuses and their clinical effectiveness]

Objective: To explore the repair methods of complex facial defect wounds involving paranasal sinuses and their clinical effectiveness. Methods: A retrospective observational study was conducted. From January 2020 to May 2022, 5 patients admitted to the Department of Burns and Plastic Surgery of Xiangya Hospital of Central South University and 4 patients admitted to the Department of Burns and Plastic Surgery of Chenzhou First People's Hospital with complex facial defect wounds involving paranasal sinuses met the inclusion criteria, including 6 males and 3 females, aged 35-69 years, including 4 patients with titanium mesh exposure combined with paranasal sinuses injury and 5 patients with tumor involving paranasal sinuses. After an adequate assessment of the damage by a multiple discipline team, titanium mesh removal, paranasal sinus debridement, and paranasal sinus mucosa removal were performed in patients with exposed titanium mesh, and radical tumor resection was performed in patients with tumors, with postoperative skin and soft tissue defects areas of 5.0 cm×2.5 cm to 18.0 cm×7.0 cm, anterior paranasal sinus wall defects/absence areas of 3 cm×2 cm to 6 cm×4 cm, and sinus cavity depths of 1 to 4 cm. Depending on the perforator course of the descending branch of the lateral circumflex femoral artery, the anterolateral femoral chimeric flap or anterolateral femoral myocutaneous flap (with flap area of 9 cm×4 cm to 19 cm×8 cm, muscle size of 5 cm×3 cm×3 cm to 11 cm×6 cm×3 cm) was transplanted to repair the defect, and the donor site wound was sutured directly. The type of tissue flap transplanted, the blood vessel of the recipient area, and the vascular anastomosis way during the operation, the recovery of the donor and recipient areas and the occurrence of complications after operation were observed. The appearance and blood supply of the recipient area and the recurrence of ulcers and tumors were followed up. Results: The anterolateral femoral myocutaneous flap transplantation was performed in 6 patients, and the anterolateral femoral chimeric flap transplantation was performed in 3 patients. The blood vessels in recipient areas were facial arteries and veins in 3 cases and superficial temporal arteries and veins in 6 cases. The superficial temporal arteries and veins were bridged with blood vessels in tissue flaps by flow-through way in 2 patients, and end-to-end anastomosis of blood vessels in donor and recipient areas was performed in 7 patients. After operation, all the tissue flaps survived, and the facial defect wounds were well repaired without cerebrospinal fluid leakage or paranasal sinus secretion leakage, no intracranial infection occurred, and the wounds in donor areas were healed well. Follow-up of 6-35 months after operation showed that all the patients had good blood supply in the recipient area, and the shape was acceptable; 4 patients with exposed titanium mesh had no recurrence of ulceration, and 5 patients with tumor had no local tumor recurrence or metastasis. Conclusions: Based on an adequate assessment of the extent of paranasal sinuses involved in the facial wound and the nature of the defect, good clinical effects can be achieved by using the anterolateral femoral muscle flap or the anterolateral femoral chimeric flap transplantation to repair complex facial defect wounds with open paranasal sinuses.

Clearance as an Early Indicator of Efficacy for Therapeutic Monoclonal Antibodies: Circumventing Dose Selection Challenges in Oncology

BACKGROUND AND OBJECTIVE

The designs of first-in-human (FIH) studies in oncology (e.g., 3 + 3 dose escalation design) usually do not provide a sufficient sample size to determine the dose-response relationship for efficacy. This study aimed to assess the feasibility of using monoclonal antibody (mAb) clearance as a biomarker for efficacy to facilitate the identification of potentially efficacious doses across cancer types and drug targets.

METHODS

We performed electronic searches of the Drugs@FDA website, the European Medicines Agency website, and PubMed to identify reports of FIH trials of approved mAbs in oncology. The clearance, half-life, and overall response rate (ORR) data for the mAbs at different dose levels were extracted.

RESULTS

Twenty-five approved mAbs were included in this study. As expected, due to the small sample sizes in FIH studies, there was no clear dose-response for ORR. However, we found a clear negative association between mAb clearance and ORR across tumors/drug targets, and a clear negative dose-clearance relationship, with clearance decreasing and saturated at high dose levels. The approved mAb doses (1-25 mg/kg) are approximately 2-fold the saturation doses (1-10 mg/kg). The associated clearance values at the approved doses vary across different cancers and drug targets (0.17-1.56 L/day), while tend to be similar within a disease/drug target. Anti-CD20 mAbs for B-cell lymphomas show a higher clearance (~ 1 L/day) than other cancers and targets (e.g., ~ 0.3 L/day for anti-PD-1).

CONCLUSIONS

Clearance of mAbs can be a tumor/drug target-agnostic biomarker for potential anti-tumor activity as clearance decreases with increasing ORR. Our findings shed important insights into target clearance values that may lead to desired efficacy for different cancers and drug targets, which can be used to guide dose selection for the future development of mAbs during FIH oncology studies.

[Clinical evaluation of platelet-rich plasma in the treatment of early and middle stage knee osteoarthritis under 3.0T MRI T2 mapping sequence]

Objective: To quantitatively evaluate the clinical effect of platelet-rich plasma(PRP) intra-articular injection for early and middle stage knee osteoarthritis(KOA) treatment by 3.0T MRI T2 mapping sequence. Methods: Clinical data of 26 patients with early or middle stage KOA who received treatment from April to December 2021 at Department of Orthopaedic Surgery,the Second Affiliated Hospital,Zhengzhou University were retrospectively analyzed. In total, 8 patients were male and 18 were female,with age of (66.4±12.0)years(range:51 to 94 years). Four patients were bilateral KOA and 22 patients were unilateral KOA.All patients received PRP intra-articular injection. Patients underwent 3.0T MRI T2 mapping sequence scanning pre-treatment,3-month-after and 6-month-after treatment respectively. Those were used to measure and compare T2 values of medial and lateral femoral articular surface and patellofemoral articular surface. Visual analogue scale(VAS) and Western Ontario and McMaster Osteoarthritis Index (WOMAC) score were recorded and evaluated. The results were analyzed using repeated measure ANOVA followed by Bonferroni multiple comparison test.The correlation between WOMAC scores and T2 values at pre-treatment and 6 months post-treatment was analyzed using Pearson correlation test. Results: After treatment, the patients' International Cartilage Regeneration＆Joint Preservation Society(ICRS) classification were partly improved(one case improved from grade Ⅲ to grade Ⅱ, one case improved from grade Ⅱ to grade Ⅰ),and all patients generally improved after treatment in clinical symptoms. Compared with pre-treatment,VAS and WOMAC scores of grade Ⅰ,Ⅱ,and Ⅲ of 6-month after treatment were declined significantly(all P<0.05).The T2 values of articular cartilage declined to varying degrees(the decrease in T2 values was about 2.06 ms in grade Ⅰ, 2.66 ms in grade Ⅱ, and 3.72 ms in grade Ⅲ).Three-month (VAS:4.8±1.3,WOMAC:21.5±4.0) and 6-month (VAS:4.2±1.4,WOMAC:17.2±2.9) after treatment, the VAS and WOMAC score were significantly higher than those before treatment (VAS:6.0±1.2, WOMAC:29.0±2.3) (F=48.846, F=346.746;both P<0.01). Multiple comparisons showed a statistically significant difference between pre-treatment and post-treatment VAS (P<0.01) and it also was significantly different between 3-month and 6-month post-treatment (P<0.01).At 3- and 6-month after treatment,WOMAC scores were significantly different from before treatment.And it also was significantly different between 3-month and 6-month post-treatment (P<0.01).There was a statistically significant improvement in T2 values of patellofemoral articular surface, medial and lateral femoral articular surface at pre-treatment((44.64±4.02)ms,(44.17±3.64)ms and(43.53±3.91)ms) and 3-month ((43.19±3.91)ms,(43.24±3.34)ms and (42.47±3.80)ms), 6-month ((41.49±3.64)ms,(41.83±3.15)ms and (41.10±3.42)ms) after treatment(F=148.845,F=73.657,F=86.268;all P<0.01).The results of the multiple comparisons showed a statistically significant difference in the T2 values of medial and lateral femoral articular surface and patellofemoral articular surface at each time point(all P<0.01).The Pearson correlation analysis suggested that the WOMAC score at pre-treatment was positively correlated with the medial condyle (r=0.856,P<0.01) and the patellofemoral joint surface T2 values (r=0.840,P<0.01);The WOMAC score at 6-month post-treatment was positively correlated with the medial condyle (r=0.731,P<0.01) and the patellofemoral joint surface T2 values (r=0.691,P<0.01). Conclusions: In the treatment of early and mid-stage KOA,MRI T2 mapping sequences are able to indicate the integrity of cartilage morphology and quantitatively evaluate cartilage repair. PRP has a good therapeutic effect on cartilage repair and reconstruction.

Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: achieving state-of-the-art predictive performance with fewer data using Swin Transformer

Many artificial intelligence models have been developed to predict clinically relevant biomarkers for colorectal cancer (CRC), including microsatellite instability (MSI). However, existing deep learning networks require large training datasets, which are often hard to obtain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin Transformer [Swin-T]), we developed an efficient workflow to predict biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, and BRAF and TP53 mutation) that required relatively small datasets. Our Swin-T workflow substantially achieved the state-of-the-art (SOTA) predictive performance in an intra-study cross-validation experiment on the Cancer Genome Atlas colon and rectal cancer dataset (TCGA-CRC-DX). It also demonstrated excellent generalizability in cross-study external validation and delivered a SOTA area under the receiver operating characteristic curve (AUROC) of 0.90 for MSI, using the Molecular and Cellular Oncology dataset for training (N = 1,065) and the TCGA-CRC-DX (N = 462) for testing. A similar performance (AUROC = 0.91) was reported in a recent study, using ~8,000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient when using small training datasets and exhibited robust predictive performance with 200-500 training samples. Our findings indicate that Swin-T could be 5-10 times more efficient than existing algorithms for MSI prediction based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models demonstrated their capability in accurately predicting MSI and BRAF mutation status, which could exclude and therefore reduce samples before subsequent standard testing in a cascading diagnostic workflow, in turn reducing turnaround time and costs.

[The treatment strategies for complications after pancreaticoduodenectomy]

The most common complications after pancreaticoduodenectomy include pancreatic fistula, biliary fistula, delayed gastric emptying, bleeding, and abdominal infection. Although advances in surgery have led to a significant decrease in perioperative mortality in recent years, the risk of complications after pancreaticoduodenectomy remains. Thus, prevention and treatment of various complications are important to improve the prognosis of patients.

Effects of whole-body vibration on reproductive physiology in a rat model of whole-body vibration

Findings from epidemiological studies suggest that occupational exposure to whole-body vibration (WBV) may increase the risk of miscarriage and contribute to a reduction in fertility rates in both men and women. However, workers exposed to WBV may also be exposed to other risk factors that contribute to reproductive dysfunction. The goal of this experiment was to examine the effects of WBV on reproductive physiology in a rat model. Male and female rats were exposed to WBV at the resonant frequency of the torso (31.5 Hz, 0.3 g amplitude) for 4 hr/day for 10 days. WBV exposure resulted in a significant reduction in number of developing follicles, and decrease in circulating estradiol concentrations, ovarian luteinizing hormone receptor protein levels, and marked changes in transcript levels for several factors involved in follicular development, cell cycle, and steroidogenesis. In males, WBV resulted in a significant reduction in spermatids and circulating prolactin levels, elevation in number of males having higher circulating testosterone concentrations, and marked alterations in levels of transcripts associated with oxidative stress, inflammation, and factors involved in regulating the cell cycle. Based upon these findings data indicate that occupational exposure to WBV contributes to adverse alterations in reproductive physiology in both genders that may lead to reduction in fertility.

Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering

Deep learning models are increasingly being used to interpret whole-slide images (WSIs) in digital pathology and to predict genetic mutations. Currently, it is commonly assumed that tumor regions have most of the predictive power. However, it is reasonable to assume that other tissues from the tumor microenvironment may also provide important predictive information. In this paper, we propose an unsupervised clustering-based multiple-instance deep learning model for the prediction of genetic mutations using WSIs of three cancer types obtained from The Cancer Genome Atlas. Our proposed model facilitates the identification of spatial regions related to specific gene mutations and exclusion of patches that lack predictive information through the use of unsupervised clustering. This results in a more accurate prediction of gene mutations when compared with models using all image patches on WSIs and two recently published algorithms for all three different cancer types evaluated in this study. In addition, our study validates the hypothesis that the prediction of gene mutations solely based on tumor regions on WSI slides may not always provide the best performance. Other tissue types in the tumor microenvironment could provide a better prediction ability than tumor tissues alone. These results highlight the heterogeneity in the tumor microenvironment and the importance of identification of predictive image patches in digital pathology prediction tasks.

Improving feature extraction from histopathological images through a fine-tuning ImageNet model

Background

Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology. Pre-trained neural networks based on ImageNet database are often used to extract “off-the-shelf” features, achieving great success in predicting tissue types, molecular features, and clinical outcomes, etc. We hypothesize that fine-tuning the pre-trained models using histopathological images could further improve feature extraction, and downstream prediction performance.

Methods

We used 100 000 annotated H&E image patches for colorectal cancer (CRC) to fine-tune a pre-trained Xception model via a 2-step approach. The features extracted from fine-tuned Xception (FTX-2048) model and Image-pretrained (IMGNET-2048) model were compared through: (1) tissue classification for H&E images from CRC, same image type that was used for fine-tuning; (2) prediction of immune-related gene expression, and (3) gene mutations for lung adenocarcinoma (LUAD). Five-fold cross validation was used for model performance evaluation. Each experiment was repeated 50 times.

Findings

The extracted features from the fine-tuned FTX-2048 exhibited significantly higher accuracy (98.4%) for predicting tissue types of CRC compared to the “off-the-shelf” features directly from Xception based on ImageNet database (96.4%) (P value = 2.2 × 10⁻⁶). Particularly, FTX-2048 markedly improved the accuracy for stroma from 87% to 94%. Similarly, features from FTX-2048 boosted the prediction of transcriptomic expression of immune-related genes in LUAD. For the genes that had significant relationships with image features (P < 0.05, n = 171), the features from the fine-tuned model improved the prediction for the majority of the genes (139; 81%). In addition, features from FTX-2048 improved prediction of mutation for 5 out of 9 most frequently mutated genes (STK11, TP53, LRP1B, NF1, and FAT1) in LUAD.

Conclusions

We proved the concept that fine-tuning the pretrained ImageNet neural networks with histopathology images can produce higher quality features and better prediction performance for not only the same-cancer tissue classification where similar images from the same cancer are used for fine-tuning, but also cross-cancer prediction for gene expression and mutation at patient level.

Quantitative Trait Loci Identification by Estimating the Genetic Model based on the Extremal Samples

Background

In genetic association studies with quantitative trait loci (QTL), the association between a candidate genetic marker and the trait of interest is commonly examined by the omnibus F test or by the t-test corresponding to a given genetic model or mode of inheritance. It is known that the t-test with a correct model specification is more powerful than the F test. However, since the underlying genetic model is rarely known in practice, the use of a model-specific t-test may incur substantial power loss. Robust-efficient tests, such as the Maximin Efficiency Robust Test (MERT) and MAX3 have been proposed in the literature.

Methods

In this paper, we propose a novel two-step robust-efficient approach, namely, the genetic model selection (GMS) method for quantitative trait analysis. GMS selects a genetic model by testing Hardy-Weinberg disequilibrium (HWD) with extremal samples of the population in the first step and then applies the corresponding genetic model-specific t-test in the second step.

Results

Simulations show that GMS is not only more efficient than MERT and MAX3, but also has comparable power to the optimal t-test when the genetic model is known.

Conclusion

Application to the data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort demonstrates that the proposed approach can identify meaningful biological SNPs on chromosome 19.

Fast and efficient correction for population stratification in multi-locus genome-wide association studies

Reducing false discoveries caused by population stratification (PS) has always been a challenge in genome-wide association studies (GWAS). The current literature established several single marker approaches including genomic control (GC), EIGENSTRAT and generalized linear mixed model association test (GMMAT) and multi-marker methods such as LASSO mixed model (LASSOMM). However, the single-marker methods require prespecifying an arbitrary p value threshold in the selection process, likely resulting in suboptimal precision or recall. On the other hand, it appears that LASSOMM is extremely computationally intensive and may not suitable for large-scale GWAS. In this paper, we proposed a simple multi-marker approach (PCA-LASSO) combining principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). We utilize PCA to correct for the confounding effects of PS and LASSO with built-in cross-validation for a data-driven selection. Compared to the current single-marker approaches, the proposed PCA-LASSO provides optimal balance between precision and recall, and consequently superior F₁ scores. Similarly, compared to LASSOMM, PCA-LASSO markedly increases the precision while minimizing the loss of recall, and therefore improves the overall F₁ score in presence of PS. More importantly, PCA-LASSO drastically reduces the computational time by > 1000 times when compared to LASSOMM. We applied PCA-LASSO to a real dataset of Alzheimer's disease and successfully identified SNP rs429358 (Gene APOE4) which has been widely reported to be associated with the onset and elevated risk of Alzheimer's disease. In conclusion, PCA-LASSO is a simple, fast, but accurate approach for GWAS in presence of latent PS.

Stratification of population in NHANES 2009-2014 based on exposure pattern of lead, cadmium, mercury, and arsenic and their association with cardiovascular, renal and respiratory outcomes

BACKGROUND

Environmental exposure to toxic metals is an important risk factor to human health. Traditional methods have examined associations between a health endpoint and exposure to heavy metals by either univariate or multiple regression. In the setting of ubiquitous heterogeneous environmental exposures, statistical methods that incorporate mixed exposures are increasingly relevant and may provide new insight into the association between metal exposure and important cardiovascular, renal and respiratory outcomes.

OBJECTIVE

The objective of this study was to classify the population of National Health and Nutrition Examination Survey (NHANES) into different exposure subgroups using modern unsupervised clustering methods based on lead, cadmium, mercury, and arsenic measured in urine or whole blood, and to assess the association between the identified exposure groups and twelve important health endpoints.

METHODS

We analyzed a sub-cohort of 9662 subjects participating in the 6 cycles (2003-2004 to 2013-2014) of NHANES study. The urine levels of 3 heavy metals (total arsenic, lead, cadmium) and blood levels of 3 heavy metals (lead, cadmium and mercury) were analyzed using a two-step approach. In the first step, we stratified the population into subgroups using unsupervised clustering (k-medoids) based on levels of metals either in urine or in blood. Then, we examine the association between 12 health endpoints and identified exposure subgroups while controlling for age, sex, race/ethnicity, education, smoking status, BMI, and urinary creatinine.

RESULTS

The k-medoids algorithm clustered NHANES population into 2 groups based on either blood or urinary levels of heavy metals. The concentrations of all the three heavy metals were significantly different between the identified groups in blood (p < 2.2e-16) or in urine (p = 0). The group with higher concentrations was defined as the "high-exposure" group, while the group with lower concentrations was defined as "low-exposure" group. Association analysis with health outcomes suggested that the high-exposure group according to either blood or urinary metal levels had significantly higher total mortality (1.63-1.64 times higher, p < 0.0001), mortality caused by malignant neoplasms (2.05-2.62 times higher, p < 0.0002), Gamma-glutamyl transferase (GGT) (1.03-1.05 times higher, p < 0.0001). In addition, the high-exposure group based on blood levels was also significantly associated with SBP, death related to hypertension, heart disease and chronic lower respiratory disease, while the high-exposure group based on urinary concentrations had higher mortality related to nephritis.

CONCLUSIONS

We proposed an unsupervised clustering method to stratify the population into high- and low-exposure groups based on the co-exposure of heavy metals. The high-exposure groups, characterized by higher metal concentrations, had significant higher GGT, SBP, DBP, and mortality rates suggesting the detrimental effects of exposure to these heavy metals. The stratification of the NHANES population based on exposure patterns provides an informative method to study the impact of metal exposures on health outcomes.

Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model

Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.

Early M-Protein Dynamics Predicts Progression-Free Survival in Patients With Relapsed/Refractory Multiple Myeloma

This study aimed to predict long‐term progression‐free survival (PFS) using early M‐protein dynamic measurements in patients with relapsed/refractory multiple myeloma (MM). The PFS was modeled based on dynamic M‐protein data from two phase III studies, POLLUX and CASTOR, which included 569 and 498 patients with relapsed/refractory MM, respectively. Both studies compared active controls (lenalidomide and dexamethasone, and bortezomib and dexamethasone, respectively) alone vs. in combination with daratumumab. Three M‐protein dynamic features from the longitudinal M‐protein data were evaluated up to different time cutoffs (1, 2, 3, and 6 months). The abilities of early M‐protein dynamic measurements to predict the PFS were evaluated using Cox proportional hazards survival models. Both univariate and multivariable analyses suggest that maximum reduction of M‐protein (i.e., depth of response) was the most predictive of PFS. Despite the statistical significance, the baseline covariates provided very limited predictive value regarding the treatment effect of daratumumab. However, M‐protein dynamic features obtained within the first 2 months reasonably predicted PFS and the associated treatment effect of daratumumab. Specifically, the areas under the time‐varying receiver operating characteristic curves for the model with the first 2 months of M‐protein dynamic data were ~ 0.8 and 0.85 for POLLUX and CASTOR, respectively. Early M‐protein data within the first 2 months can provide a prospective and reasonable prediction of future long‐term clinical benefit for patients with MM.

Adaptive weighted sum tests via LASSO method in multi-locus family-based association analysis

Family based multi-locus tests integrate information from individual loci by weighted averaging of the marginal statistics, and have been proven to be more efficient and robust than the single-locus tests in genetic association studies. The power depends on how much information the weights can extract from data. The currently published weighted sum methods are only applicable to either common or rare variants and may suffer from substantial power loss especially for rare variants. In this paper, we propose a novel data-driven weight to improve the power under both common and rare variant circumstances. We use the l₁ regularization in Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the weight serving as a simultaneously adaptive marker selection process. Simulations for a dichotomous phenotype demonstrated that our LASSO-based approach outperformed the existing multi-locus methods in the sense of providing the highest statistical power while well controlled type I error rate under different scenarios. We also applied our methods to a real dataset for rheumatoid arthritis (GAW15 Problem 2). Two groups of alleles, in which individual SNPs had only modest and non-significant effects, were detected (P < 0.00001) using our proposed methods, whereas traditional multi-locus methods failed to identify them. In conclusion, the novel LASSO-based approach represents a superior weight-choosing strategy for multi-locus tests.

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer's Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

A novel quantification of information for longitudinal data analyzed by mixed-effects modeling

Nonlinear mixed-effects (NLME) modeling is one of the most powerful tools for analyzing longitudinal data especially under the sparse sampling design. The determinant of the Fisher information matrix is a commonly used global metric of the information that can be provided by the data under a given model. However, in clinical studies, it is also important to measure how much information the data provide for a certain parameter of interest under the assumed model, for example, the clearance in population pharmacokinetic models. This paper proposes a new, easy-to-interpret information metric, the "relative information" (RI), which is designed for specific parameters of a model and takes a value between 0% and 100%. We establish the relationship between interindividual variability for a specific parameter and the variance of the associated parameter estimator, demonstrating that, under a "perfect" experiment (eg, infinite samples or/and minimum experimental error), the RI and the variance of the model parameter estimator converge, respectively, to 100% and the ratio of the interindividual variability for that parameter and the number of subjects. Extensive simulation experiments and analyses of three real datasets show that our proposed RI metric can accurately characterize the information for parameters of interest for NLME models. The new information metric can be readily used to facilitate study designs and model diagnosis.

A quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage

Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling.

Pharmacokinetics and Exposure-Response Analyses of Daratumumab in Combination Therapy Regimens for Patients with Multiple Myeloma

INTRODUCTION

Daratumumab, a human IgG monoclonal antibody targeting CD38, has demonstrated activity as monotherapy and in combination with standard-of-care regimens in multiple myeloma. Population pharmacokinetic analyses were conducted to determine the pharmacokinetics of intravenous daratumumab in combination therapy versus monotherapy, evaluate the effect of patient- and disease-related covariates on drug disposition, and examine the relationships between daratumumab exposure and efficacy/safety outcomes.

METHODS

Four clinical studies of daratumumab in combination with lenalidomide/dexamethasone (POLLUX and GEN503); bortezomib/dexamethasone (CASTOR); pomalidomide/dexamethasone, bortezomib/thalidomide/dexamethasone, and bortezomib/melphalan/prednisone (EQUULEUS) were included in the analysis. Using various dosing schedules, the majority of patients (684/694) received daratumumab at a dose of 16 mg/kg. In GEN503, daratumumab was administered at a dose of 2 mg/kg (n = 3), 4 mg/kg (n = 3), 8 mg/kg (n = 4), and 16 mg/kg (n = 34). A total of 650 patients in EQUULEUS (n = 128), POLLUX (n = 282), and CASTOR (n = 240) received daratumumab 16 mg/kg. The exposure-efficacy and exposure-safety relationships examined progression-free survival (PFS) and selected adverse events (infusion-related reactions; thrombocytopenia, anemia, neutropenia, lymphopenia, and infections), respectively.

RESULTS

Pharmacokinetic profiles of daratumumab were similar between monotherapy and combination therapy. Covariate analysis identified no clinically important effects on daratumumab exposure, and no dose adjustments were recommended on the basis of these factors. Maximal clinical benefit on PFS was achieved for the majority of patients (approximately 75%) at the 16 mg/kg dose. No apparent relationship was observed between daratumumab exposure and selected adverse events.

CONCLUSION

These data support the recommended 16 mg/kg dose of daratumumab and the respective dosing schedules in the POLLUX and CASTOR pivotal studies.

FUNDING

Janssen Research & Development.

Full covariate modelling approach in population pharmacokinetics: understanding the underlying hypothesis tests and implications of multiplicity

AIMS

To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity.

METHODS

Clinical trial simulations were performed using a simple one-compartment pharmacokinetic model. Different numbers of covariates, sample sizes, effect sizes of covariates, and correlations among covariates were explored. The false positive rate (FPR) and power were evaluated.

RESULTS

The FPR for the FCM approach dramatically increases with number of covariates. The chance of incorrectly selecting ≥1 seemingly clinically relevant covariates can be increased from 5% to a 40-70% range for 10-20 covariates. The SCI approach may provide appropriate control of the family-wise FPR, allowing more appropriate decision making. As a result, the power detecting real effects without incorrectly identifying non-existing effects can be greatly improved by the SCI approach compared to the approach in current practice. The performance of the SCI approach is driven by the ratio of sample size to number of covariates. The FPR can be controlled at 5% and 10% using the SCI approach when the ratio was ≥20 and 10, respectively.

CONCLUSION

The FCM approach still lies within the framework of statistical testing, and therefore multiplicity is an issue for this approach. It is imperative to consider multiplicity reporting and adjustments in FCM modelling practice to ensure more appropriate decision making.

Pharmacokinetics of Daratumumab Following Intravenous Infusion in Relapsed or Refractory Multiple Myeloma After Prior Proteasome Inhibitor and Immunomodulatory Drug Treatment

Daratumumab is a CD38 monoclonal antibody recently approved for the treatment of multiple myeloma (MM). We report daratumumab pharmacokinetic data from GEN501, a phase I/II dose-escalation (0.005–24 mg/kg) and dose-expansion (8 or 16 mg/kg) study, and SIRIUS, a phase II study (8 or 16 mg/kg), in relapsed or refractory MM. Noncompartmental analysis was conducted to characterize daratumumab pharmacokinetics, and, in both studies, daratumumab exhibited nonlinear pharmacokinetic characteristics. Decreasing daratumumab clearance with increasing dose suggests saturation of target-mediated clearance at higher dose levels, whereas decreasing clearance over time with repeated dosing may be due to tumor burden reductions as CD38-positive cells are eliminated. These and other pharmacokinetic data analyses support the use of the recommended dose regimen of daratumumab (16 mg/kg weekly for 8 weeks, every 2 weeks for 16 weeks, and every 4 weeks thereafter) to rapidly saturate target-mediated clearance during weekly dosing and maintain saturation when dosing every 2 or 4 weeks.

Influence of Disease and Patient Characteristics on Daratumumab Exposure and Clinical Outcomes in Relapsed or Refractory Multiple Myeloma

OBJECTIVE

The aim of this study was to understand the influence of disease and patient characteristics on exposure to daratumumab, an immunoglobulin Gκ (IgGκ) monoclonal antibody, and clinical outcomes in relapsed or refractory multiple myeloma (MM).

PATIENTS AND METHODS

Baseline myeloma type, albumin levels, renal/hepatic function, age, sex, race, weight, Eastern Cooperative Oncology Group (ECOG) status, refractory status, and number of prior therapies were evaluated using data from two clinical studies-GEN501 (N = 104) and SIRIUS (N = 124).

RESULTS

Daratumumab clearance was approximately 110% higher in IgG myeloma patients than non-IgG myeloma patients, leading to significantly lower exposure in IgG myeloma patients based on maximum trough serum concentrations (p < 0.0001). However, the overall response rate was similar for IgG and non-IgG myeloma patients (odds ratio 1.08, 95% confidence interval 0.54-2.17, p = 0.82). For a given exposure, the drug effect was significantly higher (approximately two times) in IgG versus non-IgG patients (p = 0.03). The influence of other patient and disease characteristics on daratumumab exposure was minimal and no significant effect on efficacy was observed (p ≥ 0.1). The incidences of infections and overall grade 3 or higher adverse events in subpopulations were generally consistent with that of the overall population.

CONCLUSION

Due to competition with the MM-produced IgG M-protein for neonatal Fc receptor protection from clearance, IgG-based monoclonal antibodies in general may have significantly higher clearance and lower concentrations in IgG MM patients compared with non-IgG MM patients. Careful evaluation of the impact of exposure and patient and disease characteristics on safety and efficacy is warranted for all IgG-based monoclonal antibodies used in MM.

[Gene Analysis for the Sudden Death of Hypertrophic Cardiomyopathy by Whole Exome Sequencing.]

OBJECTIVES

To analyze the related pathogenicity gene mutations in a sudden death of hypertrophic cardiomyopathy （HCM） on whole exome level.

METHODS

Whole exome sequencing （WES） was been performed on a sudden death case sample with pathological features of HCM by Illumina® Hiseq 2500 platform. Using hg19 as the reference sequences, the sequencing data were analyzed. Suspicious single nucleotide variants （SNV） were screened, and the conservatism and function were analyzed by the software such as PhyloP, PolyPhen-2, SIFT, etc.

RESULTS

After screening, a heterozygous mutation C719R was finally identified in the gene MYBPC3 of this case.

CONCLUSIONS

The molecular anatomy on whole exome level by second generation sequencing technology can help to define the molecular mechanism of HCM and provide a new mothed and thought for analysis of death cause.

Clinical Implications of Complex Pharmacokinetics for Daratumumab Dose Regimen in Patients With Relapsed/Refractory Multiple Myeloma

New therapeutic strategies are urgently needed to improve clinical outcomes in patients with multiple myeloma (MM). Daratumumab is a first-in-class, CD38 human immunoglobulin G1κ monoclonal antibody approved for treatment of relapsed or refractory MM. Identification of an appropriate dose regimen for daratumumab is challenging due to its target-mediated drug disposition, leading to time- and concentration-dependent pharmacokinetics. We describe a thorough evaluation of the recommended dose regimen for daratumumab in patients with relapsed or refractory MM.

Association between nutritional risk and routine clinical laboratory measurements and adverse outcomes: a prospective study in hospitalized patients of Wuhan Tongji Hospital

Background/Objectives:

Nutritional risk screening (NRS-2002) and routine clinical laboratory measurements (RCLMs) had been shown to have a predictive value in adverse outcomes in some studies, respectively. This study analyzed the association between NRS-2002 and RCLMs and estimated their prospective value in predicting adverse outcomes.

Subjects/Methods:

A total of 916 hospitalized patients were screened on admission with NRS-2002 and Subjective Global Assessment; RCLMs, which include blood test, kidney and liver function and electrolytes, were recorded. Diagnosis, nutritional support, surgery, radiotherapy, chemotherapy, complications, mortality and hospital stay during hospitalization were collected. The X²-test, odds ratios with 95% confidence intervals, kappa (k) statistic and regression analyses were conducted.

Results:

An overall 48.1% of the 916 patients were at nutritional risk on admission. Comparing ‘at risk' with ‘no risk', a significantly higher incidence of abnormality was found not only in nutritional markers but also in other parameters of RCLMs (OR ranged from 1.5 to 3.5). Regression analyses showed that ‘at risk' determined at admission was not a significant predictor of adverse outcomes after adjusting for other confounding factors, although it was a strong predictor in univariate analysis, whereas hypoalbuminemia, low total lymphocyte count, abnormality of hepatic and renal function were predictors after adjusting for confounders.

Conclusions:

The findings suggest that NRS-2002 might be a global index of ‘sickness' rather than be only a nutritional screening tool. It being rated once at admission is insufficient and should be repeated for using it as a predictor, whereas RCLMs routinely measured at admission may be able to be used to predict adverse outcomes.

Modeling of bounded outcome scores with data on the boundaries: application to disability assessment for dementia scores in Alzheimer's disease

Mixed-effects beta regression (BR), boundary-inflated beta regression (ZOI), and coarsening model (CO) were investigated for analyzing bounded outcome scores with data at the boundaries in the context of Alzheimer's disease. Monte Carlo simulations were conducted to simulate disability assessment for dementia (DAD) scores using these three models, and each set of simulated data were analyzed by the original simulation model. One thousand trials were simulated, and each trial contained 250 subjects. For each subject, DAD scores were simulated at baseline, 13, 26, 39, 52, 65, and 78 weeks. The simulation-reestimation exercise showed that all the three models could reasonably recover their true parameter values. The bias of the parameter estimates of the ZOI model was generally less than 1%, while the bias of the CO model was mainly within 5%. The bias of the BR model was slightly higher, i.e., less than or in the order of 20%. In the application to real-world DAD data from clinical studies, examination of prediction error and visual predictive check (VPC) plots suggested that both BR and ZOI models had similar predictive performance and described the longitudinal progression of DAD slightly better than the CO model. In conclusion, the investigated three modeling approaches may be sensible choices for bounded outcome scores with data on the edges. Prediction error and VPC plots can be used to identify the model with best predictive performance.

Abiraterone acetate to lower androgens in women with classic 21-hydroxylase deficiency

CONTEXT

Chronic supraphysiological glucocorticoid therapy controls the androgen excess of 21-hydroxylase deficiency (21OHD) but contributes to the high prevalence of obesity, glucose intolerance, and reduced bone mass in these patients. Abiraterone acetate (AA) is a prodrug for abiraterone, a potent CYP17A1 inhibitor used to suppress androgens in the treatment of prostate cancer.

OBJECTIVE

The objective of the study was to test the hypothesis that AA added to physiological hydrocortisone and 9α-fludrocortisone acetate corrects androgen excess in women with 21OHD without causing hypertension or hypokalemia.

DESIGN

This was a phase 1 dose-escalation study.

SETTING

The study was conducted at university clinical research centers.

PARTICIPANTS

We screened 14 women with classic 21OHD taking hydrocortisone 12.5-20 mg/d to enroll six participants with serum androstenedione greater than 345 ng/dL (>12 nmol/L).

INTERVENTION

AA was administered for 6 days at 100 or 250 mg every morning with 20 mg/d hydrocortisone and 9α-fludrocortisone acetate.

MAIN OUTCOME MEASURE

The primary endpoint was normalization of mean predose androstenedione on days 6 and 7 (< 230 ng/dL [<8 nmol/L)] in greater than 80% of participants. Secondary end points included serum 17-hydroxyprogesterone and testosterone (T), electrolytes, plasma renin activity, and urine androsterone and etiocholanolone glucuronides.

RESULTS

With 100 mg/d AA, mean predose androstenedione fell from 764 to 254 ng/dL (26.7-8.9 nmol/L). At 250 mg/d AA, mean androstenedione normalized in five participants (83%) and decreased from 664 to 126 ng/dL (23.2-4.4 nmol/L), meeting the primary end point. Mean androstenedione declined further during day 6 to 66 and 38 ng/dL (2.3 and 1.3 nmol/L) at 100 and 250 mg/d, respectively. Serum T and urinary metabolites declined similarly. Abiraterone exposure was strongly negatively correlated with mean androstenedione. Hypertension and hypokalemia were not observed.

CONCLUSION

AA 100-250 mg/d added to replacement hydrocortisone normalized several measures of androgen excess in women with classic 21OHD and elevated serum androstenedione.

Probing the interplay between quantum charge fluctuations and magnetic ordering in LuFe2O4

The mechanisms producing strong coupling between electric and magnetic order in multiferroics are not always well understood, since their microscopic origins can be quite different. Hence, gaining a deeper understanding of magnetoelectric coupling in these materials is the key to their rational design. Here, we use ultrafast optical spectroscopy to show that the influence of magnetic ordering on quantum charge fluctuations via the double-exchange mechanism can govern the interplay between electric polarization and magnetism in the charge-ordered multiferroic LuFe₂O₄.

Stacking principle and magic sizes of transition metal nanoclusters based on generalized Wulff construction

Nanoclusters with extra stability at certain cluster sizes are known as magic clusters with exotic properties. The classic Wulff construction principle, which stipulates that the preferred structure of a cluster should minimize its total surface energy, is often invoked in determining the cluster magicity, resulting in close-shelled Mackay icosahedronal clusters with odd-numbered magic sizes of 13, 55, 147, etc. Here we use transition metal clusters around size 55 as prototypical examples to demonstrate that, in the nanometer regime, the classic Wulff construction principle needs to be generalized to primarily emphasize the edge atom effect instead of the surface energy. Specifically, our detailed calculations show that nanoclusters with much shorter total edge lengths but substantially enlarged total surface areas are energetically much more stable. As a consequence, a large majority of the nanoclusters within the 3d-, 4d-, and 5d-transition metal series are found to be fcc or hcp crystal fragments with much lower edge energies, and the widely perceived magic size of 55 is shifted to its nearby even numbers.

Population pharmacokinetics and pharmacodynamics of rivaroxaban in patients with acute coronary syndromes

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• Population pharmacokinetics and pharmacodynamics of rivaroxaban have been characterized in healthy subjects and in patients with total venous thromboembolism, deep vein thrombosis or atrial fibrillation.

WHAT THIS STUDY ADDS

• This article is the first description of the population pharmacokinetics (PK) and pharmacodynamics (PD) of rivaroxaban in patients with acute coronary syndrome (ACS). It is the largest population pharmacokinetic and pharmacodynamic study on rivaroxaban conducted to date (n= 2290). The PK and PK-PD relationship of rivaroxaban in patients with ACS were similar to those in other patient populations. In addition, model-based simulations showed that the influence of renal function and age on the exposure to rivaroxaban in the ACS population were similar to the findings from Phase 1 special population studies. These findings suggest that rivaroxaban has highly predictable PK-PD and may provide a consistent anticoagulant effect across the studied patient populations, which allows an accurate prediction of the dose to control anticoagulation optimally.

AIMS

The aim of this analysis was to use a population approach to facilitate the understanding of the pharmacokinetics and pharmacodynamics of rivaroxaban in patients with acute coronary syndrome (ACS) and to evaluate the influence of patient covariates on the exposure of rivaroxaban in patients with ACS. METHODS A population pharmacokinetic model was developed using pharmacokinetic samples from 2290 patients in Anti-Xa Therapy to Lower Cardiovascular Events in Addition to Standard Therapy in Subjects with Acute Coronary Syndrome Thrombolysis in Myocardial Infarction 46. The relationship between pharmacokinetics and the primary pharmacodynamic end point, prothrombin time, was evaluated.

RESULTS

The pharmacokinetics of rivaroxaban in patients with ACS was adequately described by an oral one-compartment model. The estimated absorption rate, apparent clearance and volume of distribution were 1.24 h(-1) (interindividual variability, 139%), 6.48 l h(-1) (31%) and 57.9 l (10%), respectively. Simulations indicate that the influences of renal function, age and bodyweight on exposure in ACS patients are consistent with the findings in previous Phase 1 studies. Rivaroxaban plasma concentrations exhibit a close-to-linear relationship with prothrombin time in the ACS population, with little interindividual variability. The estimated pharmacokinetic and pharmacodynamic parameters for the ACS patients were comparable to those for venous thromboembolism prevention, deep vein thrombosis and atrial fibrillation patients.

CONCLUSIONS

The similarity in pharmacokinetics/pharmacodynamics of rivaroxaban among different patient populations and the low interindividual variability in the exposure-prothrombin time relationship indicate that the anticoagulant effect of rivaroxaban is highly predictable and consistent across all the patient populations studied.

Shrinkage in nonlinear mixed-effects population models: quantification, influencing factors, and impact

Shrinkage of empirical Bayes estimates (EBEs) of posterior individual parameters in mixed-effects models has been shown to obscure the apparent correlations among random effects and relationships between random effects and covariates. Empirical quantification equations have been widely used for population pharmacokinetic/pharmacodynamic models. The objectives of this manuscript were (1) to compare the empirical equations with theoretically derived equations, (2) to investigate and confirm the influencing factor on shrinkage, and (3) to evaluate the impact of shrinkage on estimation errors of EBEs using Monte Carlo simulations. A mathematical derivation was first provided for the shrinkage in nonlinear mixed effects model. Using a linear mixed model, the simulation results demonstrated that the shrinkage estimated from the empirical equations matched those based on the theoretically derived equations. Simulations with a two-compartment pharmacokinetic model verified that shrinkage has a reversed relationship with the relative ratio of interindividual variability to residual variability. Fewer numbers of observations per subject were associated with higher amount of shrinkage, consistent with findings from previous research. The influence of sampling times appeared to be larger when fewer PK samples were collected for each individual. As expected, sample size has very limited impact on shrinkage of the PK parameters of the two-compartment model. Assessment of estimation error suggested an average 1:1 relationship between shrinkage and median estimation error of EBEs.

Pharmacokinetic and pharmacodynamic modeling of opioid-induced gastrointestinal side effects in patients receiving tapentadol IR and oxycodone IR

PURPOSE

To understand the relationship between the risk of opioid-related gastrointestinal adverse effects (AEs) and exposure to tapentadol and oxycodone as well as its active metabolite, oxymorphone, using pharmacokinetic/pharmacodynamic models.

METHODS

The analysis was based on a study in patients with moderate-to-severe pain following bunionectomy. Population PK modeling was conducted to estimate population PK parameters for tapentadol, oxycodone, and oxymorphone. Time to AEs was analyzed using Cox proportional-hazards models.

RESULTS

Risk of nausea, vomiting, and constipation significantly increased with exposure to tapentadol or oxycodone/oxymorphone. However, elevated risk per drug exposure of AEs for tapentadol was ~3-4 times lower than that of oxycodone, while elevated AE risk per drug exposure of oxycodone was ~60 times lower than that for oxymorphone, consistent with reported in vitro receptor binding affinities for these compounds. Simulations show that AE incidence following administration of tapentadol IR is lower than that following oxycodone IR intake within the investigated range of analgesic noninferiority dose ratios.

CONCLUSIONS

This PK/PD analysis supports the clinical findings of reduced nausea, vomiting and constipation reported by patients treated with tapentadol, compared to patients treated with oxycodone.

Analysis of dose-response in flexible dose titration clinical studies

Assessing dose-response from flexible-dose clinical trials (e.g., titration or dose escalation studies) is challenging and often problematic due to the selection bias caused by 'titration-to-response'. We investigate the performance of a dynamic linear mixed-effects (DLME) model and marginal structural model (MSM) in evaluating dose-response from flexible-dose titration clinical trials via simulations. The simulation results demonstrated that DLME models with previous exposure as a time-varying covariate may provide an unbiased and efficient estimator to recover exposure-response relationship from flexible-dose clinical trials. Although the MSM models with independent and exchangeable working correlations appeared to be able to recover the right direction of the dose-response relationship, it tended to over-correct selection bias and overestimated the underlying true dose-response. The MSM estimators were also associated with large variability in the parameter estimates. Therefore, DLME may be an appropriate modeling option in identifying dose-response when data from fixed-dose studies are absent or a fixed-dose design is unethical to be implemented.

Population pharmacokinetics of tapentadol immediate release (IR) in healthy subjects and patients with moderate or severe pain

BACKGROUND

Tapentadol is a new, centrally active analgesic agent with two modes of action--mu opioid receptor agonism and norepinephrine reuptake inhibition--and the immediate-release (IR) formulation is approved in the US for the relief of moderate to severe acute pain. The aims of this analysis were to develop a population pharmacokinetic model to facilitate the understanding of the pharmacokinetics of tapentadol IR in healthy subjects and patients following single and multiple dosing, and to identify covariates that might explain variability in exposure following oral administration.

METHODS

The analysis included pooled data from 11,385 serum pharmacokinetic samples from 1827 healthy subjects and patients with moderate to severe pain. Population pharmacokinetic modelling was conducted using nonlinear mixed-effects modelling (NONMEM) software to estimate population pharmacokinetic parameters and the influence of the subjects' demographic characteristics, clinical laboratory chemistry values and disease status on these parameters. Simulations were performed to assess the clinical relevance of the covariate effects on tapentadol exposure.

RESULTS

A two-compartment model with zero-order release followed by first-order absorption and first-order elimination best described the pharmacokinetics of tapentadol IR following oral administration. The interindividual variability (coefficient of variation) in apparent oral clearance (CL/F) and the apparent central volume of distribution after oral administration were 30% and 29%, respectively. An additive error model was used to describe the residual variability in the log-transformed data, and the standard deviation values were 0.308 and 0.314 for intensively and sparsely sampled data, respectively. Covariate analysis showed that sex, age, bodyweight, race, body fat, hepatic function (using total bilirubin and total protein as surrogate markers), health status and creatinine clearance were statistically significant factors influencing the pharmacokinetics of tapentadol. Total bilirubin was a particularly important factor that influenced CL/F, which decreased by more than 60% in subjects with total bilirubin greater than 50 micromol/L.

CONCLUSIONS

The population pharmacokinetic model for tapentadol IR identified the relationship between pharmacokinetic parameters and a wide range of covariates. The simulations of tapentadol exposure with identified, statistically significant covariates demonstrated that only hepatic function (as characterized by total bilirubin and total protein) may be considered a clinically relevant factor that warrants dose adjustment. None of the other covariates are of clinical relevance, nor do they necessitate dose adjustment.

Absence of spin liquid behavior in Nd3Ga5SiO14 using magneto-optical spectroscopy

We measured the low-lying crystal field levels of Nd3+ in Nd3Ga5SiO14 via magneto-optical spectroscopy and employed the extracted energies, magnetic moments, and symmetries to analyze the magnetic properties and test the spin liquid candidacy of this material. The exchange interaction is surprisingly small, a discovery that places severe constraints on models used to describe the ground state of this system. Further, it demonstrates the value of local-probe photophysical techniques for rare-earth-containing materials where bulk property measurements can be skewed by low-lying electronic structure.

Charge order, dynamics, and magnetostructural transition in multiferroic LuFe2O4

We investigated the series of temperature and field-driven transitions in LuFe2O4 by optical and Mössbauer spectroscopies, magnetization, and x-ray scattering in order to understand the interplay between charge, structure, and magnetism in this multiferroic material. We demonstrate that charge fluctuation has an onset well below the charge ordering transition, supporting the "order by fluctuation" mechanism for the development of charge order superstructure. Bragg splitting and large magneto-optical contrast suggest a low-temperature monoclinic distortion that can be driven by both temperature and magnetic field.

[The effect of Beijing Fangshan Cardiovascular Prevention Program on the incidence and mortality of stroke]

OBJECTIVE

To explore potential effective measures for lowering incidence and mortality of stroke in rural community population of China.

METHODS

Beijing Fangshan Cardiovascular Prevention Program (BFCP), under whole population and high risk individuals strategies with measures of health education and hypertension control, were launched in 1991 in five communities including intervention communities (IC), about 66,000 residents, and control communities (CC), about 54,000 residents, in Fangshan, Beijing suburb.

RESULT

Incidences of stroke averaged 235.23 per 100,000 and 289.22 per 100,000, for IC and CC respectively, with a statistically significant difference, and mortalities of stroke averaged 80.63 per 100,000 and 98.01 per 100,000, for IC and CC respectively, with a statistically significant difference, during years of 1992 to 1999. The net change of stroke incidence was 126.13 per 100,000 in IC versus CC, with a statistically significant difference. Incidences of stroke increased by 11.63% and 75.27%, for IC and CC respectively, while mortalities of stroke decreased by 46.80% and 22.82%, respectively, for IC and CC from years of 1992 to 1999.

CONCLUSION

BFCP has yielded obvious effect on controlling incidence and mortality of stroke in rural community population, but the trend of stroke incidence increasing was still not restrained radically.

Hypermutability to ionizing radiation in mismatch repair-deficient, Pms2 knockout mice

DNA mismatch repair (MMR) has been shown to play a role in the cytotoxicity of ionizing radiation (IR), as cell lines established from MMR-deficient mice exhibit higher clonogenic survival after IR than do cell lines from wild-type littermates. To test whether this tolerance phenotype would render MMR-deficient animals hypermutable to IR, we compared IR mutagenesis of Pms2-deficient versus wild-type transgenic mice carrying a lambda shuttle vector for mutation detection. In Pms2 nullizygous animals, the mutation frequency in the supFG1 reporter gene was increased from 210 x 10(-5) in untreated animals to 734 x 10(-5) after 6 Gy of IR (an increase of 524 mutants per 10(5)), whereas the frequency in wild-type mice increased from 1.9 x 10(-5) to 10.2 x 10(-5) (an increase of only 8.3 mutants per 10(5)). Similarly, when the lambda cII gene was used as a reporter, the mutation frequency in nullizygous mice was increased from 16.3 x 10(-5) to 42.3 x 10(-5) after IR (an increase of 26.0 x 10(-5)), whereas the frequency in wild-type mice increased from 2.4 x 10(-5) to 9.4 x 10(-5) (an increase of only 7.0 x 10(-5)). The pattern of IR-induced mutations in the MMR-deficient animals was notable for single bp deletions and insertions in mononucleotide repeat sequences, along with a slight increase in transversions. Overall, these results suggest that MMR-deficiency confers hypermutability to IR, and that much of this hypermutability can be attributed to induced instability of simple sequence repeats. Hence, MMR influences not only the survival but also the mutability of cells in response to IR.

During multicellular migration, myosin ii serves a structural role independent of its motor function

We have shown previously that cells lacking myosin II are impaired in multicellular motility. We now extend these results by determining whether myosin contractile function is necessary for normal multicellular motility and shape control. Myosin from mutants lacking the essential (mlcE(-)) myosin light chain retains the ability to form bipolar filaments that bind actin, but shows no measurable in vitro or in vivo contractile function. The contractile function is necessary for cell shape control since mlcE(-) cells, like myosin heavy-chain null mutants (mhcA(-)), were defective in their ability to control their three-dimensional shape. When mixed with wild-type cells in chimeric aggregation streams, the mlcE(-) cells were able to move normally, unlike mhcA(-) cells which accumulated at the edges of the stream and became distorted by their interactions with wild-type cells. When mhcA(-) cells were mixed with mlcE(-) streams, the mhcA(-) cells were excluded. The normal behavior of the mlcE(-) cells in this assay suggests that myosin II, in the absence of motor function, is sufficient to allow movement in this constrained, multicellular environment. We hypothesize that myosin II is a major contributor to cortical integrity even in the absence of contractile function.