CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries.

Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging-continuous wavelet transforms-is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00.

A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes.

A distribution-based selective optimization method for eliminating periodic defects in harmonic signals.

Due to environmental interference and defects in measured objects, measurement signals are frequently affected by unpredictable noise and periodic defects. Moreover, there is a lack of effective methods for accurately distinguishing defect components from measurement signals. In this study, a distribution-based selective optimisation method (SOM) is proposed to mitigate the effects of noise and defect components. The SOM can be seen as a binary- or multiple-class signal classifier based on an error distribution, which can simultaneously eliminate periodic defect components of measurement signals and proceed with signal-fitting regression. The effectiveness, accuracy, and feasibility of the SOM are verified in theoretical and realworld measurement settings. Based on theoretical simulations under various parameter conditions, some criteria for selecting operation variables among a selection of parameter conditions are explained in detail. The proposed method is capable of separating defect components from measurement signals while also achieving a satisfactory fitting curve for the measurement signals. The proposed SOM has broad application prospects in signal processing and defect detection for mechanical measurements, electronic filtering, instrumentation, part maintenance, and other fields.

Surface Quality Improvement for Ultrasonic-Assisted Inner Diameter Sawing with Six-Axis Force Sensors.

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model's force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.

Basic psychological needs satisfaction of stroke patients: a qualitative study.

BACKGROUND: Previous studies have shown that the satisfaction of basic psychological needs is related to psychological well-being. Improving satisfaction will increase personal well-being, promote positive health outcomes, and improve disease recovery. However, no research has focused on the basic psychological needs of stroke patients. Therefore, this study aims to determine the basic psychological needs experience, satisfaction, and its influencing factors of stroke patients. METHODS: 12 males and 6 females in the non-acute phase with stroke were recruited in the Department of Neurology, Nanfang Hospital. The individual, semi-structured interviews were conducted in a separate room. The data were imported to Nvivo 12 and analyzed using the directed content analysis approach. RESULTS: Three main themes consisting of 9 sub-themes were derived from the analysis. These three main themes focused on the needs for autonomy, competence, and relatedness of stroke patients. CONCLUSION: Participants have different degrees of satisfaction of their basic psychological needs, which may be related to their family environment, work environment, stroke symptoms, or other factors. Stroke symptoms can significantly reduce the patients' needs for autonomy and competence. However, the stroke seems to increase the patients' satisfaction of the need for relatedness.

Acetoacetyl-CoA transferase ydiF regulates the biofilm formation of avian pathogenic Escherichia coli.

Avian pathogenic Escherichia coli (APEC) causes persistent infection of poultry and multi-system diseases, which seriously endanger the development of the poultry industry. Biofilm allows bacteria to adapt to the natural environment and plays an important role in resistance to the external environment and the pathogenicity of APEC, but the mechanism of its formation and regulatory network have not been clarified. In this study, we used a Tn5 transposon random mutation library constructed with APEC and identified ydiF, a gene that has not previously been recognized in E. coli biofilm formation. To confirm that the ydiF gene really can regulate the formation of APEC biofilm, the ydiF gene deletion strain was constructed using APEC81. Protein association networks prediction results show that ydiF is mainly associated with genes related to the metabolism of sugars and fatty acids. Deletion of the ydiF gene significantly reduces the formation of APEC biofilm and scanning electron microscopy indicated that the degree of adhesion between the bacteria was also reduced. The deletion of the ydiF gene also significantly reduced the motility of APEC81 and through transmission electron microscopy APEC81 was observed to have significantly fewer flagella. However, the colony morphology of APEC81 on Congo red and Coomassie brilliant blue media was unaffected. The results of fluorescence quantification showed that the deletion of the ydiF gene caused a down-regulation in the transcription of genes related to the second messenger, sugar metabolism, and quorum sensing. These results indicate that ydiF plays an important role in biofilm formation and the movement of APEC. In addition, it may be possible to regulate the formation of APEC biofilms by different methods such as by regulating the second messenger and metabolic system.

Oral health knowledge, attitudes, and practices and oral health-related quality of life among stroke inpatients: a cross-sectional study.

BACKGROUND: Stroke patients have poor oral hygiene, experience oral dysfunction due to disease factors, and have impaired oral health-related quality of life (OHRQoL). This study aimed to determine the oral health knowledge, attitudes, and practices of stroke inpatients, assess the OHRQoL of these patients, and identify their correlates. METHODS: In this cross-sectional study, 281 stroke inpatients aged between 22 and 88 years (57.94 ± 10.94) were conveniently selected from three hospitals in Guangzhou, China. OHRQoL was measured among these stroke patients using a Chinese version of the Oral Health Impact Profile-14 (OHIP-14). SPSS 26.0 was used for statistical analysis. Mean scores, standard deviations, and frequency distributions were obtained. The Mann-Whitney U test, Kruskal‒Wallis H test, Spearman's correlation, and multiple linear regression were used in the analysis. RESULTS: The mean score of the patients' OHRQoL was 8.37 ± 6.67, with the highest score in the pain or discomfort of the mouth dimension (3.11 ± 2.13) and pain being the most common negative effect (13.5%). In multiple linear regression analysis, significant differences were found between patients only in age (P = 0.008), toothache (P < 0.001), self-rated oral health (P < 0.001), time since last dentist visit (P = 0.037) and reason for not having visited a dentist in the past year (P < 0.001). CONCLUSION: The OHRQoL of patients hospitalised with stroke was moderate, and oral conditions still need to be improved. Increasing age, toothache, a longer time since the last dental visit and the reason for not visiting a dentist in the past year had a negative effect on OHRQoL, and better self-rated oral health had a positive effect. Therefore, in clinical work, greater attention should be given to elderly stroke patients, patients with poor oral status and poor oral health behaviours, timely assessment of patients' swallowing function, nutritional function, and self-care ability, and early and targeted oral health interventions and guidance.

Machine Learning-Based Risk Stratification for Gestational Diabetes Management.

Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK's National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019-0.023], 0.482 [0.442-0.516], and 0.112 [0.109-0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients.

Identification of novel biofilm genes in avian pathogenic Escherichia coli by Tn5 transposon mutant library.

Avian pathogenic Escherichia coli (APEC) is the main pathogens that inflict the poultry industry. Biofilm as the pathogenic factors of APEC, which can enhance the anti-host immune system of APEC and improve its survival in the environment. In order to screen for new genes related to APEC biofilm. The APEC strain APEC81 was used to construct a mutant library by Tn5 insertion mutagenesis. Moreover the 28 mutant strains with severely weakened biofilm were successfully screened from 1500 mutant strains by crystal violet staining, in which 17 genes were obtained by high-efficiency thermal asymmetric interlaced PCR. The reported genes include 3 flagella genes (fliS, fliD, and fliR), 4 curli fimbriae genes (csgD, csgA, csgF, and csgG) and 3 type 1 fimbriae genes (fimA, fimD, and fimC). The novel genes include 3 coenzyme genes (gltA, bglX, and mltF) and 4 putative protein genes (yehE, 07045, 11735, 11255). To investigate whether these 17 genes co-regulate the biofilm, the 17 identified genes were deleted from APEC strain APEC81. The results showed that except for the 11735 and 11255 genes, the deletion of 15 genes significantly reduced the biofilm formation ability of APEC81 (P < 0.05). The result of rdar (red, dry and rough) colony morphology showed that curli fimbriae genes (csgD, csgA, csgF, and csgG) and other functional genes (fimC, glxK, yehE, 07045, and 11255) affected the colony morphology. In particular, the hypothetical protein YehE had the greatest influence on the biofilm. It was predicted to have the same structure as the type 1 fimbria protein. When yehE was deleted, the fimE transcription was up-regulated, and the fimA and fimB transcription were down-regulated, resulting in a decrease in type 1 fimbriae. Hence, the yehE mutant significantly reduced the biofilm and the adhesion and invasion ability to cells (P < 0.05). This study identified 5 novel genes (gltA, bglX, mltF, yehE, and 07045) related to biofilm formation and confirmed that yehE affects biofilm formation by type 1 fimbriae, which will benefit further study of the mechanism of biofilm regulation in APEC.

Standardising the assessment of caesarean birth using an oxford caesarean prediction score for mothers with gestational diabetes.

Mothers with gestational diabetes are at increased risk of giving birth by caesarean section. A standardised assessment method may help to guide in recommendations in planning caesarean birth. We analysed 203 women with gestational diabetes managed in a single centre and developed an aggregate heuristic risk score. Among 155 women who had not had a previous caesarean birth, five risk factors (previous birth, weight gain during pregnancy, mother's height, and glycated haemoglobin and fasting blood glucose results at the beginning of pregnancy) were found associated with primary caesarean birth. Risk of primary caesarean birth in low-risk women (score 0-1) was 13.8%, medium-risk (score 2-3) 24.5% and high risk (score ≥ 4) 66.7%. The area under the receiver operating characteristic (AUROC) for primary caesarean birth prediction is 0.726 ± 0.003. Machine learning models were then deployed on 97 patients to explore the role of temporal blood glucose in predicting caesarean birth, achieving an AUROC of 0.857 ± 0.008. In conclusion, Oxford caesarean prediction score could help clinicians counselling women with gestational diabetes about their individual risk of primary caesarean birth. Temporal blood glucose measurements may improve the prediction subject to further validation.

Translation and Cross-Cultural Adaptation of Post-Stroke Checklist into Mandarin: A Cognitive Interview Study.

PURPOSE: This study aimed to translate the Post-stroke Checklist into Mandarin, validate its content, and cross-culturally adapt the Mandarin version of the Post-stroke Checklist (M-PSC) in line with expert review and cognitive interviewing. PATIENTS AND METHODS: After translating into Mandarin, the M-PSC was modified and content validated using expert review, which resulted in a pilot version for cognitive interviewing among stroke survivors inclusive of subjects from an outpatient unit (n = 7), a rehabilitation department using traditional Chinese medicine (n = 10), and a community (n = 10). The interviews were transcribed and analyzed according to the four-stage cognitive model. A set of nine practice rounds where the number of rounds of iteration was determined based on the saturation of information was conducted. Data analysis was performed concurrently with data collection. Recommendations for changes to the M-PSC were made based on the analyses in the iteration. RESULTS: We successfully developed an initial M-PSC, back-translated it into English, reviewed the discrepancies and performed a three-step expert review to modify the M-PSC and validate its excellent content. Twenty-seven interviews were carried out, and a wide range of sources of error primarily related to the comprehension, retrieval of information, and judgment was reported, and each item of the M-PSC was rectified accordingly. CONCLUSION: The Post-stroke Checklist was translated into Mandarin and cross-culturally adapted based on expert review and cognitive interviewing. Adaptations were made to support that the Mandarin version can be implemented in clinical practice for long-term post-stroke care.

A methodological study on the combination of qualitative and quantitative methods in cognitive interviewing for cross-cultural adaptation.

AIM: The aim of this study is to explore the use of the Questionnaire Appraisal System with a combination of qualitative and quantitative methods in cognitive interviewing for cross-cultural adaptation. DESIGN: This is a descriptive methodological study. METHODS: Using the Mandarin version of the Post-Stroke Checklist as an example, cognitive interviews were conducted with 27 stroke survivors in Guangzhou between November 2020 and February 2021. The Questionnaire Appraisal System was applied as a codebook in focus group discussions to perform quantitative data collections and quantitative content analysis. RESULTS: Thirty-eight problems were proposed in focus group discussions and identified all but four of the 30 questions that emerged in the cognitive interviews. A new item was added to the Questionnaire Appraisal System for better categorization. Four categories and six subcategories of problems in the checklist were revealed.

Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review.

Hypertension affects an estimated 1.4 billion people and is a major cause of morbidity and mortality worldwide. Early diagnosis and intervention can potentially decrease cardiovascular events later in life. However, blood pressure (BP) measurements take time and require training for health care professionals. The measurements are also inconvenient for patients to access, numerous daily variables affect BP values, and only a few BP readings can be collected per session. This leads to an unmet need for an accurate, 24-h continuous, and portable BP measurement system. Electrocardiograms (ECGs) have been considered as an alternative way to measure BP and may meet this need. This review summarizes the literature published from January 1, 2010, to January 1, 2020, on the use of only ECG wave morphology to monitor BP or identify hypertension. From 35 articles analyzed (9 of those with no listed comorbidities and confounders), the P wave, QTc intervals and TpTe intervals may be promising for this purpose. Unfortunately, with the limited number of articles and the variety of participant populations, we are unable to make conclusions about the effectiveness of ECG-only BP monitoring. We provide 13 recommendations for future ECG-only BP monitoring studies and highlight the limited findings in pregnant and pediatric populations. With the advent of convenient and portable ECG signal recording in smart devices and wearables such as watches, understanding how to apply ECG-only findings to identify hypertension early is crucial to improving health outcomes worldwide.

The KdpD/KdpE two-component system contributes to the motility and virulence of avian pathogenic Escherichia coli.

Two-component systems (TCSs) are widespread regulatory systems which can help bacteria to control their cellular functions and respond to a diverse range of stimuli. The KdpD/KdpE system had been well studied for regulating potassium transport and identified as an adaptive regulator involved in the virulence of some pathogenic bacteria, but its role in avian pathogenic Escherichia coli (APEC) was still unknown. In this study, the mutant strain AE17ΔKdpDE was obtained successfully of a clinical APEC isolation AE17 using the lambda Red recombinase system and performed the transcriptional sequencing of the wild type strain AE17 and the mutant strain AE17ΔKdpDE. The transcriptional sequencing results revealed that the KdpD/KdpE two-component system mainly influenced the expression of the genes covering metabolic pathways, flagellar assembly, global transcription regulator. The expression of some flagellar-related genes detecting by quantitative real-time PCR was consistent with the results of transcriptional sequencing. Importantly, fewer flagellum of the mutant strain AE17ΔKdpDE was observed than AE17 using the transmission electron microscope and a decreased motility circle of AE17ΔKdpDE appeared in the semisolid medium. In addition, the serum bactericidal assay was carried out with the specific-pathogen-free chicken in different dilution and the survival ability in the serum of AE17ΔKdpDE was also obviously lower than that of AE17. These results suggested that in APEC, the KdpD/KdpE two-component system mainly influenced the expression of flagella-related genes, the flagellum formation, the motility and antiserum bactericidal activity.

Wearable Vital Signs Monitoring for Patients With Asthma: A Review.

Worldwide,an estimated 461 000 people die from asthma attacks each year. While there remain treatments to alleviate asthma symptoms and reduce deaths, patient deterioration needs to be identified in sufficient time. To prevent asthma deterioration, patients need to be aware of personal and environmental triggers and monitor their asthma symptoms. The aim of this article is to provide a comprehensive review of the current state-of-the-art wearable sensors and devices that use vital signs for asthma patient monitoring and management. Among all vital signs, breathing rate and airflow sound are key indicators of asthmatic patients' health that can be measured directly using wearable sensors to provide continuous and constant patient monitoring or indirectly by estimations based on proven algorithms using electrocardiogram (ECG), photoplethysmogram (PPG), and chest movements. ECG and PPG signals are widely used in smart watches and chest bands, enabling easy integration of a more extensive body sensor framework for asthmatic exacerbation prediction. Other vital signs used in asthma patient monitoring include blood oxygen saturation, temperature, blood pressure, verbal sound, and pain responses. The use of wearable vital signs enabled a broad range of wearable sensor application scenarios for asthma monitoring and management.

A Hallmark-Based Six-Gene Expression Signature to Assess Colorectal Cancer and Its Recurrence Risk.

Purpose: As part of the effort to establish a general profile for solid tumors, the aim of this study was to develop a real-time polymerase chain reaction (RT-PCR)-based assay to assess colorectal cancer (CRC) and its recurrence risk utilizing the limited amounts of tissues available from biopsies through colonoscopy. Materials and Methods: Six candidate genes, reflecting the hallmarks of cancer cells, were identified by analyzing the gene expression profiles of primary invasive tumors in the public database. The expression of these genes in CRC and noncancerous colon tissues was quantified by RT-quantitative PCR. Classifiers were then generated to distinguish the tumors from the normal colon tissues, and to assess the risk of CRC recurrence based on the disease-free survival time, overall survival time, and metastatic status of the patients. Results: The expression profile of a five-gene panel was utilized to build a model that is capable of distinguishing CRC cancer tissues from noncancerous colorectal tissues (p < 0.0001). A classifier based on the expression signature of four genes, three of which were included in the five-gene panel, was then developed for assessing the tumor recurrence risk. This classifier could correctly identify those with a poor likelihood of survival (high risk of recurrence) >80% of time. There was a significant difference in disease-free survival time between patients in the low recurrence group and those in the high-risk group. Conclusion: The expression signatures of the six genes that reflect the genetic hallmarks of cancer cells could serve as a biomarker for identifying CRC and assessing the risk of recurrence with high sensitivity and specificity.

Mapping Hypoxia in Renal Carcinoma with Oxygen-enhanced MRI: Comparison with Intrinsic Susceptibility MRI and Pathology.

Purpose To cross-validate T1-weighted oxygen-enhanced (OE) MRI measurements of tumor hypoxia with intrinsic susceptibility MRI measurements and to demonstrate the feasibility of translation of the technique for patients. Materials and Methods Preclinical studies in nine 786-0-R renal cell carcinoma (RCC) xenografts and prospective clinical studies in eight patients with RCC were performed. Longitudinal relaxation rate changes (∆R1) after 100% oxygen inhalation were quantified, reflecting the paramagnetic effect on tissue protons because of the presence of molecular oxygen. Native transverse relaxation rate (R2*) and oxygen-induced R2* change (∆R2*) were measured, reflecting presence of deoxygenated hemoglobin molecules. Median and voxel-wise values of ∆R1 were compared with values of R2* and ∆R2*. Tumor regions with dynamic contrast agent-enhanced MRI perfusion, refractory to signal change at OE MRI (referred to as perfused Oxy-R), were distinguished from perfused oxygen-enhancing (perfused Oxy-E) and nonperfused regions. R2* and ∆R2* values in each tumor subregion were compared by using one-way analysis of variance. Results Tumor-wise and voxel-wise ∆R1 and ∆R2* comparisons did not show correlative relationships. In xenografts, parcellation analysis revealed that perfused Oxy-R regions had faster native R2* (102.4 sec-1 vs 81.7 sec-1) and greater negative ∆R2* (-22.9 sec-1 vs -5.4 sec-1), compared with perfused Oxy-E and nonperfused subregions (all P < .001), respectively. Similar findings were present in human tumors (P < .001). Further, perfused Oxy-R helped identify tumor hypoxia, measured at pathologic analysis, in both xenografts (P = .002) and human tumors (P = .003). Conclusion Intrinsic susceptibility biomarkers provide cross validation of the OE MRI biomarker perfused Oxy-R. Consistent relationship to pathologic analyses was found in xenografts and human tumors, demonstrating biomarker translation. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

A novel approach to analyze lysosomal dysfunctions through subcellular proteomics and lipidomics: the case of NPC1 deficiency.

Superparamagnetic iron oxide nanoparticles (SPIONs) have mainly been used as cellular carriers for genes and therapeutic products, while their use in subcellular organelle isolation remains underexploited. We engineered SPIONs targeting distinct subcellular compartments. Dimercaptosuccinic acid-coated SPIONs are internalized and accumulate in late endosomes/lysosomes, while aminolipid-SPIONs reside at the plasma membrane. These features allowed us to establish standardized magnetic isolation procedures for these membrane compartments with a yield and purity permitting proteomic and lipidomic profiling. We validated our approach by comparing the biomolecular compositions of lysosomes and plasma membranes isolated from wild-type and Niemann-Pick disease type C1 (NPC1) deficient cells. While the accumulation of cholesterol and glycosphingolipids is seen as a primary hallmark of NPC1 deficiency, our lipidomics analysis revealed the buildup of several species of glycerophospholipids and other storage lipids in selectively late endosomes/lysosomes of NPC1-KO cells. While the plasma membrane proteome remained largely invariable, we observed pronounced alterations in several proteins linked to autophagy and lysosomal catabolism reflecting vesicular transport obstruction and defective lysosomal turnover resulting from NPC1 deficiency. Thus the use of SPIONs provides a major advancement in fingerprinting subcellular compartments, with an increased potential to identify disease-related alterations in their biomolecular compositions.