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Single cell RNA sequencing (scRNA-seq) is a novel high-throughput technique that enables the investigation of a single cell’s entire transcriptome. It elucidates intricate cellular networks and generates indices that will eventually enable the development of more targeted and personalized medications. The importance of scRNA-seq has been highlighted in complex biological systems such as cancer and the immune system, which exhibit significant cellular heterogeneity. Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer-related death globally. Chemotherapy continues to be used to treat these patients. However, 5-FU has been utilized in chemotherapy regimens with oxaliplatin and irinotecan since the 1960s and is still used today. Additionally, chemotherapy-resistant metastatic CRCs with poor prognoses have been treated with immunotherapy employing monoclonal antibodies, immune checkpoint inhibitors, adoptive cell therapy and cancer vaccines. Personalized immunotherapy employing tumor-specific neoantigens allows for treating each patient as a distinct group. Sequencing and multi-omics approaches have helped us identify patients more precisely in the last decade. The introduction of modern methods and neoantigen-based immunotherapy may usher in a new era in treating CRC. The unmet goal is to better understand the cellular and molecular mechanisms that contribute to CRC pathogenesis and resistance to treatment, identify novel therapeutic targets, and make more stratified and informed treatment decisions using single cell approaches. This review summarizes current scRNA-seq utilization in CRC research, examining its potential utility in the development of precision immunotherapy for CRC.

Objective We designed an automatic, computationally efficient, and interpretable algorithm for detecting ventricular ectopic beats in long-term, single-lead electrocardiogram recordings. Methods We built five simple, interpretable, and computationally efficient features from each cardiac cycle, including a novel morphological feature which described the distance to the median beat in the recording. After an unsupervised subject-specific normalization procedure, we trained an ensemble binary classifier using the AdaBoost algorithm Results After our classifier was trained on subset DS1 of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, our classifier obtained an F1 score of 94.35% on subset DS2 of the same database. The same classifier achieved F1 scores of 92.06% on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database and 91.40% on the MIT-BIH Long-term database. A phenotype-specific analysis of model performance was afforded by the annotations included in the St. Petersburg INCART Arrhythmia database Conclusion The five features this novel algorithm employed allowed our ventricular ectopy detector to obtain high precision on previously unseen subjects and databases Significance Our ventricular ectopy detector will be used to study the relationship between premature ventricular contractions and adverse patient outcomes such as congestive heart failure and death.

STUDY OBJECTIVES: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multiple sleep centers is time-consuming, we developed an artificial intelligence system to efficiently evaluate the reliability and consistency of sleep scoring and hence the sleep center quality. METHODS: An interpretable machine learning algorithm was used to evaluate the interrater reliability (IRR) of sleep stage annotation among sleep centers. The artificial intelligence system was trained to learn raters from 1 hospital and was applied to patients from the same or other hospitals. The results were compared with the experts’ annotation to determine IRR. Intracenter and intercenter assessments were conducted on 679 patients without sleep apnea from 6 sleep centers in Taiwan. Centers with potential quality issues were identified by the estimated IRR. RESULTS: In the intracenter assessment, the median accuracy ranged from 80.3%–83.3%, with the exception of 1 hospital, which had an accuracy of 72.3%. In the intercenter assessment, the median accuracy ranged from 75.7%–83.3% when the 1 hospital was excluded from testing and training. The performance of the proposed method was higher for the N2, awake, and REM sleep stages than for the N1 and N3 stages. The significant IRR discrepancy of the 1 hospital suggested a quality issue. This quality issue was confirmed by the physicians in charge of the 1 hospital. CONCLUSIONS: The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality. CITATION: Liu G-R, Lin T-Y, Wu H-T, et al. Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm. J Clin Sleep Med. 2021;17(2):159–166.

What is the influence of chronic maternal prenatal stress (PS) on fetal iron homeostasis? In a prospective case-control study in 164 pregnant women, we show that cord blood transferrin saturation is lower in male stressed neonates. The total effect of PS exposure on fetal ferritin revealed a decrease of 15.4% compared with controls. Electrocardiogram-based Fetal Stress Index (FSI) identified affected fetuses non-invasively during the third trimester of gestation. FSI-based timely detection of fetuses affected by PS can support early individualized iron supplementation and neurodevelopmental follow-up to prevent long-term sequelae due to PS-exacerbated impairment of the iron homeostasis., FELICITy study; ClinicalTrials.gov registration number is NCT03389178

Background The World Health Organization (WHO) estimated that 29% of global tuberculosis (TB) and almost 47% of childhood TB cases were not reported to national TB programs in 2019. In Cambodia, most childhood TB cases were reported from health facilities supported by the Global Fund to Fight AIDS, Tuberculosis, and Malaria in 2019. This study aimed to compare the healthcare providers' knowledge, attitude, and practices (KAP) on childhood TB case detection in operational districts (ODs) with high and low childhood TB case detection in Cambodia. Methods We conducted a cross-sectional study between November and December 2020 among healthcare providers in 10 purposively selected ODs with high childhood TB case detection and 10 ODs with low childhood TB case detection. A total of 110 healthcare providers from referral hospitals (RHs) and 220 from health centers (HCs) were interviewed. We collected information on socio-demographic characteristics, training, and KAP on childhood TB. Pearson's Chi-square or Fisher's exact and Student's t-tests were performed to explore the differences in KAP of healthcare providers from ODs with low vs. high childhood TB detection. Results Of the 330 respondents, 193 were from ODs with high childhood TB case detection, and 66.67% were from HCs. A significantly higher proportion (46.11%) of respondents from ODs with high childhood TB case detection received training on childhood TB within the past two years than those from low childhood TB case detection ODs (34.31%) (p = 0.03). Key knowledge on childhood TB was not significantly different among respondents from ODs with high and low childhood TB case detection. A significantly higher proportion of respondents from ODs with high childhood TB case detection had a good attitude (98.96 vs. 97.08%, p = 0.002) and performed good practices (58.55 vs. 45.26%, p = 0.02) on contact investigation in the community than those from low childhood TB case detection ODs. Conclusions Healthcare providers from ODs with high childhood TB detection had better attitudes and practices towards childhood TB. The attitudes and practices need to be improved among healthcare providers in ODs with low case detection. Further investment in training and experience sharing on childhood TB case detection among healthcare providers is needed to improve childhood TB case detection.

Viral- and host-targeted traditional Chinese medicine (TCM) formulae NRICM101 and NRICM102 were administered to hospitalized patients with COVID-19 during the mid-2021 outbreak in Taiwan. We report the outcomes by measuring the risks of intubation or admission to intensive care unit (ICU) for patients requiring no oxygen support, and death for those requiring oxygen therapy.This multicenter retrospective study retrieved data of 840 patients admitted to 9 hospitals between May 1 and July 26, 2021. After propensity score matching, 302 patients (151 received NRICM101 and 151 did not) and 246 patients (123 received NRICM102 and 123 did not) were included in the analysis to assess relative risks.During the 30-day observation period, no endpoint occurred in the patients receiving NRICM101 plus usual care while 14 (9.27%) in the group receiving only usual care were intubated or admitted to ICU. The numbers of deceased patients were 7 (5.69%) in the group receiving NRICM102 plus usual care and 27 (21.95%) in the usual care group. No patients receiving NRICM101 transitioned to a more severe status; NRICM102 users were 74.07% less likely to die than non-users (relative risk= 25.93%, 95% confidence interval 11.73%-57.29%).NRICM101 and NRICM102 were significantly associated with a lower risk of intubation/ICU admission or death among patients with mild-to-severe COVID-19. This study provides real-world evidence of adopting broad-spectrum oral therapeutics and shortening the gap between outbreak and effective response. It offers a new vision in our preparation for future pandemics.

Objective: While the survival of extremely premature infants has improved over the past decades, the rate of complications – especially for bronchopulmonary dysplasia (BPD) – remains un-acceptably high. Over the past 50 years, no safe therapy has had a substantial impact on the incidence and severity of BPD. Methods: This may stem from the multifactorial disease pathogenesis and the increasing lung imma-turity. Mesenchymal Stromal Cells (MSCs) display pleiotropic effects and show promising results in neonatal rodents in preventing or rescuing lung injury without adverse effects. Early phase clinical trials are now underway to determine the safety and efficacy of this therapy in extremely premature infants. Results and Conclusion: This review summarizes our current knowledge about MSCs, their mecha-nism of action and the results of preclinical studies that provide the rationale for early phase clinical trials and discuss remaining gaps in our knowledge

Despite prolonged and cumulative exposure during gestation, little is known about the fetal response to maternal sleep. Eighty-four pregnant women with obesity (based on pre-pregnancy BMI) participated in laboratory-based polysomnography (PSG) with continuous fetal electrocardiogram monitoring at 36 weeks gestation. Multilevel modeling revealed both correspondence and lack of it in maternal and fetal heart rate patterns. Fetal heart rate (fHR) and variability (fHRV), and maternal heart rate (mHR) and variability (mHRV), all declined during the night, with steeper rates of decline prior to 01:00. fHR declined upon maternal sleep onset but was not otherwise associated with maternal sleep stage; fHRV differed during maternal REM and NREM. There was frequent maternal waking after sleep onset (WASO) and fHRV and mHRV were elevated during these episodes. Cross-correlation analyses revealed little temporal coupling between maternal and fetal heart rate, except during WASO, suggesting that any observed associations in maternal and fetal heart rates during sleep are the result of other physiological processes. Implications of the maternal sleep context for the developing fetus are discussed, including the potential consequences of the typical sleep fragmentation that accompanies pregnancy.

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS, however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0&plusmn, 4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

Aims Detrusor overactivity (DO) of the bladder is a finding on urodynamic studies (UDS) that often correlates with lower urinary tract symptoms and drives management. However, UDS interpretation remains nonstandardized. We sought to develop a mathematical model to reliably identify DO in UDS. Methods We utilized UDS archive files for studies performed at our institution between 2013 and 2019. Raw tracings of vesical pressure, abdominal pressure, detrusor pressure, infused volume, and all annotations during UDS were obtained. Patients less than 1 year old, studies with calibration issues, or those with significant artifacts were excluded. In the training set, five representative DO patterns were identified. Candidate Pdet signal segments were matched to representative DO patterns. Manifold learning and dynamic time warping algorithms were used. Five-fold cross validation (CV) was used to evaluate the performance. Results A total of 799 UDS studies were included. The median age was 9 years (range, 1-33). There were 1,742 DO events that did not overlap with annotated artifacts (cough, cry, valsalva, movements). The AUC of the training sets from the five-fold CV was 0.84 ± 0.01. The five-fold CV leads to an overall accuracy 81.35%, and sensitivity and specificity of detecting DO events are 76.92% and 81.41%, respectively, in the testing set. Conclusions Our predictive model using machine learning algorithms provides promising performance to facilitate automated identification of DO in UDS. This would allow for standardization and potentially more reliable UDS interpretation. Signal processing and machine learning interpretation of the other components of UDS are forthcoming.

Dermal fibroblasts isolated from an apparently healthy 50-year-old man were successfully transformed into induced pluripotent stem cells (iPSCs) by using the integration-free CytoTune-iPS Sendai Reprogramming method. The generated iPSC line has been expanded under feeder-free conditions and displayed all hallmarks of a standard pluripotent stem cell line such as a normal karyotype, expression of pluripotent factors and differentiation capacity into the three germ layers.

BACKGROUND Cardiovascular waveforms contain information for clinical diagnosis. By learning and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimensional structure and display it as a novel 3-dimensional (3D) image. We hypothesize that the shape of this structure conveys clinically relevant inner dynamics information. METHODS To validate this hypothesis, we investigate the electrocardiography (ECG) waveform for ischemic heart disease and arterial blood pressure (ABP) waveform in dynamic vasoactive episodes. We model each beat or pulse to be a point lying on a manifold-like a surface-and use the diffusion map (DMap) to establish the relationship among those pulses. The output of the DMap is converted to a 3D image for visualization. For ECG datasets, first we analyzed the non-ST-elevation ECG waveform distribution from unstable angina to healthy control in the 3D image, and we investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG waveform changes. For ABP datasets, we analyzed waveforms collected under endotracheal intubation and administration of vasodilator. To quantify the dynamic separation, we applied the support vector machine (SVM) analysis and reported the total accuracy and macro-F1 score. We further performed the trajectory analysis and derived the moving direction of successive beats (or pulses) as vectors in the high-dimensional space. RESULTS For the non-ST-elevation ECG, a hierarchical tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented in the 3D image (accuracy = 97.6%, macro-F1 = 96.1%). The DMap helps quantify and visualize the evolving direction of intraoperative ST-elevation myocardial episode in a 1-hour period (accuracy = 97.58%, macro-F1 = 96.06%). The ABP waveform analysis of Nicardipine administration shows interindividual difference (accuracy = 95.01%, macro-F1 = 96.9%) and their common directions from intraindividual moving trajectories. The dynamic change of the ABP waveform during endotracheal intubation shows a loop-like trajectory structure, which can be further divided using the manifold learning knowledge obtained from Nicardipine. CONCLUSIONS The DMap and the generated 3D image of ECG or ABP waveforms provides clinically relevant inner dynamics information. It provides clues of acute coronary syndrome diagnosis, shows clinical course in myocardial ischemic episode, and reveals underneath physiological mechanism under stress or vasodilators.

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible., Sensors, 20 (7), ISSN:1424-8220

Global statistics have placed colorectal cancer (CRC) as the third most frequently diagnosed cancer and the fourth principal cause of cancer-related deaths worldwide. Improving survival for CRC is as important as early detection. Personalized medicine is important in maximizing an individual’s treatment success and minimizing the risk of adverse reactions. Approaches in achieving personalized therapy in CRC have included analyses of specific genes with its clinical implications. Tumour genotyping via next-generation sequencing has become a standard practice to guide clinicians into predicting tumor behaviour, disease prognosis, and treatment response. Nevertheless, better prognostic markers are necessary to further stratify patients for personalized treatment plans. The discovery of new markers remains indispensable in providing the most effective chemotherapy in order to improve the outcomes of treatment and survival in CRC patients. This review aims to compile and discuss newly discovered, less frequently mutated genes in CRC. We also discuss how these mutations are being used to assist therapeutic decisions and their potential prospective clinical utilities. In addition, we will summarize the importance of profiling the large genomic rearrangements, gene amplification, and large deletions and how these alterations may assist in determining the best treatment option for CRC patients.

Background: Fluctuating hearing loss is characteristic of Meniere's Disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility. Aims/Objectives: To find parameters for predicting MD hearing outcomes. Material and Methods: We applied machine learning techniques to analyse transient-evoked otoacoustic emission (TEOAE) signals recorded from patients with MD. Thirty unilateral MD patients were recruited prospectively after onset of acute cochleo-vestibular symptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recorded longitudinally. Denoised TEOAE signals were projected onto the three most prominent principal directions through a linear transformation. Binary classification was performed using a support vector machine (SVM). TEOAE signal parameters, including signal energy and group delay, were compared between improved and nonimproved groups using Welchs t-test. Results: Signal energy did not differ (p = 0.64) but a significant difference in 1-kHz (p = 0.045) group delay was recorded between improved and nonimproved groups. The SVM achieved a cross-validated accuracy of >80% in predicting hearing outcomes. Conclusions and Significance: This study revealed that baseline TEOAE parameters obtained during acute MD episodes, when processed through machine learning technology, may provide information on outer hair cell function to predict hearing recovery., This is a journal version accepted by Acta Oto-Laryngologica on December 6, 2019

Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of 'fetal programming' is mediated by the effects of the prenatal experience on the developing hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS). The HPA axis is a dynamic system regulating homeostasis, especially the stress response, and is highly sensitive to adverse early life experiences. We review the evidence for the effects of PS on fetal programming of the HPA axis and the ANS. We derive a multi-scale multi-species approach to devising preclinical and clinical studies to identify early non-invasively available pre- and postnatal biomarkers of these programming effects. Such approach would identify adverse postnatal brain developmental trajectories, a prerequisite for designing therapeutic interventions. The multiple scales include the biomarkers reflecting changes in the brain epigenome, metabolome, microbiome and the ANS activity gauged via an array of advanced non-invasively obtainable properties of fetal heart rate fluctuations. The proposed framework has the potential to reveal mechanistic links between maternal stress during pregnancy and changes across these physiological scales. Such biomarkers may hence be useful as early and non-invasive predictors of neurodevelopmental trajectories influenced by the PS. We conclude that studies into PS effects must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive biomarkers., Comment: Focused review, 13 pages, 5 figures

Purpose: To investigate whether lacrimal canaliculus epithelial stem cells (LCESC) could be isolated and expanded in vitro. Methods: The lacrimal canaliculus epithelium of 6 patients with limbal stem cell deficiency (LSCD) caused by alkali burn or Stevens Johnson Syndrome were examined by lacrimal endoscope. Cadaveric eyelids were fixed and prepared for cross section and stained with HE and antibodies against PCK, Vim, p63α, SCF and c-Kit. Canaliculus tissue was separated under an operating microscope using a lacrimal probe as an indicator and digested with collagenase A. The clusters of epithelial cells with closely associated stroma were further digested with Trypsin/EDTA to obtain single cells for culture on Matrigel-coated plastic plates in MESCM media. The expression of SCF, c-Kit and p63α was determined by immunostaining. The colony-forming efficiency on 3T3 feeder layers was also measured by calculating the percentage of the clone number divided by the total number cells seeded. Results: The epithelial layers of five out of six inferior lacrimal canaliculi and all the six superior lacrimal canaliculi were visually normal in appearance. Five to fifteen layers of the epithelium in the human lacrimal canaliculi were present with a small, tightly compacted basal layer of cells expressing PCK, p63α, SCF and c-Kit. LCESC were isolated by collagenase A and obtained clonal growth in MESCM. The colony-forming efficiency of LCESC holoclones on a 3T3 feeder layer was 3.2%, compared to 1.9% for those of limbal stem cells (LSC). Conclusions: Herein, we first report that LCESCs can be isolated and have stem cell characteristics, similar to those of LSCs. Such a discovery raises a promising substrate resource of stem cells for LSC reconstruction in LSCD patients.

Fetal Alcohol Spectrum Disorders encompass a wide range of birth defects in children born to mothers who consumed alcohol during pregnancy. Typical mental impairments in FASD include difficulties in life adaptation and learning and memory, deficits in attention, visuospatial skills, language and speech disabilities, mood disorders and motor disabilities. Multimodal imaging methods have enabled in vivo studies of the teratogenic effects of alcohol on the central nervous system, giving more insight into the FASD phenotype. This paper offers an up-to-date comprehensive review of radiological findings in the central nervous system in studies of prenatal alcohol exposure in both humans and translational animal models, including Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography, Single Photon Emission Tomography and Ultrasonography.

We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels. We design an algorithm based on the optimal-shrinkage and the nonlocal Euclidean median under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A. For the morphological analysis, we propose to simulate semi-real databases by mixing the MIT-BIH Normal Sinus Rhythm Database and MITDB Arrhythmia Database. For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia. To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose., Comment: 25 pages, 6 figures

Recent studies from high-risk countries such as the US, Denmark and Ireland have shown rising incidence rates of hormone receptor (HR)-positive and falling rates of HR-negative breast cancers (BC). However, it remains unclear whether a similar pattern occurs in low-risk countries. Detailed clinical and risk factor data were collected from 2,977 female invasive BC patients (≥20 years) in Sarawak General Hospital, Malaysia, representing 93% of the population. The population-at-risk was obtained from the Department of Statistics Malaysia. Secular trends in age-standardized incidence rates were assessed using estimated average annual percent changes. Associations between established BC risk factors and tumor subtypes defined by HR or joint human epidermal growth factor receptor 2 (HR/HER2) status were examined by case-case comparisons using logistic regression. From 2006 to 2015, incidence rates increased for HR-positive cancers by 4.46%/year (95% CI = 2.19-6.78) and decreased for HR-negative cancers by 2.29%/year (95% CI = -4.31 to -0.24). When further stratified by HER2, the most contrasting difference in linear trends was observed between HR+/HER2- and HR-/HER2- subtypes. After controlling for potential confounders, cases with excess body weight (ORoverweight vs. normal = 0.82; 95% CI = 0.69-0.98; ORobese vs. normal = 0.62; 95% CI = 0.48-0.80), later age at first birth (OR≥26 years vs.

Breast cancer is the most common and the second leading cause of cancer-related deaths in women. It has two distinctive hallmarks: rapid abnormal growth and the ability to invade and metastasize. During metastasis, cancer cells are thought to form actin-rich protrusions, called invadopodia, which degrade the extracellular matrix. Current breast cancer treatments, particularly chemotherapy, comes with adverse effects like immunosuppression, resistance development and secondary tumour formation. Hence, naturally-occurring molecules claimed to be less toxic are being studied as new drug candidates. Ampelopsin E, a natural oligostilbene extracted from Dryobalanops species, has exhibited various pharmacological properties, including anticancer and anti-inflammatory activities. However, there is yet no scientific evidence of the effects of ampelopsin E towards metastasis. Scratch assay, transwell migration and invasion assays, invadopodia and gelatin degradation assays, and ELISA were used to determine the effects of ampelopsin E towards the invasiveness of MDA-MB-231 cells. Strikingly in this study, ampelopsin E was able to halt migration, transmigration and invasion in MDA-MB-231 cells by reducing formation of invadopodia and its degradation capability through significant reduction (p &lt, 0.05) in expression levels of PDGF, MMP2, MMP9 and MMP14. In conclusion, ampelopsin E reduced the invasiveness of MDA-MB-231 cells and was proven to be a potential alternative in treating TNBC.

We introduce a recently developed nonlinear-type time-frequency analysis tool, synchrosqueezing transform (SST), to quantify complicated and noisy physiological waveform that has time-varying amplitude and frequency. We apply it to analyze a peripheral venous pressure (PVP) signal recorded during a seven hours aortic valve replacement procedure. In addition to showing the captured dynamics, we also quantify how accurately we can estimate the instantaneous heart rate from the PVP signal.

Bioactive compounds from endophytic fungi exhibit diverse biological activities which include anticancer effect. Capitalising on the abundance of unexplored endophytes that reside within marine plants, this study assessed the anticancer potential of ethyl acetate endophytic fungal extracts (i.e. MBFT Tip 2.1, MBL 1.2, MBS 3.2, MKS 3 and MKS 3.1) derived from leaves, stem and fruits of marine plants that grow along Morib Beach, Malaysia. For identification of endophytic fungi, EF 4/ EF 3 and ITS 1/ ITS 4 PCR primer pairs were used to amplify the fungal 18S rDNA sequence and ITS region sequence, respectively. The resultant sequences were subjected to similarity search via the NCBI GenBank database. High-performance thin layer chromatography (HPTLC) hyphenated with bioassays was used to characterise the extracts in terms of their phytochemical profiles and bioactivity. Microchemical derivatisation was used to assess polyphenolic and phytosterol/ terpenoid content whereas biochemical derivatisation was used to establish antioxidant activities and α-amylase enzyme inhibition. The sulforhodamine B (SRB) assay was used to assess the anticancer effect of the extracts against HCT116 (a human colorectal cancer cell line). The present results indicated MBS 3.2 (Penicillium decumbens) as the most potent extract against HCT116 (IC50 = 0.16 μg/mL), approximately 3-times more potent than 5-flurouracil (IC50 = 0.46 μg/mL). Stepwise multiple regression method suggests that the anticancer effect of MBS 3.2 could be associated with high polyphenolic content and antioxidant potential. Nonlinear regression analysis confirmed that low to moderate α-amylase inhibition exhibits maximum anticancer activity. Current findings warrant further in-depth mechanistic studies.

See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring. The winning algorithms forecast seizures at rates significantly greater than chance., See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.

Summary Background Due to increasing concerns about post-chikungunya (pCHIK) rheumatic disorders in Latin America we aimed to evaluate its occurrence in travelers returning to NYC from the Caribbean. Method Patients diagnosed with chikungunya (CHIK) during 2014 at the Bronx-Lebanon Hospital Center (Bronx, NewYork) were identified by reviewing laboratory and electronic medical records. Patients and caregivers of pediatric patients were interviewed by phone ≥9 months after the CHIK diagnosis to survey for chronic symptomatology and current health care needs. Reported chronic musculoskeletal complaints were categorized according to validated criteria. Results A total of 28 patients (54% females, median age [range] of 51.5 [0, 88] years) diagnosed with CHIK at our center were identified. Most (82%) had returned from the Dominican Republic. Nineteen (68%) patients were successfully contacted at a median (range) of 13 (9, 16) months since the acute diagnosis. A third (37%) reported ongoing complaints related to CHIK including joint pain (32%), muscle pain (32%), and joint swelling (26%). A presumptive diagnosis of pCHIK chronic inflammatory arthritis ( n = 4) and pCHIK musculoskeletal disorder ( n = 3) was established. Conclusions A third of travelers with CHIK acquired in the Caribbean may be at risk for developing persistent symptoms suggestive of pCHIK rheumatic disorder.

Capnography involves the measurement of end-tidal CO2 (EtCO2) values to detect hypoventilation in patients undergoing sedation. In a previous study, we reported that initiating a flexible bronchoscopy (FB) examination only after detecting signs of hypoventilation could reduce the risk of hypoxemia without compromising the tolerance of the patient for this type of intervention. We hypothesize that hypoventilation status could be determined with greater precision by combining thoracic impedance-based respiratory signals, RESP, and EtCO2 signals obtained from a nasal-oral cannula. Retrospective analysis was conducted on RESP and EtCO2 waveforms obtained from patients during the induction of sedation using propofol for bronchoscopic examination in a previous study. EtCO2 waveforms associated with hypoventilation were then compared with RESP patterns, patient variables, and sedation outcomes. Signals suitable for analysis were obtained from 44 subjects, 42 of whom presented indications of hypoventilation, as determined by EtCO2 waveforms. Two subtypes of hypoventilation were identified by RESP: central-predominant (n = 22, flat line RESP pattern) and non-central-predominant (n = 20, RESP pattern indicative of respiratory effort with upper airway collapse). Compared to cases of non-central-predominant hypoventilation, those presenting central-predominant hypoventilation during induction were associated with a lower propofol dose (40.2 ± 18.3 vs. 60.8 ± 26.1 mg, p = 0.009), a lower effect site concentration of propofol (2.02 ± 0.33 vs. 2.38 ± 0.44 µg/ml, p = 0.01), more rapid induction (146.1 ± 105.5 vs. 260.9 ± 156.2 s, p = 0.01), and lower total propofol dosage (96.6 ± 41.7 vs. 130.6 ± 53.4 mg, p = 0.04). Hypoventilation status (as revealed by EtCO2 levels) could be further classified by RESP into central-predominant or non-central-predominant types. It appears that patients with central-predominant hypoventilation are more sensitive to propofol during the induction of sedation. RESP values could be used to tailor sedation management specifically to individual patients.

High-frequency heart rate variability (HRV) has identified parasympathetic nervous system alterations in autism spectrum disorder (ASD). In a cohort of school-aged children with and without ASD, we test a set of alternative linear and nonlinear HRV measures, including phase rectified signal averaging, applied to a segment of resting ECG, for associations with ASD vs. other psychiatric conditions. Using machine learning, we identify HRV measures derived from time, frequency, and geometric signal-analytical domains that (1) identify children with ASD relative to peers with receiver operating curve area of .89, and (2) differentiate such children from those with conduct problems or depression. Despite the small cohort and lack of prospective external validation, these preliminary results warrant larger prospective validation studies.

Background Limited evidence, mostly from studies in Western populations, suggests that the prognostic effects of lifestyle-related risk factors may be molecular subtype-dependent. Here, we examined whether pre-diagnostic lifestyle-related risk factors for breast cancer are associated with clinical outcomes by molecular subtype among patients from an understudied Asian population. Methods In this population-based case series, we evaluated breast cancer risk factors in relation to 10-year all-cause mortality (ACM) and 5-year recurrence by molecular subtype among 3012 women with invasive breast cancer in Sarawak, Malaysia. A total of 579 deaths and 314 recurrence events occurred during a median follow-up period of ~ 24 months. Subtypes (luminal A-like, luminal B-like, HER2-enriched, triple-negative) were defined using immunohistochemical markers for hormone receptors and human epidermal growth factor receptor 2 (HER2) in conjunction with histologic grade. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between risk factors and ACM/recurrence were estimated in subtype-specific Cox regression models. Results We observed heterogeneity in the relationships between parity/breastfeeding, age at first full-term pregnancy (FFP), family history, body mass index (BMI), and tumor subtype (p value 30 vs

Background and purpose With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings. Methods In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches. Results As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account. Conclusion The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings. Electronic supplementary material The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.

Objective Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series. Approach We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. Main results On our private database of 27 participants, our accuracy, sensitivity, specificity, and [Formula: see text] values for predicting the wake stage are [Formula: see text], 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. Significance This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.

Object It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a reliable pulse transit time (PTT) signal. Approach This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit. All data sets include recordings of PPG and ECG using a commonly deployed patient monitor. We calculated the PTT signal offline. Main results We report a novel constant oscillatory pattern in the PTT signal and identify this pattern as a sawtooth artifact. We apply an approach based on the de-shape method to visualize, quantify and validate this sawtooth artifact. Significance The PPG and ECG signals not designed for the PTT evaluation may contain unwanted artifacts. The PTT signal should be calibrated before analysis to avoid erroneous interpretation of its physiological meaning.

The linear part of transient evoked (TE) otoacoustic emission (OAE) is thought to be generated via coherent reflection near the characteristic place of constituent wave components. Because of the tonotopic organization of the cochlea, high frequency emissions return earlier than low frequencies; however, due to the random nature of coherent reflection, the instantaneous frequency (IF) and amplitude envelope of TEOAEs both fluctuate. Multiple reflection components and synchronized spontaneous emissions can further make it difficult to extract the IF by linear transforms. In this paper, we propose to model TEOAEs as a sum of {\em intrinsic mode-type functions} and analyze it by a {nonlinear-type time-frequency analysis} technique called concentration of frequency and time (ConceFT). When tested with synthetic OAE signals {with possibly multiple oscillatory components}, the present method is able to produce clearly visualized traces of individual components on the time-frequency plane. Further, when the signal is noisy, the proposed method is compared with existing linear and bilinear methods in its accuracy for estimating the fluctuating IF. Results suggest that ConceFT outperforms the best of these methods in terms of optimal transport distance, reducing the error by 10 to {21\%} when the signal to noise ratio is 10 dB or below.

Fetal magnetocardiography (fMCG) provides accurate and reliable measurements of electrophysiological events in the fetal heart and is capable of studying fetuses with congenital heart diseases. A variety of techniques exist to extract the fMCG signal with the demand for non-invasively obtained fetal cardiac information. To the best of our knowledge, there is no comparative study published in the field as to how the various extraction algorithms perform. We perform a comparative study of the ability of five methods to extract the fMCG using real biomagnetic signals, two of those methods are applied to real fMCG data for the first time. Biomagnetic signals were recorded and processed with each of the five methods to obtain fMCG. The R peaks of the fMCG traces were obtained via a peak-detection algorithm. From whole recording for each method, the fetal heart rate (FHR) was calculated and used to perform FHR variability (FHRV) analysis. Additionally, we calculated durations from the PQRST complex from time-averaged data during sinus rhythm. The five methods recovered the fMCG signals, but two of them were able to extract cleaner fMCG and the morphology was observed from the continuous data. The time-averaged data showed very similar morphologies between methods, but two of them displayed a signal amplitude reduction on the R-waves and T-waves. Values of PQRST durations, FHR and FHRV were in the range of previous fetal cardiac studies. We have compared five methods for fMCG extraction and showed their ability to perform fMCG analysis.

Cervical cancer is probably caused by a sexually transmitted agent. A case-control study was conducted in three hospitals in Thailand to investigate further the role of male sexual behavior, particularly regarding sexual contacts with prostitutes, in the development of this disease. Data were obtained from interviews with 225 married women with invasive squamous cell cervical carcinoma and 791 hospitalized controls, all of whom reported having only one sexual partner, and from interviews with their husbands. Risk of cervical cancer was strongly related to the women's husbands having visited prostitutes without using a condom when the husbands were less than 30 years old. A strong increasing trend in risk in relation to decreasing frequency of the husbands' condom use with prostitutes was observed, and a weaker increasing trend in risk with husbands' estimated lifetime total number of visits to prostitutes was found. The average latent period between the women's likely initial exposure to a sexually transmitted oncogenic agent and her diagnosis of invasive cervical cancer was about a quarter of a century. Regular use of condoms by customers of prostitutes could reduce the number of invasive cervical cancer cases in the general population of Thailand by at least one fourth.Researchers compared data on 225 Thai married women with invasive squamous cell cervical cancer admitted to Siriraj and Chulalongkorn hospitals in Bangkok and at Maharaj Nakorn Chiang Mai Hospital during October 1979-September 1998 with data on 791 hospitalized Thai controls to examine the role of male sexual behavior and prostitution in the development of cervical cancer and the likely protective effect of condom use against cervical cancer. All the cases and controls claimed to have had only one sexual partner. Interviews were conducted with the husbands of all the cases and controls. There was a significant trend of increasing risk of cervical cancer as the frequency of husband's condom use with prostitutes declined (p = 0.004) (relative risk [RR] = 2.05 for rarely or never, 1.24 for sometimes, and 0.96 for always or frequently). The increasing risk of cervical cancer associated with little or no condom use with prostitutes was highest when the husbands were less than 30 years old (RR = 2.11 vs. 1.56-1.81 for age 20 or older). There was a weaker trend of increasing risk of cervical cancer with the husband's estimated lifetime total number of visits to prostitutes (p = 0.12). On average, the latent period between the wife's likely first exposure to a sexually transmitted oncogenic agent (i.e., date of their marriage) and her diagnosis of invasive cervical cancer was 24 years. These findings indicate the condom use with prostitutes would reduce the risk of cervical cancer.

PURPOSE: To characterize the spectrum of germline muta-tions in BRCA1, BRCA2, and PALB2 in population-based unselected breast cancer cases in an Asian population. Methods Germline DNA from 467 breast cancer patients in Sarawak General Hospital, Malaysia, where 93% of the breast cancer patients in Sarawak are treated, was sequenced for the entire coding region of BRCA1; BRCA2; PALB2; Exons 6, 7, and 8 of TP53; and Exons 7 and 8 of PTEN. Pathogenic variants included known pathogenic variants in ClinVar, loss of function variants, and variants that disrupt splice site. RESULTS: We found 27 pathogenic variants (11 BRCA1, 10 BRCA2, 4 PALB2, and 2 TP53) in 34 patients, which gave a prevalence of germline mutations of 2.8, 3.23, and 0.86%for BRCA1, BRCA2, and PALB2, respectively. Compared to mutation non-carriers, BRCA1 mutation carriers were more likely to have an earlier age at onset, triple-negative subtype, and lower body mass index, whereas BRCA2 mutation carriers were more likely to have a positive family history. Mutation carrier cases had worse survival compared to non-carriers; however, the association was mostly driven by stage and tumor subtype. We also iden-tified 19 variants of unknown significance, and some of them were predicted to alter splicing or transcription factor binding sites. CONCLUSION: Our data provide insight into the genetics of breast cancer in this understudied group and suggest the need for modifying genetic testing guidelines for this population with a much younger age at diagnosis and more limited resources compared with Caucasian populations.

To develop an applicable prediction for obstructive sleep apnea (OSA) is still a challenge in clinical practice. We apply a modern machine learning method, the support vector machine to establish a predicting model for the severity of OSA. The support vector machine was applied to build up a prediction model based on three anthropometric features (neck circumference, waist circumference, and body mass index) and age on the first database. The established model was then valided independently on the second database. The anthropometric features and age were combined to generate powerful predictors for OSA. Following the common practice, we predict if a subject has the apnea-hypopnea index greater then 15 or not as well as 30 or not. Dividing by genders and age, for the AHI threhosld 15 (respectively 30), the cross validation and testing accuracy for the prediction were 85.3% and 76.7% (respectively 83.7% and 75.5%) in young female, while the negative likelihood ratio for the AHI threhosld 15 (respectively 30) for the cross validation and testing were 0.2 and 0.32 (respectively 0.06 and 0.1) in young female. The more accurate results with lower negative likelihood ratio in the younger patients, especially the female subgroup, reflect the potential of the proposed model for the screening purpose and the importance of approaching by different genders and the effects of aging.

The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fetal ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modelled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sentivities and positive predictive values comparable to other published algorithms using multiple leads and many more features.

Oscillatory phenomena abound in many types of signals. Identifying the individual oscillatory components that constitute an observed biological signal leads to profound understanding about the biological system. The instantaneous frequency (IF), the amplitude modulation (AM), and their temporal variability are widely used to describe these oscillatory phenomena. In addition, the shape of the oscillatory pattern, repeated in time for an oscillatory component, is also an important characteristic that can be parametrized appropriately. These parameters can be viewed as phenomenological surrogates for the hidden dynamics of the biological system. To estimate jointly the IF, AM, and shape, this paper applies a novel and robust time-frequency analysis tool, referred to as the synchrosqueezing transform (SST). The usefulness of the model and SST are shown directly in predicting the clinical outcome of ventilator weaning. Compared with traditional respiration parameters, the breath-to-breath variability has been reported to be a better predictor of the outcome of the weaning procedure. So far, however, all these indices normally require at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\hbox{20}$\end{document}min of data acquisition to ensure predictive power. Moreover, the robustness of these indices to the inevitable noise is rarely discussed. We find that based on the proposed model, SST and only \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\hbox{3}$\end{document} min of respiration data, the ROC area under curve of the prediction accuracy is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\hbox{0.76}$\end{document}. The high predictive power that is achieved in the weaning problem, despite a shorter evaluation period, and the stability to noise suggest that other similar kinds of signal may likewise benefit from the proposed model and SST.

There is growing interest in the use of probiotic lactic acid bacteria (LAB) for prevention of hypercholesterolaemia. This study assessed the cholesterol lowering ability of Pediococcus acidilactici LAB4 and Lactobacillus plantarum LAB12 in growth media. Both LAB yielded >98% (39.2 μg/ml) cholesterol lowering in growth media. Nile Red staining indicated direct assimilation of cholesterol by the LAB. The LAB were then explored for their prophylactic (pre-treatment of HT29 cells with LAB prior to cholesterol exposure) and biotherapeutic (treatment of HT29 cells with LAB after exposure to cholesterol) use against short and prolonged exposure of HT29 cells to cholesterol, respectively. For HT29 cells pre-treated with LAB, cholesterol lowering was accompanied by down-regulation of ATP-binding cassette family transporter-type A1 (ABCA1), cluster of differentiation 36 (CD36) and scavenger receptor class B member 1 (SCARB1). HT29 cells treated with LAB after prolonged exposure to cholesterol source, on the other hand, was associated with up-regulation of ABCA1, restoration of CD36 to basal level and down-regulation of Neimann-Pick C1-Like 1 (NPC1L1). The present findings implied the potential use of LAB4 and LAB12 as part of the strategies in prevention and management of hypercholesterolaemia.

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature , and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications . We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension , criticality , bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering .

Objective Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data. Approach Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity. Main results SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain. Significance This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.

Impaired heart rate variability (HRV) has been demonstrated as a negative survival prognosticator in various diseases. We conducted this prospective study to evaluate how HRV affects brain metastasis (BM) patients. Fifty-one BM patients who had not undergone previous brain operation or radiotherapy (RT) were recruited from January 2010 to July 2012, and 40 patients were included in the final analysis. A 5-minute electrocardiogram was obtained before whole brain radiotherapy. Time domain indices of HRV were compared with other clinical factors on overall survival (OS). In the univariate analysis, Karnofsky performance status (KPS) P=0.002) and standard deviation of the normal-to-normal interval (SDNN) P=0.004) significantly predict poor survival. The multivariate analysis revealed that KPS

Background There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status. Methods This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure. Results There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals’ explanation was understandable. Conclusions It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs. Electronic supplementary material The online version of this article (doi:10.1186/s12913-017-2496-5) contains supplementary material, which is available to authorized users.

Based on third-harmonic-generation (THG) microscopy and a k-means clustering algorithm, we developed a label-free imaging cytometry method to differentiate and determine the types of human leukocytes. According to the size and average intensity of cells in THG images, in a two-dimensional scatter plot, the neutrophils, monocytes, and lymphocytes in peripheral blood samples from healthy volunteers were clustered into three differentiable groups. Using these features in THG images, we could count the number of each of the three leukocyte types both in vitro and in vivo. The THG imaging-based counting results agreed well with conventional blood count results. In the future, we believe that the combination of this THG microscopy-based imaging cytometry approach with advanced texture analysis of sub-cellular features can differentiate and count more types of blood cells with smaller quantities of blood.

The association between the J wave, a key component of the early repolarization pattern, and adverse cardiovascular outcomes remains unclear. Inconsistencies have stemmed from the different methods used to measure the J wave. We examined the association between the J wave, detected by an automated method, and adverse cardiovascular outcomes in 14,592 (mean age = 54 ± 5.8 years; 56% women; 26% black) participants from the Atherosclerosis Risk In Communities (ARIC) study. The J wave was detected at baseline (1987 to 1989) and during follow-up study visits (1990 to 1992, 1993 to 1995, and 1996 to 1998) using a fully automated method. Sudden cardiac death, coronary heart disease death, and cardiovascular mortality were ascertained from hospital discharge records, death certificates, and autopsy data through December 31, 2010. A total of 278 participants (1.9%) had evidence of a J wave. Over a median follow-up of 22 years, 4,376 of the participants (30%) died. In a multivariable Cox regression analysis adjusted for demographics, cardiovascular risk factors, and potential confounders, the J wave was not associated with an increased risk of sudden cardiac death (hazard ratio [HR] 0.74, 95% CI 0.36 to 1.50), coronary heart disease death (HR 0.72, 95% CI 0.40 to 1.32), or cardiovascular mortality (HR 1.16, 95% CI 0.87 to 1.56). An interaction was detected for cardiovascular mortality by gender with men (HR 1.54, 95% CI 1.09 to 2.19) having a stronger association than women (HR 0.74, 95% CI 0.43 to 1.25; P-interaction = 0.030). In conclusion, our findings suggest that the J wave is a benign entity that is not associated with an increased risk for sudden cardiac arrest in middle-aged adults in the United States.

O bjective: Heart rate variability (HRV) offers a noninvasive way to peek into the physiological status of the human body. When this physiological status is dynamic, traditional HRV indices calculated from power spectrum do not resolve the dynamic situation due to the issue of nonstationarity. Clinical anesthesia is a typically dynamic situation that calls for time-varying HRV analysis. Concentration of frequency and time (ConceFT) is a nonlinear time–frequency (TF) analysis generalizing the multitaper technique and the synchrosqueezing transform. The result is a sharp TF representation capturing the dynamics inside HRV. Companion indices of the commonly applied HRV indices, including time-varying low-frequency power (tvLF), time-varying high-frequency power, and time-varying low-high ratio, are considered as measures of noxious stimulation. Methods: To evaluate the feasibility of the proposed indices, we apply these indices to study two different types of noxious stimulation, the endotracheal intubation and surgical skin incision, under general anesthesia. The performance was compared with traditional HRV indices, the heart rate reading, and indices from electroencephalography. Results: The results indicate that the tvLF index performs best and outperforms not only the traditional HRV index, but also the commonly used heart rate reading. Conclusion: With the help of ConceFT, the proposed HRV indices are potential to provide a better quantification of the dynamic change of the autonomic nerve system. Significance: Our proposed scheme of time-varying HRV analysis could contribute to the clinical assessment of analgesia under general anesthesia.

Objectives Our study aimed to correlate pulse wave parameters such as augmentation index (AI) and heart rate variability with traditional Chinese medicine (TCM) constitution for evaluating health status. Design Out of 177 subjects, 69 healthy subjects were enrolled in the present study, and others were excluded because of cardiovascular, liver, kidney, or other diseases. Each subject was invited to complete pulse wave examination and the Constitution in Chinese Medicine Questionnaire. Independent Student's t -tests, Mann-Whitney tests, and binary logistic regression analysis were used to analyse the correlation between pulse wave parameters and TCM constitution. Results Qi-deficient individuals had higher AI (p=0.006) and lower diastolic blood pressure (p=0.011); yang-deficient individuals had lower dP/dt max (p=0.030), systolic blood pressure (p=0.020), and pulse pressure (p=0.048); and damp-heat individuals had higher subendocardial viability index (SEVI) scores (p=0.011). We then categorized the phlegm dampness and yang-deficiency individuals into the cold group and those with damp-heat and yin-deficiency into the heat group. A comparison of the two constitution groups showed higher AI in the cold group (p=0.026). Binary logistic regression analysis demonstrated that only AI was a determinant, as evidenced by the finding that an increase of one unit in AI corresponded to an increase of 5% in the odds ratio for individuals to have a cold constitution (p=0.026). Conclusions Individuals with qi-deficient and cold constitutions had higher AI and lower SEVI, potentially reflecting an increase in arterial stiffness. This study can provide a basis for further investigation of the physiological indicators of TCM constitutions in modern medicine.