The Mystery of the Z-Score

Transcriptomic observations of intra and extracellular immunotherapy targets for pediatric brain tumors

OBJECTIVES

Despite surgical resection, chemoradiation, and targeted therapy, brain tumors remain a leading cause of cancer-related death in children. Immunotherapy has shown some promise and is actively being investigated for treating childhood brain tumors. However, a critical step in advancing immunotherapy for these patients is to uncover targets that can be effectively translated into therapeutic interventions.

METHODS

In this study, our team performed a transcriptomic analysis across pediatric brain tumor types to identify potential targets for immunotherapy. Additionally, we assessed components that may impact patient response to immunotherapy, including the expression of genes essential for antigen processing and presentation, inhibitory ligands and receptors, interferon signature, and overall predicted T cell infiltration.

RESULTS

We observed distinct expression patterns across tumor types. These included elevated expression of antigen genes and antigen processing machinery in some tumor types while other tumors had elevated inhibitory checkpoint receptors, known to be associated with response to checkpoint inhibitor immunotherapy.

CONCLUSION

These findings suggest that pediatric brain tumors exhibit distinct potential for specific immunotherapies. We believe our findings can guide investigators in their assessment of appropriate immunotherapy classes and targets in pediatric brain tumors.

Early heart disease prediction using feature engineering and machine learning algorithms

Heart disease is one of the most widespread global health issues, it is the reason behind around 32 % of deaths worldwide every year. The early prediction and diagnosis of heart diseases are critical for effective treatment and sickness management. Despite the efforts of healthcare professionals, cardiovascular surgeons and cardiologists' misdiagnosis and misinterpretation of test results may happen every day. This study addresses the growing global health challenge raised by Cardiovascular Diseases (CVDs), which account for 32 % of all deaths worldwide, according to the World Health Organization (WHO). With the progress of Machine Learning (ML) and Deep Learning (DL) techniques as part of Artificial Intelligence (AI), these technologies have become crucial for predicting and diagnosing CVDs. This research aims to develop an ML system for the early prediction of cardiovascular diseases by choosing one of the powerful existing ML algorithms after a deep comparative analysis of several. To achieve this work, the Cleveland and Statlog heart datasets from international platforms are used in this study to evaluate and validate the system's performance. The Cleveland dataset is categorized and used to train various ML algorithms, including decision tree, random forest, support vector machine, logistic regression, adaptive boosting, and K-nearest neighbors. The performance of each algorithm is assessed based on accuracy, precision, recall, F1 score, and the Area Under the Curve metrics. Hyperparameter tuning approaches have been employed to find the best hyperparameters that reflect the optimal performance of the used algorithms based on different evaluation approaches including 10-fold cross-validation with a 95 % confidence interval. The study's findings highlight the potential of ML in improving the early prediction and diagnosis of cardiovascular diseases. By comparing and analyzing the performance of the applied algorithms on both the Cleveland and Statlog heart datasets, this research contributes to the advancement of ML techniques in the medical field. The developed ML system offers a valuable tool for healthcare professionals in the early prediction and diagnosis of cardiovascular diseases, with implications for the prediction and diagnosis of other diseases as well.

Spatial accumulation of lignin monomers and cellulose underlying stalk strength in maize

Lodging largely affects yield, quality and mechanical harvesting of maize. Stalk strength is one of the major factors that affect maize lodging. Although plant cell wall components including lignin and cellulose were known to be associated with stalk strength and lodging resistance, spatial accumulation of specific lignin monomers and cellulose in different tissues and their association with stalk strength in maize was not clearly understood. In this study, we found that both G and S lignin monomers accumulate highest in root, stem rind and leaf vein. Consistently, most lignin biosynthetic genes were expressed higher in root and stem than in other tissues. However, cellulose appears to be lowest in root. There are only mild changes of G lignin and cellulose in different internodes. Instead, we noticed a dramatic decrease of S-lignin accumulation and lignin biosynthetic gene expression in 2nd to 4th internodes wherein stem breakage usually occurs, thereby revealing a few candidate lignin biosynthetic genes associated with stalk strength. Moreover, stalk strength is positively correlated with G, S lignin, and cellulose, but negatively correlated with S/G ratio based on data of maize lines with high or low stalk strength. Loss-of-function of a caffeic acid o-methyltransferase (COMT), which is involved in S lignin biosynthesis, in the maize bm3 mutant, leads to lower stalk strength. Our data collectively suggest that stalk strength is determined by tissue-specific accumulation of lignin monomers and cellulose, and manipulation of the cell wall components by genetic engineering is vital to improve maize stalk strength and lodging resistance.

Effect of stress-based interventions on the quality of life of people with an intellectual disability and their caregivers

PURPOSE

People with intellectual disabilities often show challenging behaviour, which can manifest itself in self-harm or aggression towards others. Real-time monitoring of stress in clients with challenging behaviour can help caregivers to promptly deploy interventions to prevent escalations, ultimately to improve the quality of life of client and caregiver. This study aimed to assess the impact of real-time stress monitoring with HUME, and the subsequent interventions deployed by the care team, on stress levels and quality of life.

MATERIALS AND METHODS

Real-time stress monitoring was used in 41 clients with intellectual disabilities in a long-term care setting over a period of six months. Stress levels were determined at the start and during the deployment of the stress monitoring system. The quality of life of the client and caregiver was measured with the Outcome Rating Scale at the start and at three months of use.

RESULTS

The results showed that the HUME-based interventions resulted in a stress reduction. The perceived quality of life was higher after three months for both the clients and caregivers. Furthermore, interventions to provide proximity were found to be most effective in reducing stress and increasing the client's quality of life.

CONCLUSIONS

The study demonstrates that real-time stress monitoring with the HUME and the following interventions were effective. There was less stress in clients with an intellectual disability and an increase in the perceived quality of life. Future larger and randomized controlled studies are needed to confirm these findings.

Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks

BACKGROUND

Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges.

METHODS

We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed.

RESULTS

Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate.

CONCLUSIONS

In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

Bandgap prediction of non-metallic crystals through machine learning approach

The determination of bandgap is the heart of electronic structure of any material and is a crucial factor for thermoelectric performance of it. Due to large amount to data (features) that are related to bandgap are now a days available, it is possible to make use of machine learning (ML) approach to predict the bandgap of the material. The study commences by selecting the feature through Pearson correlation study between bandgap and various thermoelectric parameters in non-metallic crystals. Among the 42 parameters available in the dataset, the Seebeck coefficient and its corresponding temperatures show high correlation with the bandgap. With these three selected features we have used different ML models like multilinear regression, polynomial regression, random forest regression and support vector regression to predict the bandgap. Amongst the different ML models considered, random forest regression outperforms the other models to predict the bandgap withR2value of 97.55% between actual bandgap and predicted bandgap.

Guidance for use of neurofilament light chain as a cerebrospinal fluid and blood biomarker in multiple sclerosis management

Neurofilament light chain (NfL) is a long-awaited blood biomarker that can provide clinically useful information about prognosis and therapeutic efficacy in multiple sclerosis (MS). There is now substantial evidence for this biomarker to be used alongside magnetic resonance imaging (MRI) and clinical measures of disease progression as a decision-making tool for the management of patients with MS. Serum NfL (sNfL) has certain advantages over traditional measures of MS disease progression such as MRI because it is relatively noninvasive, inexpensive, and can be repeated frequently to monitor activity and treatment efficacy. sNfL levels can be monitored regularly in patients with MS to determine change from baseline and predict subclinical disease activity, relapse risk, and the development of gadolinium-enhancing (Gd+) lesions. sNfL does not replace MRI, which provides information related to spatial localisation and lesion stage. Laboratory platforms are starting to be made available for clinical application of sNfL in several countries. Further work is needed to resolve issues around comparisons across testing platforms (absolute values) and normalisation (reference ranges) in order to guide interpretation of the results.

Harmonization of CSF and imaging biomarkers in Alzheimer's disease: Need and practical applications for genetics studies and preclinical classification

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers, leading to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS results using this approach with currently accepted methods. We also used a generalized mixture model to calculate the threshold for biomarker-positivity. Based on our findings, our normalization approach performed as well as meta-analysis and did not lead to any spurious results. In terms of dichotomization, cutoffs calculated with this approach were very similar to those reported previously. These findings show that the Z-score based harmonization approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

Establishment and application of high throughput screening cell model for nutrient regulation of embryonic development

Embryo development exerts far-reaching influence on pregnancy outcome, postnatal development and lifelong health. Thereafter, to select functional nutrients to improve embryo development is of great importance. Herein, a stable porcine trophectoderm cell line expressing a luciferase reporter gene driven by a 1,009 bp PCNA gene promoter was constructed through lentiviral transduction and G418 selection. A high throughput screening assay was subsequently developed using the stable reporter cell line to screen a library of 225 nutrients. Seven nutrients with a minimum Z-score of 2.0 were initially identified to be capable of enhancing embryonic development. Among these nutrients, resveratrol, apigenin, and retinol palmitate were furtherly confirmed the beneficial effects for embryo development. Resveratrol significantly increased the expression of key genes involved in pTr cell proliferation and the number of S-phase cells. Resveratrol was furtherly confirmed to promote the expression of key genes in trophoblast development and increase embryo adhesion rate in vitro. Similarly, dietary 0.05% resveratrol supplementation significantly increased the number of embryo attachment and serum level of P4 and E2 in rats. Resveratrol could also improve maternal antioxidant levels and reduce intracellular ROS. Collectively, a high throughput screening cell model for nutrient regulation of embryonic development was established, which can be used to highly effectively select the potential candidates for embryo development. These findings have great implications for exploring optimal functional nutrients to improve embryo development, ultimately beneficial for pregnancy outcome, offspring postnatal development and lifelong health for human beings and mammalian animals.

Impacts of visual impairment on pragmatic impairment: A systematic review and meta-analysis

BACKGROUND

Consideration for patients with visual impairment, from low vision to blindness, is an important part of building a barrier-free society. Some authors have elaborated that visual impairment can indeed lead to delayed development in theory of mind, thereby causing pragmatic knowledge deficiency. Verifying whether those with eye conditions have pragmatic impairment is an essential way for their clinical evaluation, intervention and rehabilitation.

OBJECTIVE

We primarily carry out a meta-analysis of visual impairment from low vision to blindness and pragmatic impairment in people with low vision or blindness to verify visual impairment may cause pragmatic impairment.

DATA SOURCES

Electronic databases Pubmed, Medline, MesH, Psychinfo, Ovid, EBSCO and CNKI and the reference sections of previous reviews.

STUDY ELIGIBILITY CRITERIA

Studies were included when they built on primary data from clinical questionnaire surveys or field trials anywhere in the world, and when they reported impacts of visual impairment on social cognition, communication, skills, behavior and intelligence. In total, 25 original studies were included, in which 25735 people were evaluated.

RESULTS

Statistically, visual impairments and pragmatic impairment exist correlation due to the significant p value(p = 0.0005 < 0.05) in group and the subgroup sorted in the light of 18 years old (p < 0.0001 and p = 0.003 < 0.05). Psychologically, because people with visual impairment can not normally get non-verbal information, they can not get a complete pragmatic knowledge system. Pragmatic knowledge deficiency leads to abnormal in executive functions and development delay from the perspective of theory of mind, inducing pragmatic impairment. Therefore, visual impairment has an impact on pragmatic impairment.

CONCLUSION

The meta-analysis reveals robust evidence on the relationship of vision impairment and pragmatic impairment in children or adults. Such evidence may help to gradually improve the clinical evaluation, intervention and rehabilitation of these people.

Deep learning-based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra

Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilised DLAP to analyse subtypes of tyrosine hydroxylase (TH)-positive dopaminergic midbrain neurons in mouse and human brain-sections. These neurons modulate complex behaviour, and are differentially affected in Parkinson's and other diseases. DLAP allows the analysis of large cell numbers, and facilitates the identification of small cellular subpopulations. Using DLAP, we identified a small subpopulation of TH-positive neurons (~5%), mainly located in the very lateral Substantia nigra (SN), that was immunofluorescence-negative for the plasmalemmal dopamine transporter (DAT), with ~40% smaller cell bodies. These neurons were negative for aldehyde dehydrogenase 1A1, with a lower co-expression rate for dopamine-D2-autoreceptors, but a ~7-fold higher likelihood of calbindin-d28k co-expression (~70%). These results have important implications, as DAT is crucial for dopamine signalling, and is commonly used as a marker for dopaminergic SN neurons.

Long-term patterns in inland fish kills associated with cold-shock and winter stress: a regional case study from Texas

Colder water temperatures are generally regarded as a stressful period for fishes (i.e., winter stress syndrome), which can be exacerbated by cold-shock stress associated with major arctic freezes. Although cold-shock stress and mass mortalities are well documented for coastal marine fishes, few studies report the effects of winter stress or cold-shock stress on inland fishes. The purposes of this study were to describe patterns in inland fish mortalities associated with winter stress syndrome and with cold-shock stress in Texas as a regional example of inland fish mortalities associated with colder water temperatures. Using fish mortality reports (1969-2021) recorded by state agency biologists, colder water temperature mortalities occurred in 66% (N = 35) of the years, with greatest percentages of the reports occurring during three major arctic freezes in 1981, 1983 and 2021. The majority of reports were from urbanized counties (79%) and from lentic habitats (56%). Seventeen taxa and 1,021,217 individuals were estimated to be killed during the 53s years. Numbers of inland fish mortalities were greater during major arctic freeze years than non-major arctic freeze years, attributed primarily to mortalities of non-native fishes (e.g., blue tilapia Oreochromis aureus, suckermouth catfish Hypostomus plecostomus). Numbers of native fish mortalities, primarily clupeids and catostomids, were not different between major arctic freeze years and non-major arctic freezes. The 43,000 inland fish mortalities reported during major arctic freeze years are in stark contrast to the 35 million coastal marine fish mortalities. Proposed mechanisms to explain cold-shock mortalities in coastal waters (e.g., species within the northern extent of their range, lack of access to deeper water) are similar in inland waters, yet inland waters do not have the same level of mortalities. Consequently, the disparities between mortalities in coastal and inland waters are not readily discernable at this time.

Identification of signaling pathways modifying human dopaminergic neuron development using a pluripotent stem cell-based high-throughput screening automated system: purinergic pathways as a proof-of-principle

Introduction: Alteration in the development, maturation, and projection of dopaminergic neurons has been proposed to be associated with several neurological and psychiatric disorders. Therefore, understanding the signals modulating the genesis of human dopaminergic neurons is crucial to elucidate disease etiology and develop effective countermeasures. Methods: In this study, we developed a screening model using human pluripotent stem cells to identify the modulators of dopaminergic neuron genesis. We set up a differentiation protocol to obtained floorplate midbrain progenitors competent to produce dopaminergic neurons and seeded them in a 384-well screening plate in a fully automated manner. Results and Discussion: These progenitors were treated with a collection of small molecules to identify the compounds increasing dopaminergic neuron production. As a proof-of-principle, we screened a library of compounds targeting purine- and adenosine-dependent pathways and identified an adenosine receptor 3 agonist as a candidate molecule to increase dopaminergic neuron production under physiological conditions and in cells invalidated for the HPRT1 gene. This screening model can provide important insights into the etiology of various diseases affecting the dopaminergic circuit development and plasticity and be used to identify therapeutic molecules for these diseases.

Normative Echocardiographic Left Ventricular Parameters and Reference Intervals in Infants

BACKGROUND

In pediatric echocardiography, reference intervals are required to distinguish normal variation from pathology. Left ventricular (LV) parameters are particularly important predictors of clinical outcome. However, data from healthy newborns are limited, and current reference intervals provide an inadequate approximation of normal reference ranges.

OBJECTIVES

Normative reference intervals and z-scores for 2-dimensional echocardiographic measurements of LV structure and function based on a large group of healthy newborns were developed.

METHODS

The study population included 13,454 healthy newborns from the Copenhagen Baby Heart Study who were born at term to healthy mothers, had an echocardiogram performed within 30 days of birth, and did not have congenital heart disease. To develop normative reference intervals, this study modeled 10 LV parameters as a function of body surface area through joint modeling of 4 statistical components.

RESULTS

Infants in the study population (48.5% were female) had a median body surface area of 0.23 m² (IQR: 0.22-0.25 m²) and median age of 12.0 days (IQR: 8.0-15.0 days) at examination. All normative reference intervals performed well in both sexes without stratification on infant sex. In contrast, creation of separate reference models for infants examined at <7 days of age and those examined at 7-30 days of age was necessary to optimize the performance of the reference intervals.

CONCLUSIONS

This study provides normative reference intervals and z-scores for 10 clinical, widely used echocardiographic measures of LV structure and function based on a large cohort of newborns. These results provide highly needed reference material for clinical application by pediatric cardiologists.

Harmonization of CSF and imaging biomarkers for Alzheimer's disease biomarkers: need and practical applications for genetics studies and preclinical classification

INTRODUCTION

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers which leads to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values.

METHODS

We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS result using this method with currently accepted methods. We also used a generalized mixture modelling to calculate the threshold for biomarker-positivity.

RESULTS

Z-scores method performed as well as meta-analysis and did not lead to any spurious results. Cutoffs calculated with this approach were found to be very similar to those reported previously.

DISCUSSION

This approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response

BACKGROUND

Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM).

METHODS

To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immunotherapy.

RESULTS

It is found that the m6A regulatory factor significantly regulates the mutation of GBM and TME. Based on Europe, America, and China data, we established m6Ascore through the m6A model. The model accurately predicted the results of 1206 GBM patients from the discovery cohort. Additionally, a high m6A score was associated with poor prognoses. Significant TME features were found among the different m6A score groups, which demonstrated positive correlations with biological functions (i.e., EMT2) and immune checkpoints.

CONCLUSIONS

m6A modification was important to characterize the tumorigenesis and TME infiltration in GBM. The m6Ascore provided GBM patients with valuable and accurate prognosis and prediction of clinical response to various treatment modalities, which could be useful to guide patient treatments.

INSnet: a method for detecting insertions based on deep learning network

BACKGROUND

Many studies have shown that structural variations (SVs) strongly impact human disease. As a common type of SV, insertions are usually associated with genetic diseases. Therefore, accurately detecting insertions is of great significance. Although many methods for detecting insertions have been proposed, these methods often generate some errors and miss some variants. Hence, accurately detecting insertions remains a challenging task.

RESULTS

In this paper, we propose a method named INSnet to detect insertions using a deep learning network. First, INSnet divides the reference genome into continuous sub-regions and takes five features for each locus through alignments between long reads and the reference genome. Next, INSnet uses a depthwise separable convolutional network. The convolution operation extracts informative features through spatial information and channel information. INSnet uses two attention mechanisms, the convolutional block attention module (CBAM) and efficient channel attention (ECA) to extract key alignment features in each sub-region. In order to capture the relationship between adjacent subregions, INSnet uses a gated recurrent unit (GRU) network to further extract more important SV signatures. After predicting whether a sub-region contains an insertion through the previous steps, INSnet determines the precise site and length of the insertion. The source code is available from GitHub at https://github.com/eioyuou/INSnet .

CONCLUSION

Experimental results show that INSnet can achieve better performance than other methods in terms of F1 score on real datasets.

Influence of ergonomic factors on peripheral neuropathy under HAV exposure

BACKGROUND

Hand-arm vibration (HAV) is a risk factor for carpal tunnel syndrome (CTS) and ulnar neuropathy at the elbow (UNE). It is unclear how ergonomic factors influence the relationship between HAV exposure and CTS and UNE.

AIMS

We aimed to assess the relationship between cumulative HAV exposure and CTS and UNE in workers exposed to HAV from two tools with different ergonomic profiles.

METHODS

We performed nerve conduction studies (NCSs) of the sensory and motor median and ulnar nerves and recorded symptoms indicating CTS and UNE in workers exposed to HAV from impact wrenches or from rock drills. Exposure was measured as cumulative lifetime exposure. We used linear regression adjusted for age and body mass index to assess linear relationships.

RESULTS

Sixty-five workers participated (33 rock drill and 32 impact wrench operators). We found inverse linear associations between cumulative HAV exposure and median nerve sensory conduction velocity in impact wrench operators and ulnar nerve motor conduction velocity in rock drill operators (beta of 0.63 and 0.75). Based on NCS findings and symptoms, seven impact wrench operators had CTS and one UNE, and four rock drill operators had CTS and six UNE.

CONCLUSIONS

Our findings indicate that ergonomic factors influence the development of CTS and UNE under HAV exposure. The ergonomic profile seems to influence which type of neuropathy workers exposed to HAV will develop. Design of occupational exposure guidelines and future studies should be based on ergonomic profile and exposure characteristics for different tools and not merely HAV.

Using two-step cluster analysis to classify inpatients with primary biliary cholangitis based on autoantibodies: A real-world retrospective study of 537 patients in China

Background

A variety of autoantibodies have been detected in primary biliary cholangitis (PBC), while the presence of autoantibody clusters and their clinical significance have not been fully understood. We aimed at defining autoantibody clusters and to better understand the clinical features and prognosis of PBC patients based on autoantibody clusters under real-world conditions.

Methods

We retrospectively analyzed 788 inpatients with PBC evaluated between October 2008 and July 2019, and included 537 patients. Nineteen autoantibodies which were measured routinely were investigated for cluster analysis. Two-step clustering, Kaplan-Meier survival, and Cox regression analyses were used.

Results

Five clusters were defined. A cluster of antinuclear antibodies (ANA) and anti-gp210 positive patients were identified with a high rate of cirrhosis at baseline and low survival rate; a cluster of ANA, anti-centromere antibodies (ACA) and/or anti-CENP-B female dominant patients with older disease onset, low level of platelet count at baseline, high rate of hepatic decompensation, and low survival rate was also characterized; and another cluster of anti-mitochondrial antibodies (AMA) and/or AMA-M2, anti-Ro52 and a high rate of anti-gp210 positive patients were identified with a high proportion of male patients and low survival rate. A subgroup of patients with anti-SSA and/or anti-SSB coexists with SjS was also identified; patients with only AMA and/or AMA-M2-positive with a benign clinical outcome and relatively high complication of non-alcoholic fatty liver disease (NAFLD) were also identified. Only anti-gp210 was considered as a significant predictor for poor outcomes especially in patients with cirrhosis.

Conclusion

Clustering methods allow the identification of distinct autoantibody profiles of PBC that form clinical subsets and can be useful for personalized approaches to diagnosis, clinical management, and the prediction of clinical outcomes. Anti-gp210 was the strongest predictive factor for poor outcomes especially in PBC patients with cirrhosis under real-world conditions.

HTAD patient pathway: Strategy for diagnostic work-up of patients and families with (suspected) heritable thoracic aortic diseases (HTAD). A statement from the HTAD working group of VASCERN

Heritable thoracic aortic diseases (HTAD) are rare pathologies associated with thoracic aortic aneurysms and dissection, which can be syndromic or non-syndromic. They may result from genetic defects. Associated genes identified to date are classified into those encoding components of the (a) extracellular matrix (b) TGFβ pathway and (c) smooth muscle contractile mechanism. Timely diagnosis allows for prompt aortic surveillance and prophylactic surgery, hence improving life expectancy and reducing maternal complications as well as providing reassurance to family members when a diagnosis is ruled out. This document is an expert opinion reflecting strategies put forward by medical experts and patient representatives involved in the HTAD Rare Disease Working Group of VASCERN. It aims to provide a patient pathway that improves patient care by diminishing time to diagnosis, facilitating the establishment of a correct diagnosis using molecular genetics when possible, excluding the diagnosis in unaffected persons through appropriate family screening and avoiding overuse of resources. It is being recommended that patients are referred to an expert centre for further evaluation if they meet at least one of the following criteria: (1) thoracic aortic dissection (<70 years if hypertensive; all ages if non-hypertensive), (2) thoracic aortic aneurysm (all adults with Z score >3.5 or 2.5-3.5 if non-hypertensive or hypertensive and <60 years; all children with Z score >3), (3) family history of HTAD with/without a pathogenic variant in a gene linked to HTAD, (4) ectopia lentis without other obvious explanation and (5) a systemic score of >5 in adults and >3 in children. Aortic imaging primarily relies on transthoracic echocardiography with magnetic resonance imaging or computed tomography as needed. Genetic testing should be considered in those with a high suspicion of underlying genetic aortopathy. Though panels vary among centers, for patients with thoracic aortic aneurysm or dissection or systemic features these should include genes with a definitive or strong association to HTAD. Genetic cascade screening and serial aortic imaging should be considered for family screening and follow-up. In conclusion, the implementation of these strategies should help standardise the diagnostic work-up and follow-up of patients with suspected HTAD and the screening of their relatives.

Which Performance Tests Best Define the Special Judo Fitness Test Classification in Elite Judo Athletes?

The normative values of the Special Judo Fitness Test (SJFT) are used to evaluate judo athletes, and the question arises of which performance tests from crucial motor abilities best define the SJFT classification in elite judo athletes. This study aimed to investigate the relationship between elite judo athletes’ physical performance and the evaluation using SJFT index norms. Nineteen judo athletes (11 females) (22.8 ± 2.5 years old) from the senior judo national team voluntarily participated in this study. Body composition, reaction time, balance, flexibility, agility, hand grip strength, 20 m sprint, vertical jump, SJFT, and Wingate tests were performed by athletes on four separate days at one-day intervals. Athletes were classified as regular and above (≥regular) or poor and below (≤poor) according to their SJFT index scores. Simple logistic regression analysis was used to evaluate this classification’s consistency with performance test results. Odds ratios (OR) and 95% confidence intervals related to each possible factor and Wald test statistics were calculated. The SJFT index classification was associated with Wingate peak and mean power, vertical jump, and sprint performance results (p < 0.05), whereas it was not associated with body fat percentage, agility, reaction time, hand grip strength, flexibility, and balance performances (p > 0.05). SJFT index classificatory norms are mainly related to athletes’ anaerobic power. Higher anaerobic power increases athletes’ possibility of being classified as ≥regular.

Role of the Gut Microbiota in the Increased Infant Body Mass Index Induced by Gestational Diabetes Mellitus

The connection between gestational diabetes mellitus (GDM) and the offspring's development, such as obesity, is well established. Emerging evidence indicates that the microbiota of the neonate's meconium is associated with maternal GDM status. To explore whether the association between GDM and infant body mass index (BMI) in early childhood is affected by the meconium microbiota, we recruited 120 mothers (60 healthy women and 60 with GDM) and their newborns from the Women's Hospital of Nanjing Medical University. Meconium of 120 neonates was collected within a few hours after birth and sequenced using 16S rRNA sequencing analysis. Children's BMI was measured at 12 months of age. The results revealed that infants born to mothers with GDM had increased BMI Z-scores at 12 months old and that the β-diversity of their meconium microbiota was reduced. Several genera were observed to be significantly different between the GDM and control groups. The genus Burkholderia-Caballeronia-Paraburkholderia and an untitled genus in the family Enterobacteriaceae enriched in neonates born to healthy mothers were found to be negatively associated with infant BMI by using regression analysis. A coabundance group depleted in the GDM group was correlated negatively with 12-month BMI and mediated 21.65% of the association between GDM and infant BMI by mediation analyses. This study provided evidence for the associations among maternal GDM, the meconium microbiota, and infant BMI. Maternal GDM was demonstrated to affect infant BMI, mediated by the gut microbiome. Gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity. IMPORTANCE Using 16S rRNA sequencing analysis, regression analysis and mediation analysis were used to explore whether maternal gestational diabetes mellitus (GDM) changed the function and composition of the meconium microbiota and whether this explained the GDM-induced alterations of infant body mass index (BMI). This study showed that gut microbiome dysbiosis induced by maternal GDM might play an important role in the increased infant BMI during the first 12 months of life. Therefore, gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity.

Spatial heterogeneity of first flowering date in Beijing's main urban area and its response to urban thermal environment

Phenology - the rhythm of periodic plant life cycle events - was significantly shaped by urban climate, with flowering as one most sensitive phenophase. Apart from the widely noticed urban-rural phenological discrepancy caused by heat island effect, driven by the aggravating spatial unevenness of urban thermal environment, the spatial heterogeneity of flowering time was also found within the urbanized area of some metropolitans, bringing multiple impacts on urban ecology, landscape and public health. This research aimed to reveal the intraurban spatial variation and response characteristics of Beijing's trees flowering phenology that remained largely unclear before. We analyzed the spatial heterogeneity pattern of the first flowering date (FFD) for 42 deciduous woody species in Beijing's main urban area (MUA), and explored the species-specific phenological response to local thermal environment. The sample plots were set in 9 green spaces distributing from urban center to northwest suburb in Beijing's MUA, the FFD data was collected by ground-based phenological observation, and local thermal environment was measured with land surface temperature (LST) retrieved from MOD11A1 products. The main results are as follows: (1) A significant spatial variation for FFD existed among 9 sample plots and the maximum spatial difference of FFD reached 6.76 ± 1.77 days in average, FFD showed an overall delay trend from urban center in 2^nd Ring to outskirts beyond 5^th Ring with 3^rd Ring as a critical line for significant phenological difference. (2) The FFD of 35 species was found to be negatively correlated with [Formula: see text] (average of daily mean LST above 0 °C before mean FFD) in the sample plot (p < 0.05) with a response sensitivity of 2.99 ± 0.87 days/°C, which reflected the significant impact of LST variation during flower development period. Furthermore, the spatial difference and response sensitivity of FFD for a specific species were found to be negatively associated with its mean FFD value (p < 0.05), i.e., the flowering time of early-blooming species tended to be more sensitive to thermal environment variation compared with late-blooming ones. This research illustrated how flowering phenology responded to the heterogeneous intraurban thermal environment in Beijing's MUA, which can improve our understanding of the vegetation dynamics in a constantly changing urban environment. And as a critical indicator of trees' climate vulnerability assessment, the species-specific phenological response sensitivity could also guide species selection in urban forest construction.

Comprehensive diagnosis model of environmental impact caused by expressway vehicle emission

With the rapid development of the economy, the expressway has been used as a main mode of transportation due to its function to meet traffic demand of people and thus has been given full attention. But, at the same time, it has gradually become the main cause of pollution of traffic environment. To clarify the degree of pollution caused by expressway vehicle and improve the expressway pollution diagnosis system, upon the notion of low-carbon transportation, this paper divides expressway environmental pollution into four types: air pollution, photochemical smog pollution, noise pollution, and vibration pollution, and analyzes each of them, respectively. Then, a comprehensive diagnosis model of environmental pollution caused by running vehicles will be built. This paper monitors the pollution intensity on different spots on the expressway to obtain the single-vehicle factors of various pollutants of the motor vehicles. Combined with the geographic information system, this puts forward the diagnosis methods in terms of the environmental "air pollution," "photochemical smog pollution," "noise pollution" and "vibration pollution" caused by the expressway vehicles, respectively, and further establishes a diagnosis model of vehicle pollution corresponding to the characteristics of the expressway. The result of the case study on the actual monitoring data of six expressways in Jiangsu Province shows that the pollution diagnosis values of six expressways are all between (0.4, 0.6] which symbolizes "slight pollution." The research results can provide technical support for monitoring of environmental pollution caused by expressway more comprehensively and reasonably, and provide data support for formulating effective control strategies.

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.

Normative Values of Pediatric Thoracic Aortic Diameters Indexed to Body Surface Area Using Computed Tomography

PURPOSE

The purpose of this study was to establish normative values for the thoracic aorta diameter in pediatric patients from birth to 18 years of age using computed tomography (CT) measurements and to create nomograms related to body surface area (BSA).

METHODS

A total of 623 pediatric patients without cardiovascular disease (42.1% females; from 3 d to 18 y old) with high-quality, non-electrocardiogram-gated, contrast-enhanced CT imaging of the chest were retrospectively evaluated. Systematic measurements of the aortic diameter at predetermined levels were recorded, and demographic data including age, sex, ethnicity, and BSA were collected. Reference graphs plotting BSA over aortic diameter included the mean and Z -3 to Z +3, where Z represents SDs from the mean.

RESULTS

The study population was divided into 2 groups (below 2 and greater than or equal to 2 y old). There were no significant differences in average aortic measurements between males and females. Both age groups exhibited significant positive correlations among all size-related metrics (all P <0.001) with BSA having the highest correlation. For both groups, the average orthogonal thoracic aortic diameters at each level of the thoracic aorta were used to create nomograms.

CONCLUSION

This study establishes clinically applicable, BSA-specific reference values of the normal thoracic aorta for the pediatric population from CT imaging.

Capture Order Across Social Bathyergids Indicates Similarities in Division of Labour and Spatial Organisation

The social mole-rats of the family Bathyergidae show elaborate social organisation that may include division of labour between breeders and non-breeders as well as across non-breeders within their groups. However, comparative behavioural data across the taxa are rare and contrasts and similarities between species are poorly understood. Field studies of social bathyergids usually involve capturing all group members until the entire group is captured. Because each animal is only captured once and traps are typically placed in close proximity to active foraging areas, the order in which animals are captured provides an indication of the foraging activity of different individuals and of the spatial organisation of the group within the burrow system. Here, we compare the association of capture order with breeding status, sex, and body mass in four species and subspecies of social bathyergids, which vary in group size and represent all three social genera within the family Bathyergidae. We show that in naked and Damaraland mole-rats (Heterocephalus glaber and Fukomys damarensis), male and female breeders are captured later than non-breeders, whereas in two different subspecies of the genus Cryptomys only female breeders are captured later than non-breeders. The effect sizes vary largely and are 10 times larger in naked mole-rats as compared to Fukomys and 3–4 times larger than in Cryptomys. Among non-breeders, sex effects are notably absent in all species and body mass predicted capture order in both naked and Damaraland mole-rats. In naked mole-rats, larger non-breeders were captured earlier than smaller ones, whereas in Damaraland mole-rats intermediate-sized non-breeders were captured first. Our data suggest that there are similarities in behavioural structure and spatial organisation across all social bathyergid species, though the most pronounced differences within groups are found in naked mole-rats.

The Psychological Consequences of the COVID-19 Pandemic in Adults Treated for Childhood Cancer

Background: Compared with the general population, childhood cancer survivors (CCS) could be at greater risk of psychological distress following the emergence of the COVID-19 pandemic. Purpose: This cross-sectional study assessed the psychological consequences of COVID-19 on the mental health of CCS. Design and participants: In December 2020, we interviewed through an online self-report questionnaire, 580 5-year CCS participating in the French Childhood Cancer Survivor Study (FCCSS) cohort. Methods: We first compared the mental health score of CCS with that observed in the French general population of the same age and gender. Subsequently, we studied predictors of the mental health score of CCS. Results: External comparisons revealed that the mental health score of CCS was similar to that of the general population. Among CCS, almost 42% stated that their psychological state had been worse during the lockdown. Predictors of poorer mental health included, among others, female gender, reporting a change in the occupational situation, having a relative who had been hospitalized or had died following COVID-19, and a greater perceived infection risk. Interpretation and Implications: Given the pre-existing vulnerability of some CCS to mental distress, the additional psychological consequences of COVID-19 in vulnerable survivors should receive attention from health care providers.

Valvuloplasty in 103 fetuses with critical aortic stenosis: outcome and new predictors for postnatal circulation

OBJECTIVES

To review our experience with fetal aortic valvuloplasty (FAV) in fetuses with critical aortic stenosis (CAS) and evolving hypoplastic left heart syndrome (eHLHS), including short- and medium-term postnatal outcome, and to refine selection criteria for FAV by identifying preprocedural predictors of biventricular (BV) outcome.

METHODS

This was a retrospective review of all fetuses with CAS and eHLHS undergoing FAV at our center between December 2001 and September 2020. Echocardiograms and patient charts were analyzed for pre-FAV ventricular and valvular dimensions and hemodynamics and for postnatal procedures and outcomes. The primary endpoints were type of circulation 28 days after birth and at 1 year of age. Classification and regression-tree analysis was performed to investigate the predictive capacity of pre-FAV parameters for BV circulation at 1 year of age.

RESULTS

During the study period, 103 fetuses underwent 125 FAVs at our center, of which 87.4% had a technically successful procedure. Technical success per fetus was higher in the more recent period (from 2014) than in the earlier period (96.2% (51/53) vs 78.0% (39/50); P = 0.0068). Eighty fetuses were liveborn after successful intervention and received further treatment. BV outcome at 1 year of age was achieved in 55% of liveborn patients in our cohort after successful FAV, which is significantly higher than the BV-outcome rate (23.7%) in a previously published natural history cohort fulfilling the same criteria for eHLHS (P = 0.0015). Decision-tree analysis based on the ratio of right to left ventricular (RV/LV) length combined with LV pressure (mitral valve regurgitation maximum velocity (MR-Vmax)) had a sensitivity of 96.97% and a specificity of 94.44% for predicting BV outcome without signs of pulmonary arterial hypertension at 1 year of age. The highest probability for a BV outcome was reached for fetuses with a pre-FAV RV/LV length ratio of < 1.094 (96.4%) and for those fetuses with a RV/LV length ratio ≥ 1.094 to < 1.135 combined with a MR-Vmax of ≥ 3.14 m/s (100%).

CONCLUSIONS

FAV could be performed with high success rates and an acceptable risk with improving results after a learning curve. Pre-FAV RV/LV length ratio combined with LV pressure estimates were able to predict a successful BV outcome at 1 year of age with high sensitivity and specificity. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Simulation of the pollution abatement behavior of regional metal-related enterprises based on the interactive perspective of industrial agglomerations and emission reduction effects

A machine learning method was used to process a multiagent information database to study the spatial distribution characteristics of agglomerations of metal-related enterprises and to analyze the spatial and temporal differentiation characteristics of pollution reduction in metal-related enterprises. Based on the spatial distribution of enterprises and a simulation of their pollution reduction behaviors, the layout of 380 enterprises sample is optimized, and the direction of industrial transfer is planned to give full play to the pollution reduction effect of enterprise agglomeration. The results showed that (1) the metal-related enterprises in the Chang-Zhu-Tan urban agglomeration have obvious spatial heterogeneity and are mainly distributed in the district of Changsha, the Qingshuitang Industrial Zone, Liling city and the Qibaoshan Industrial Zone of Liuyang city, while the metal-related enterprises in Shaoshan city, Zhuzhou County and Liling city are scattered. (2) The pollution emission behaviors of enterprises differ in time and space, and the pollution concentrations are highest in industrial parks such as Qingshuitang and Zhubu Port. (3) There is an interactive relationship between the degree of enterprise agglomeration and the pollution reduction effect. The spatial positive coupling degree between the concentration of metal-related enterprises and the degree of metal-related pollution is significant, accounting for 94.96% of the study area. Low pollution-high agglomeration areas, high pollution-low agglomeration areas, high pollution-high agglomeration areas, and low pollution-low agglomeration area account for 1.01%, 4.03%, 2.87%, and 92.09% of the study area, respectively. Finally, based on the new development concept of dual circulation and the theory of a two-oriented society in the new era, the paper puts forward suggestions and policies for the sustainable development of industrial transfer.

DeepCompNet: A Novel Neural Net Model Compression Architecture

The emergence of powerful deep learning architectures has resulted in breakthrough innovations in several fields such as healthcare, precision farming, banking, education, and much more. Despite the advantages, there are limitations in deploying deep learning models in resource-constrained devices due to their huge memory size. This research work reports an innovative hybrid compression pipeline for compressing neural networks exploiting the untapped potential of z-score in weight pruning, followed by quantization using DBSCAN clustering and Huffman encoding. The proposed model has been experimented with state-of-the-art LeNet Deep Neural Network architectures using the standard MNIST and CIFAR datasets. Experimental results prove the compression performance of DeepCompNet by 26x without compromising the accuracy. The synergistic blend of the compression algorithms in the proposed model will ensure effortless deployment of neural networks leveraging DL applications in memory-constrained devices.

Magnetic resonance angiography derived predictors of progressive dilatation and surgery of the aortic root in Marfan syndrome

BACKGROUND

To identify magnetic resonance (MR) angiography derived predictors of progressive dilatation and surgery of the aortic root in Marfan syndrome.

MATERIAL AND METHODS

We retrospectively included 111 patients (32.7±16.5 years, range: 7-75 years) with a total of 446 MR angiographies. Aortic diameter growth rates of the entire thoracic aorta and Z-scores were estimated from annual diameter measurements. Aortic root shape was subdivided into three different types: (T0) normal; (T1) localized dilatation; (T2) generalized aortic root dilatation. Aortic diameter, Z-score, age, and aortic root shape at baseline were tested as predictors of aortic root dilatation using a multivariate logistic regression model.

RESULTS

The highest aortic growth rate was observed at the level of the sinuses of Valsalva. Higher aortic root diameters and Z-scores at baseline predicted an increased growth of the aortic root (p = 0.003 and p<0.001). Young age (<30 years) was a predictor for the increase of Z-scores when compared to patients ≥30 years (p = 0.019). 25/111 patients (22.5%) had a T0 aortic root shape, 59/111 patients (53.2%) had a T1 aortic root shape, and 27/111 patients (24.3%) had a T2 aortic root shape. Aortic root shape did not predict further aortic growth (p>0.05). However, significantly more patients undergoing surgery had a generalized aortic dilatation (19/28, 76.9%) than a localized aortic root dilatation (9/28, 32.1%) (p = 0.001).

CONCLUSION

Larger baseline aortic root diameter and Z-score as well as young age predict solely progressive aortic root dilatation in Marfan patients. MR angiography derived type of aortic root shape does not predict aortic growth, but patients with generalized aortic root dilatation are referred more frequently for aortic surgery.

iTRAQ-based quantitative proteomic analysis of the hepatopancreas in Scylla paramamosain during the molting cycle

The hepatopancreas is the key organ involved in energy storage, immune response, and metabolism during crustacean molting, yet the underlying molecular mechanisms in the hepatopancreas that regulate molting remain unknown. In the present study, we conducted a comprehensive proteomic analysis in the hepatopancreas and quantified 1527 proteins, of which 193 changed significantly in abundance among three molting stages (pre-molt: PrM, post-molt: PoM, and inter-molt: InM) of Scylla paramamosain using iTRAQ-coupled LC-MS/MS. Ten exoskeleton and cuticle reconstruction proteins, such as chitinase, cuticle protein and myosin heavy chain, were found change significantly in abundance between PoM and PrM. Six energy metabolism proteins such as mitochondrial cytochrome c oxidase, cytochrome b-c1 and cAMP-dependent protein kinase with positive loadings showed a higher abundance in InM than PoM. In addition, all differentially abundance proteins (DAPs) were annotated for GO function and KEGG pathway analysis. GO analysis demonstrated function subcategories mainly including thiamine metabolism, complement and coagulation cascades, endocrine, shigellosis, salmonella infection, and other factor-regulated calcium reabsorption. The KEGG pathway enrichment analysis indicated that the DAPs were mainly involved in reconstruction of the exoskeleton and cuticle, energy reserves, metabolism, and immune response during the molting process. The results for the proteins and key pathways involved in the molting process provide fundamental molecular evidence that will improve our understanding of morphological and metabolism variation in the molting cycle and will serve as a potential blueprint for future study on molecular mechanism of molting in crustaceans.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.

Is Aortic Z-score an Appropriate Index of Beneficial Drug Effect in Clinical Trials in Aortic Aneurysm Disease?

Aortic Z-score (Z-score) is utilized in clinical trials to monitor the effect of medications on aortic dilation rate in Marfan (MFS) patients. Z-scores are reported in relation to body surface area and therefore are a function of height and weight. However, an information void exists regarding natural, non-pharmacological changes in Z-scores as children age. We had concerns that Z-score decrease attributed to "therapeutic" effects of investigational drugs for Marfan disease connective tissue diseases might simply reflect normal changes ("filling out" of body contour) as children age. This investigation studies natural changes with age in Z-score in normal and untreated MFS children, teasing out normal effects that might erroneously be attributed to drug benefit. (1) We first compared body mass index (BMI) and Z-scores (Boston Children's Hospital calculator) in 361 children with "normal" single echo exams in four age ranges (0 to 1, 5 to 7, 10 to 12, 15 to 18 years). Regression analysis revealed that aging itself decreases ascending Z-score, but not root Z-score, and that increase in BMI with aging underlies the decreased Z-scores. (2) Next, we examined Z-score findings in both "normal" and Marfan children (all pharmacologically untreated) as determined on sequential echo exams over time. Of 27 children without aortic disease with sequential echos, 19 (70%) showed a natural decrease in root Z-score and 24 (89%) showed a natural decrease in ascending Z- score, over time. Of 25 untreated MFS children with sequential echos, 12 (40%) showed a natural decrease in root Z-score and 10 (33%) showed a natural decrease in ascending Z-score. Thus, Z-score is over time affected by natural factors even in the absence of any aneurysmal pathology or medical intervention. Specifically, Z-score decreases spontaneously as a natural phenomenon as children age and with fill out their BMI. Untreated Marfan patients often showed a spontaneous decrease in Z-score. In clinical drug trials in aneurysm disease, decreasing Z-score has been interpreted as a sign of beneficial drug effect. These data put such conclusions into doubt.

Discovery of natural products capable of inducing porcine host defense peptide gene expression using cell-based high throughput screening

Background

In-feed antibiotics are being phased out in livestock production worldwide. Alternatives to antibiotics are urgently needed to maintain animal health and production performance. Host defense peptides (HDPs) are known for their broad-spectrum antimicrobial and immunomodulatory capabilities. Enhancing the synthesis of endogenous HDPs represents a promising antibiotic alternative strategy to disease control and prevention.

Methods

To identify natural products with an ability to stimulate the synthesis of endogenous HDPs, we performed a high-throughput screening of 1261 natural products using a newly-established stable luciferase reporter cell line known as IPEC-J2/pBD3-luc. The ability of the hit compounds to induce HDP genes in porcine IPEC-J2 intestinal epithelial cells, 3D4/31 macrophages, and jejunal explants were verified using RT-qPCR. Augmentation of the antibacterial activity of porcine 3D4/31 macrophages against a Gram-negative bacterium (enterotoxigenic E. coli) and a Gram-positive bacterium (Staphylococcus aureus) were further confirmed with four selected HDP-inducing compounds.

Results

A total of 48 natural products with a minimum Z-score of 2.0 were identified after high-throughput screening, with 21 compounds giving at least 2-fold increase in luciferase activity in a follow-up dose-response experiment. Xanthohumol and deoxyshikonin were further found to be the most potent in inducing pBD3 mRNA expression, showing a minimum 10-fold increase in IPEC-J2, 3D4/31 cells, and jejunal explants. Other compounds such as isorhapontigenin and calycosin also enhanced pBD3 mRNA expression by at least 10-fold in both IPEC-J2 cells and jejunal explants, but not 3D4/31 cells. In addition to pBD3, other porcine HDP genes such as pBD2, PG1-5, and pEP2C were induced to different magnitudes by xanthohumol, deoxyshikonin, isorhapontigenin, and calycosin, although clear gene- and cell type-specific patterns of regulation were observed. Desirably, these four compounds had a minimum effect on the expression of several representative inflammatory cytokine genes. Furthermore, when used at HDP-inducing concentrations, these compounds showed no obvious direct antibacterial activity, but significantly augmented the antibacterial activity of 3D4/31 macrophages (P < 0.05) against both Gram-negative and Gram-positive bacteria.

Conclusions

Our results indicate that these newly-identified natural HDP-inducing compounds have the potential to be developed as novel alternatives to antibiotics for prophylactic and therapeutic treatment of infectious diseases in livestock production.

The cross-sectional association between mean corpuscular volume level and cognitive function in Chinese over 45 years old: Evidence from the China Health and Retirement Longitudinal Study

Fewer studies have focused on the independent association between mean corpuscular volume (MCV) and cognitive performance. This study was designed to characterize the cross-sectional association between MCV and cognitive performance in a large sample of Chinese residents (age≥45 years) from the China Health and Retirement Longitudinal Study (CHARLS). A total of 4023 male and 4173 female adults with MCV ≥ 80 fl were included for analysis. By multivariable linear regression analysis, for the total subjects, MCV level was significantly negatively associated with global cognitive function and episodic memory. When adjusted by sex, only in male subjects, higher MCV level was associated with reduced scores for global cognitive function, episodic memory and mental status. Via binary logistic regression analysis, the higher MCV level (MCV>100 fl) was associated with poor global cognitive function (OR = 1.601; 95% CI = 1.198–2.139; p = 0.001), episodic memory (OR = 1.679; 95% CI = 1.281–2.201; p<0.001), and mental status (OR = 1.422; 95% CI = 1.032–1.959; p = 0.031) for the whole participants. When testing this association by sex, the significant relationship between higher MCV level with worse episodic memory was observed both in male (OR = 1.690; 95% CI = 1.211–2.358; p = 0.002) and female (OR = 1.729; 95% CI = 1.079–2.770; p = 0.023) subjects; while the association between higher MCV level and poor global cognitive function (OR = 1.885; 95% CI = 1.329, 2.675; p<0.001) and mental status (OR = 1.544; 95% CI = 1.034, 2.306; p = 0.034) only existed in male subjects. Further studies are warranted to clarify the association between MCV level and cognitive performance by considering sex into consideration both cross-sectionally and longitudinally.

Aortic root enlargement: When and how

Aortic valve replacement is the definitive management for severe aortic stenosis. Intraoperatively, an aortic root enlargement (ARE) may be used to facilitate the implementation of a suitably sized prosthetic valve. This is to prevent patient prosthesis mismatch (PPM), a condition that causes a left ventricular outflow obstruction. There are four main techniques that are used to perform ARE, namely, Nicks, Manouguian, Nunez (modified Manouguian), and Kanno-Rastan procedures. They each involve incisions through different anatomical structures and allow a variety of valve sizes to be implanted. Studies prove that ARE effectively reduces the incidence of PPM. In addition, they show that there is no definitive link between ARE and perioperative mortality or other complications. There is a scarcity of literature exploring the comparative outcomes of each surgical technique. Therefore, further research is warranted for these procedures to be compared adequately. This review aims to summarise the available literature surrounding ARE with respect to three main questions. (1) What are the indications for ARE, (2) what surgical techniques exist to facilitate ARE, and (3) are there significant differences in patient outcomes when these surgical techniques are employed?

Patient-Specific CT-Based Fluid-Structure-Interaction Aorta Model to Quantify Mechanical Conditions for the Investigation of Ascending Aortic Dilation in TOF Patients

Background

Some adult patients with Tetralogy of Fallot (TOF) were found to simultaneously develop ascending aortic dilation. Severe aortic dilation would lead to several aortic diseases, including aortic aneurysm and dissection, which seriously affect patients' living quality and even cause patients' death. Current practice guidelines of aortic-dilation-related diseases mainly focus on aortic diameter, which has been found not always a good indicator. Therefore, it may be clinically useful to identify some other factors that can potentially better predict aortic response to dilation.

Methods

20 TOF patients scheduled for TOF repair surgery were recruited in this study and were divided into dilated and nondilated groups according to the Z scores of ascending aorta diameters. Patient-specific aortic CT images, pressure, and flow rates were used in the construction of computational biomechanical models.

Results

Simulation results demonstrated a good coincidence between numerical mean flow rate at inlet and the one obtained from color Doppler ultrasonography, which implied that computational models were able to simulate the movement of the aorta and blood inside accurately. Our results indicated that aortic stress can effectively differentiate patients of the dilated group from the ones of the nondilated group. Mean ascending aortic stress-P1 (maximal principal stress) from the dilated group was 54% higher than that from the nondilated group (97.97 kPa vs. 63.47 kPa, p value = 0.044) under systolic pressure. Velocity magnitude in the aorta and aortic wall displacement of the dilated group were also greater than those of the nondilated group with p value < 0.1.

Conclusion

Computational modeling and ascending aortic biomechanical factors may be used as a potential tool to identify and analyze aortic response to dilation. Large-scale clinical studies are needed to validate these preliminary findings.

Systems Virology and Human Cytomegalovirus: Using High Throughput Approaches to Identify Novel Host-Virus Interactions During Lytic Infection

Human Cytomegalovirus (HCMV) is a highly prevalent herpesvirus, persistently infecting between 30 and 100% of the population, depending on socio-economic status (Fields et al., 2013). HCMV remains an important clinical pathogen accounting for more than 60% of complications associated with solid organ transplant patients (Kotton, 2013; Kowalsky et al., 2013; Bruminhent and Razonable, 2014). It is also the leading cause of infectious congenital birth defects and has been linked to chronic inflammation and immune aging (Ballard et al., 1979; Griffith et al., 2016; Jergovic et al., 2019). There is currently no effective vaccine and HCMV antivirals have significant side effects. As current antivirals target viral genes, the virus can develop resistance, reducing drug efficacy. There is therefore an urgent need for new antiviral agents that are effective against HCMV, have better toxicity profiles and are less vulnerable to the emergence of resistant strains. Targeting of host factors that are critical to virus replication is a potential strategy for the development of novel antivirals that circumvent the development of viral resistance. Systematic high throughput approaches provide powerful methods for the identification of novel host-virus interactions. As well as contributing to our basic understanding of virus and cell biology, such studies provide potential targets for the development of novel antiviral agents. High-throughput studies, such as RNA sequencing, proteomics, and RNA interference screens, are useful tools to identify HCMV-induced global changes in host mRNA and protein expression levels and host factors important for virus replication. Here, we summarize new findings on HCMV lytic infection from high-throughput studies since 2014 and how screening approaches have evolved.

Normalization of multicenter CT radiomics by a generative adversarial network method

PURPOSE

To reduce the variability of radiomics features caused by computed tomography (CT) imaging protocols through using a generative adversarial network (GAN) method.

MATERIAL AND METHODS

In this study, we defined a set of images acquired with a certain imaging protocol as a domain, and a total of 4 domains (A, B, C, and T [target]) from 3 different scanners were included. In dataset#1, 60 patinets for each domain were collected. Datasets#2 and #3 included 40 slices of spleen for each of the domains. In dataset#4, the slices of 3 colorectal cancer groups (n = 28, 38, and 32) were separately retrieved from 3 different scanners, and each group contained short-term and long-term survivors. 77 features were extracted for evaluation by comparing features distributions. First, we trained the GAN model on dataset#1 to learn how to normalize images from domains A, B, and C to T. Next, by comparing feature distributions between normalized images of the different domains, we identified the appropriate model and assessed it , in dataset #2 and dataset#3, respectively. Finally, to investigate whether our proposed method could facilitate multicenter radiomics analysis, we built the lasso classifier to distinguish short-term from long-term survivors based on a certain group in dataset#4, and validate it in another two groups, which formed a cross-validation between groups in dataset#4.

RESULTS

After normalization, the percentage of aligned features between domains A vs T, B vs T, and C vs T increased from 10.4 %, 18.2%, and 50.1% to 93.5%, 89.6%, and 77.9%, respectively. In the cross-validation results, average improvement of the area under the receiver operating characteristic curve achieved 11% (3%-32%).

CONCLUSION

Our proposed GAN-based normalization method could reduce the variability of radiomics features caused by different CT imaging protocols and facilitate multicenter radiomics analysis.

Rescuing compounds for Lesch-Nyhan disease identified using stem cell-based phenotypic screening

Lesch-Nyhan disease (LND) is a rare monogenic disease caused by deficiency of the salvage pathway enzyme hypoxanthine-guanine phosphoribosyltransferase (HGPRT). LND is characterized by severe neuropsychiatric symptoms that currently cannot be treated. Predictive in vivo models are lacking for screening and evaluating candidate drugs because LND-associated neurological symptoms are not recapitulated in HGPRT-deficient animals. Here, we used human neural stem cells and neurons derived from induced pluripotent stem cells (iPSCs) of children affected with LND to identify neural phenotypes of interest associated with HGPRT deficiency to develop a target-agnostic-based drug screening system. We screened more than 3000 molecules and identified 6 pharmacological compounds, all possessing an adenosine moiety, that corrected HGPRT deficiency-associated neuronal phenotypes by promoting metabolism compensations in an HGPRT-independent manner. This included S-adenosylmethionine, a compound that had already been used as a compassionate approach to ease the neuropsychiatric symptoms in LND. Interestingly, these compounds compensate abnormal metabolism in a manner complementary to the gold standard allopurinol and can be provided to patients with LND via simple food supplementation. This experimental paradigm can be easily adapted to other metabolic disorders affecting normal brain development and functioning in the absence of a relevant animal model.

Reference values for high attenuation areas on chest CT in a healthy, never-smoker, multi-ethnic sample: The MESA study

BACKGROUND AND OBJECTIVE

Normative values for HAA-a quantitative, CT-based measure of subclinical ILD-in healthy adults are needed to improve interpretability in clinical and research settings.

METHODS

HAA was measured on full-lung CT in 3110 participants in the MESA study. Clinical prediction models were developed using a healthy never-smoker subset with normal spirometry (n = 696). RMSE on cross-validation was used as the primary criterion for model selection. Parametric and non-parametric methods were considered. z-Scores were calculated for the entire study sample. Associations between z-scores and several ILD features were estimated.

RESULTS

In the healthy never-smoker subset, the mean age was 69 years with a range of 54-93 years. The median HAA was 4.3% with a range of 2.7-17.8%. Linear regression had better predictive performance than other methods. The final model included race, height, weight, age and sex. The standard error of the estimate was 1.62 with a cross-validated RMSE of 1.64 and an adjusted R² of 0.139. z-Scores were associated with several ILD outcomes in adjusted models, including ILA (OR: 1.40 per z-unit; 95% CI: 1.30, 1.52), exertional dyspnoea (OR: 1.08 per z-unit; 95% CI: 1.02, 1.15) and FVC (expected increase per z-unit: -2.49; 95% CI: -2.95, - 2.03).

CONCLUSION

We present a reference equation and z-scores to define expected values of HAA on full-lung CT to aid HAA interpretation in middle-aged and older adults.

Irbesartan in Marfan syndrome (AIMS): a double-blind, placebo-controlled randomised trial

BACKGROUND

Irbesartan, a long acting selective angiotensin-1 receptor inhibitor, in Marfan syndrome might reduce aortic dilatation, which is associated with dissection and rupture. We aimed to determine the effects of irbesartan on the rate of aortic dilatation in children and adults with Marfan syndrome.

METHODS

We did a placebo-controlled, double-blind randomised trial at 22 centres in the UK. Individuals aged 6-40 years with clinically confirmed Marfan syndrome were eligible for inclusion. Study participants were all given 75 mg open label irbesartan once daily, then randomly assigned to 150 mg of irbesartan (increased to 300 mg as tolerated) or matching placebo. Aortic diameter was measured by echocardiography at baseline and then annually. All images were analysed by a core laboratory blinded to treatment allocation. The primary endpoint was the rate of aortic root dilatation. This trial is registered with ISRCTN, number ISRCTN90011794.

FINDINGS

Between March 14, 2012, and May 1, 2015, 192 participants were recruited and randomly assigned to irbesartan (n=104) or placebo (n=88), and all were followed for up to 5 years. Median age at recruitment was 18 years (IQR 12-28), 99 (52%) were female, mean blood pressure was 110/65 mm Hg (SDs 16 and 12), and 108 (56%) were taking β blockers. Mean baseline aortic root diameter was 34·4 mm in the irbesartan group (SD 5·8) and placebo group (5·5). The mean rate of aortic root dilatation was 0·53 mm per year (95% CI 0·39 to 0·67) in the irbesartan group compared with 0·74 mm per year (0·60 to 0·89) in the placebo group, with a difference in means of -0·22 mm per year (-0·41 to -0·02, p=0·030). The rate of change in aortic Z score was also reduced by irbesartan (difference in means -0·10 per year, 95% CI -0·19 to -0·01, p=0·035). Irbesartan was well tolerated with no observed differences in rates of serious adverse events.

INTERPRETATION

Irbesartan is associated with a reduction in the rate of aortic dilatation in children and young adults with Marfan syndrome and could reduce the incidence of aortic complications.

FUNDING

British Heart Foundation, the UK Marfan Trust, the UK Marfan Association.

Improving comparability between qPCR-based telomere studies

Comparability of findings from qPCR‐based telomere studies is hampered by such measurement results being assay‐specific, precluding a direct quantitative comparisons of observed differences and/or slopes of associations between studies. It is proposed that this can be partially alleviated by expressing qPCR‐based telomere data as Z‐scores.

Soft sensor based on 2D-fluorescence and process data enabling real-time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.

A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis

Background

Patient narrative data in online health care forums (communities) are receiving increasing attention from the scientific community for implementing patient-centered care. Natural language processing (NLP) methods are gaining more and more attention because of the enormous data volume. However, state-of-the-art NLP still cannot meet the need of high-resolution analysis of patients’ narratives. Manual qualitative analysis still plays a pivotal role in answering complicated research questions from analyzing patient narratives.

Objective

This study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums.

Methods

Our systematic framework consists of 4 phases: (1) data collection, (2) data preparation, (3) content analysis, and (4) interpretation of the results. Data collection and data preparation phases are constructed based on text mining methods for identifying appropriate online health forums for data collection, differentiating posts of patients from other stakeholders, protecting patients’ privacy, sampling, and choosing the unit of analysis. Content analysis phase is built on the framework method, which facilitates and accelerates the identification of patterns and themes by an interdisciplinary research team. In the end, the focus of interpretation of the results phase is to measure the data quality and interpret the findings regarding the dimensions and aspects of patients’ experiences and concerns in their original contexts.

Results

We demonstrated the usability of the proposed systematic framework using 2 case studies: one on determining factors affecting patients’ attitudes toward antidepressants and another on identifying the disease management strategies in patient with diabetes facing financial difficulties. The framework provides a clear step-by-step process for systematic content analysis of patient narratives and produces high-quality structured results that can be used for describing patterns or regularities in patients’ experiences, generating and testing hypotheses, and identifying areas of improvement in the health care systems.

Conclusions

The systematic framework is a rigorous and standardized method for qualitative analysis of patient narratives. Findings obtained through such a process indicate authentic dimensions and aspects of patient experiences and shed light on patients’ concerns, needs, preferences, and values, which are the core of patient-centered care.

International Registered Report Identifier (IRRID)

RR1-10.2196/13914