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Radiology Radiology

Abnormal amplitude of spontaneous low-frequency fluctuation in children with growth hormone deficiency: A resting-state functional magnetic resonance imaging study.

In Neuroscience letters

Growth hormone deficiency (GHD) is a developmental disorder caused by the partial or complete deficiency of growth hormone secreted by the pituitary gland, or its receptor. Patients with GHD are characterized by short stature, slow growth, and certain cognitive and behavioral abnormalities. Previous behavioral and neuroimaging studies indicate that GHD might affect the brain functional activity associated with cognitive and behavioral abilities. We thus investigated the spontaneous neural activity in children with GHD using amplitude of low-frequency fluctuation (ALFF) analysis. ALFF was calculated based on resting-state functional magnetic resonance imaging (rs-fMRI) data in 26 children with GHD and 15 age- and sex-matched healthy controls (HCs). Comparative analysis revealed that the ALFF of the right lingual gyrus and angular gyrus were significantly increased, while the ALFF of the right dorsolateral superior frontal gyrus, the left postcentral gyrus, superior parietal gyrus and middle temporal gyrus were significantly decreased in children with GHD relative to HCs. These findings support the presence of abnormal brain functional activity in children with GHD, which may account for the abnormal cognition and behavior, such as aggression, somatic complaints, attention deficits, and language withdrawal. This study provides imaging evidence for future studies on the pathophysiological mechanisms of abnormal behavior and cognition in children with GHD.

Zhang Fanyu, Hua Bo, Wang Tengfei, Wang Mei, Ding Zhong Xiang, Ding Ju-Rong


amplitude of low-frequency fluctuation, fMRI, growth hormone deficiency, short stature

General General

Advanced machine-learning techniques in drug discovery.

In Drug discovery today ; h5-index 68.0

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery.

Elbadawi Moe, Gaisford Simon, Basit Abdul W


Internal Medicine Internal Medicine

Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy.

In Gastrointestinal endoscopy ; h5-index 72.0

BACKGROUND AND AIMS : Diagnosis of esophageal cancer or precursor lesions by endoscopic imaging depends on endoscopist expertise and is inevitably subject to interobserver variability. Studies on computer-aided diagnosis (CAD) using deep-learning or machine-learning are on the rise. However, studies with small sample sizes are limited by inadequate statistical strength. Here, we used a meta-analysis to evaluate the diagnostic test accuracy (DTA) of CAD algorithms of esophageal cancers or neoplasms using endoscopic images.

METHODS : Core databases were searched for studies based on endoscopic imaging using CAD algorithms for the diagnosis of esophageal cancer or neoplasms and presenting data on diagnostic performance, and a systematic review and DTA meta-analysis were performed.

RESULTS : Overall, 21 and 19 studies were included in the systematic review and DTA meta-analysis, respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer or neoplasms in the image-based analysis were .97 (95% confidence interval [CI], .95-.99), .94 (95% CI, .89-.96), .88 (95% CI, .76-.94), and 108 (95% CI, 43-273), respectively. Meta-regression showed no heterogeneity, and no publication bias was detected. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer invasion depth were .96 (95% CI, .86-.99), .90 (95% CI, .88-.92), .88 (95% CI, .83-.91), and 138 (95% CI, 12-1,569), respectively.

CONCLUSIONS : CAD algorithms showed high accuracy for the automatic endoscopic diagnosis of esophageal cancer and neoplasms. The limitation of lacking performance in external-validation and clinical application should be overcome.

Bang Chang Seok, Lee Jae Jun, Baik Gwang Ho


Artificial intelligence, Barrett’s esophagus, Deep learning, Esophageal cancer, Esophageal neoplasms

General General

Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes.

In Cell host & microbe ; h5-index 102.0

Small open reading frames (smORFs) and their encoded microproteins play central roles in microbes. However, there is a vast unexplored space of smORFs within human-associated microbes. A recent bioinformatic analysis used evolutionary conservation signals to enhance prediction of small protein families. To facilitate the annotation of specific smORFs, we introduce SmORFinder. This tool combines profile hidden Markov models of each smORF family and deep learning models that better generalize to smORF families not seen in the training set, resulting in predictions enriched for Ribo-seq translation signals. Feature importance analysis reveals that the deep learning models learn to identify Shine-Dalgarno sequences, deprioritize the wobble position in each codon, and group codon synonyms found in the codon table. A core-genome analysis of 26 bacterial species identifies several core smORFs of unknown function. We pre-compute smORF annotations for thousands of RefSeq isolate genomes and Human Microbiome Project metagenomes and provide these data through a public web portal.

Durrant Matthew G, Bhatt Ami S


deep learning, genome annotation, machine learning, microbiome, microproteins, small open reading frames

Internal Medicine Internal Medicine

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.

In Blood advances

Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.

Eckardt Jan-Niklas, Bornhäuser Martin, Wendt Karsten, Middeke Jan Moritz


General General

CRISPRidentify: identification of CRISPR arrays using machine learning approach.

In Nucleic acids research ; h5-index 217.0

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

Mitrofanov Alexander, Alkhnbashi Omer S, Shmakov Sergey A, Makarova Kira S, Koonin Eugene V, Backofen Rolf