Showing papers in "Journal of Translational Medicine in 2021"

TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository.

Abstract: Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

The effect of omega-3 fatty acid supplementation on clinical and biochemical parameters of critically ill patients with COVID-19: a randomized clinical trial.

Abstract: Omega-3 polyunsaturated fatty acids (n3-PUFAs) may exert beneficial effects on the immune system of patients with viral infections This paper aimed to examine the effect of n3-PUFA supplementation on inflammatory and biochemical markers in critically ill patients with COVID-19 A double-blind, randomized clinical trial study was conducted on 128 critically ill patients infected with COVID-19 who were randomly assigned to the intervention (fortified formula with n3-PUFA) (n = 42) and control (n = 86) groups Data on 1 month survival rate, blood glucose, sodium (Na), potassium (K), blood urea nitrogen (BUN), creatinine (Cr), albumin, hematocrit (HCT), calcium (Ca), phosphorus (P), mean arterial pressure (MAP), O2 saturation (O2sat), arterial pH, partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), bicarbonate (HCO3), base excess (Be), white blood cells (WBCs), Glasgow Coma Scale (GCS), hemoglobin (Hb), platelet (Plt), and the partial thromboplastin time (PTT) were collected at baseline and after 14 days of the intervention The intervention group had significantly higher 1-month survival rate and higher levels of arterial pH, HCO3, and Be and lower levels of BUN, Cr, and K compared with the control group after intervention (all P < 005) There were no significant differences between blood glucose, Na, HCT, Ca, P, MAP, O2sat, PO2, PCO2, WBCs, GCS, Hb, Plt, PTT, and albumin between two groups Omega-3 supplementation improved the levels of several parameters of respiratory and renal function in critically ill patients with COVID-19 Further clinical studies are warranted Trial registry Name of the registry: This study was registered in the Iranian Registry of Clinical Trials (IRCT); Trial registration number: IRCT20151226025699N3; Date of registration: 2020520; URL of trial registry record: https://enirctir/trial/48213

A paradigm shift in cell-free approach: the emerging role of MSCs-derived exosomes in regenerative medicine.

Abstract: Recently, mesenchymal stem/stromal cells (MSCs) due to their pro-angiogenic, anti-apoptotic, and immunoregulatory competencies along with fewer ethical issues are presented as a rational strategy for regenerative medicine. Current reports have signified that the pleiotropic effects of MSCs are not related to their differentiation potentials, but rather are exerted through the release of soluble paracrine molecules. Being nano-sized, non-toxic, biocompatible, barely immunogenic, and owning targeting capability and organotropism, exosomes are considered nanocarriers for their possible use in diagnosis and therapy. Exosomes convey functional molecules such as long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs), proteins (e.g., chemokine and cytokine), and lipids from MSCs to the target cells. They participate in intercellular interaction procedures and enable the repair of damaged or diseased tissues and organs. Findings have evidenced that exosomes alone are liable for the beneficial influences of MSCs in a myriad of experimental models, suggesting that MSC- exosomes can be utilized to establish a novel cell-free therapeutic strategy for the treatment of varied human disorders, encompassing myocardial infarction (MI), CNS-related disorders, musculoskeletal disorders (e.g. arthritis), kidney diseases, liver diseases, lung diseases, as well as cutaneous wounds. Importantly, compared with MSCs, MSC- exosomes serve more steady entities and reduced safety risks concerning the injection of live cells, such as microvasculature occlusion risk. In the current review, we will discuss the therapeutic potential of MSC- exosomes as an innovative approach in the context of regenerative medicine and highlight the recent knowledge on MSC- exosomes in translational medicine, focusing on in vivo researches.

Panaxydol attenuates ferroptosis against LPS-induced acute lung injury in mice by Keap1-Nrf2/HO-1 pathway.

Abstract: Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) induces uncontrolled and self-amplified pulmonary inflammation, and has high morbidity and mortality rates in critically ill patients. In recent years, many bioactive ingredients extracted from herbs have been reported to effectively ameliorate ALI/ARDS via different mechanisms. Ferroptosis, categorized as regulated necrosis, is more immunogenic than apoptosis and contributes to the progression of ALI. In this study, we examined the impact of panaxydol (PX), isolated from the roots of Panax ginseng, on lipopolysaccharide (LPS)-induced ALI in mice. In vivo, the role of PX on LPS-induced ALI in mice was tested by determination of LPS-induced pulmonary inflammation, pulmonary edema and ferroptosis. In vitro, BEAS-2B cells were used to investigate the molecular mechanisms by which PX functions via determination of inflammation, ferroptosis and their relationship. Administration of PX protected mice against LPS-induced ALI, including significantly ameliorated lung pathological changes, and decreased the extent of lung edema, inflammation, and ferroptosis. In vitro, PX inhibited LPS-induced ferroptosis and inflammation in bronchial epithelial cell line BEAS-2B cells. The relationship between ferroptosis and inflammation was investigated. The results showed that ferroptosis mediated inflammation in LPS-treated BEAS-2B cells, and PX might ameliorate LPS-induced inflammation via inhibiting ferroptosis. Meanwhile, PX could upregulate Keap1-Nrf2/HO-1 pathway, and selective inhibition of Keap1-Nrf2/HO-1 pathway significantly abolished the anti-ferroptotic and anti-inflammatory functions of PX in LPS-treated cells. PX attenuates ferroptosis against LPS-induced ALI via Keap1-Nrf2/HO-1 pathway, and is a promising novel therapeutic candidate for ALI.

One-tube SARS-CoV-2 detection platform based on RT-RPA and CRISPR/Cas12a.

Abstract: COVID-19 has spread rapidly around the world, affecting a large percentage of the population. When lifting certain mandatory measures for an economic restart, robust surveillance must be established and implemented, with nucleic acid detection for SARS-CoV-2 as an essential component. We tried to develop a one-tube detection platform based on RT-RPA (Reverse Transcription and Recombinase Polymerase Isothermal Amplification) and DNA Endonuclease-Targeted CRISPR Trans Reporter (DETECTR) technology, termed OR-DETECTR, to detect SARS-CoV-2. We designed RT-RPA primers of the RdRp and N genes following the SARS-CoV-2 gene sequence. We optimized reaction components so that the detection process could be carried out in one tube. Specificity was demonstrated by detecting nucleic acid samples from pseudoviruses from seven human coronaviruses and Influenza A (H1N1). Clinical samples were used to validate the platform and all results were compared to rRT-PCR. RNA standards and pseudoviruses diluted by different gradients were used to demonstrate the detection limit. Additionally, we have developed a lateral flow assay based on OR-DETECTR for detecting COVID-19. The OR-DETECTR detection process can be completed in one tube, which takes approximately 50 min. This method can specifically detect SARS-CoV-2 from seven human coronaviruses and Influenza A (H1N1), with a low detection limit of 2.5 copies/µl input (RNA standard) and 1 copy/µl input (pseudovirus). Results of six samples from SARS-CoV-2 patients, eight samples from patients with fever but no SARS-CoV-2 infection, and one mixed sample from 40 negative controls showed that OR-DETECTR is 100% consistent with rRT-PCR. The lateral flow assay based on OR-DETECTR can be used for the detection of COVID-19, and the detection limit is 2.5 copies/µl input. The OR-DETECTR platform for the detection of COVID-19 is rapid, accurate, tube closed, easy-to-operate, and free of large instruments.

SLC7A11 regulated by NRF2 modulates esophageal squamous cell carcinoma radiosensitivity by inhibiting ferroptosis

Abstract: Background Solute carrier family 7 member 11(SLC7A11) is a component of cysteine/glutamate transporter, which plays a key role in tumor growth; however, its underlying effect on radiosensitivity in esophageal squamous cell carcinoma (ESCC) remains unclear. This study aimed to clarify SLC7A11's expression and correlation with nuclear expression of nuclear factor erythroid-2 (NRF2)-associated radioresistance in ESCC. Methods We included 127 ESCC patients who received radical chemoradiotherapy. Immunohistochemical staining was used to detect SLC7A11 and NRF2 nuclear expression, and the relationship between clinicopathological characteristics and survival rates or therapy response were evaluated. Western blot, dual-reporter assays and Chromatin immunoprecipitation (ChIP)-sequencing were used to analyze their relationship in vitro. Their roles in radioresistance were then investigated through multiple validation steps. Results NRF2 nuclear expression and SLC7A11 expression were overexpressed in ESCC tissues and were positively correlated with one another. NRF2 nuclear expression was significantly associated with tumor length, lymph node metastasis, and TNM stage, while SLC7A11 expression was associated with lymph node metastasis. Patients with high NRF2 nuclear expression and SLC7A11 expression had significantly shorter overall and progression-free survival, and poor treatment response. The multivariate model showed that NRF2 nuclear expression and SLC7A11 expression, sex and tumor location are independent prognostic factors. In vitro analysis confirmed that hyperactivation of NRF2 induced SLC7A11 expression by directly binding to its promoter region, promoting radioresistance, reducing radiotherapy-induced lipid peroxidation levels, PTGS2 expression, and radiotherapy-related ferroptosis morphologic features. Conclusion Our study reveals a connection between high SLC7A11 expression and NRF2 nuclear expression in patients with ESCC that was related to worse survival and poorer therapy outcomes. SLC7A11-mediated ferroptosis inhibition induced NRF2-associated radioresistance, highlighting potential of NRF2/SLC7A11/ferroptosis axis as future therapeutic targets against therapy resistance biomarker.

Exosomal LncRNA LINC00659 transferred from cancer-associated fibroblasts promotes colorectal cancer cell progression via miR-342-3p/ANXA2 axis.

Abstract: Cancer-associated fibroblasts (CAFs) play a pivotal role in regulating tumor progression by transferring exosomes to adjacent cells. Our aim was to clarify the role of LINC00659 encapsulated in CAFs-derived exosomes (CAFs-exo) in colorectal cancer (CRC). CAFs and normal fibroblasts (NFs) were isolated and cultured. CAFs-exo and NFs-derived exosomes (NFs-exo) were characterized by transmission electron microscope and Western blot. The mRNA level of LINC00659 in CAFs-exo and NFs-exo were measured. Then we analyzed cell proliferation by CCK-8 and clone formation assay, cell migration by cell scratch, and cell invasion by Transwell. Epithelial mesenchymal transformation (EMT) related markers E-cadherin, N-cadherin, Vimentin and Snail-1 expressions were assessed by Western blot. The binding of LINC00659 and miR-342-3p, miR-342-3p and ANXA2 were analyzed by dual-luciferase reporter gene assay. CAFs and NFs showed a spindle-like morphology. CAFs-exo promoted CRC cell proliferation, migration, invasion and EMT progression. The expression of LINC00659 in CAF-derived exosomes was significantly increased, and fibroblasts could transfer exosomal LINC00659 to CRC cells. We further revealed that transfection of miR-342-3p mimic or sh-ANXA2 could obviously reverse the promotion effect of exosomal LINC00659 on CRC progression. Functional studies reveal that LINC00659 is transferred from CAFs to the cancer cells via exosomes, where it promotes CRC cell proliferation, invasion, migration and EMT progression in vitro. Mechanistically, LINC00659 interacts directly with miR-342-3p to increase ANXA2 expression in CRC cells. Collected evidence supported that CAFs-derived exosomal LINC00659 promotes CRC cell proliferation, invasion and migration via miR-342-3p/ANXA2axis.

Application of stem cell-derived exosomes in ischemic diseases: opportunity and limitations.

Abstract: Ischemic diseases characterized by an insufficient blood flow that leads to a decrease in oxygen and nutrient uptake by cells have emerged as an important contributor to both disability and death worldwide. Up-regulation of angiogenesis may be a key factor for the improvement of ischemic diseases. This article searched articles in PubMed with the following keywords: stem cells, exosomes, angiogenesis, ischemic diseases either alone or in grouping form. The most relevant selected items were stem cell-derived exosomes and ischemic diseases. A growing body of evidence indicates that stem cells produce exosomes, which is the novel emerging approach to cell-to-cell communication and offers a new standpoint on known therapeutic strategies of ischemic diseases. Exosomes transport biological molecules such as many types of proteins, RNAs, DNA fragments, signaling molecules, and lipids between cells. Different stem cells release exosomes representing beneficial effects on ischemic diseases as they promote angiogenesis both in vitro and in vivo experiments. Application of exosomes for therapeutic angiogenesis opened new opportunities in the regenerative medicine, however, some limitations regarding exosomes isolation and application remain concerned. In addition, most of the experiments were conducted in preclinical and therefore translation of these results from bench to bed requires more effort in this field. Exosomes from stem cells are a promising tool for the treatment of ischemic diseases. In addition, translation of pre-clinic results into clinic needs further studies in this field.

Correction to: Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

Abstract: Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved. The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis. In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE. IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.

Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis.

Abstract: Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA is difficult to diagnose due to the lack of effective biomarkers. This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses. Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein–protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student’s t-test. A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be potential biomarkers for diagnosis of early RA. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA. This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.

A novel immune classification reveals distinct immune escape mechanism and genomic alterations: implications for immunotherapy in hepatocellular carcinoma.

Abstract: The tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy. However, the heterogeneous TIME and the mechanisms by which TIME affects immunotherapy have not been elucidated in hepatocellular carcinoma (HCC). A total of 2195 eligible HCC patients from TCGA and GEO database were collected. We comprehensively explored the different heterogeneous TIME phenotypes and its clinical significance. The potential immune escape mechanisms and what genomic alterations may drive the formation of different phenotypes were further investigated. We identified three phenotypes in HCC: TIME-1, the “immune-deficiency” phenotype, with immune cell depletion and proliferation; TIME-2, the “immune-suppressed” phenotype, with enrichment of immunosuppressive cells; TIME-3, the “immune-activated phenotype”, with abundant leukocytes infiltration and immune activation. The prognosis and sensitivity to both sorafenib and immunotherapy differed among the three phenotypes. We also underlined the potential immune escape mechanisms: lack of leukocytes and defective tumor antigen presentation capacity in TIME-1, increased immunosuppressive cells in TIME-2, and rich in immunoinhibitory molecules in TIME-3. The different phenotypes also demonstrated specific genomic events: TIME-1 characterized by TP53, CDKN2A, CTNNB1, AXIN1 and FOXD4 alterations; TIME-2 characterized by significant alteration patterns in the PI3K pathway; TIME-3 characterized by ARID1A mutation. Besides, the TIME index (TI) was proposed to quantify TIME infiltration pattern, and it was a superior prognostic and immunotherapy predictor. A pipeline was developed to classify single patient into one of these three subtypes and calculated the TI. We identified three TIME phenotypes with different clinical outcomes, immune escape mechanisms and genomic alterations in HCC, which could present strategies for improving the efficacy of immunotherapy. TI as a novel prognostic and immunotherapeutic signature that could guide personalized immunotherapy and clinical management of HCC.

Patient-derived organoid (PDO) platforms to facilitate clinical decision making

Abstract: Based on recent advances in organoid research as well as the need to find more accurate models for drug screening in cancer research, patient-derived organoids have emerged as an effective in vitro model system to study cancer. Showing numerous advantages over 2D cell lines, 3D cell lines, and primary cell culture, organoids have been applied in drug screening to demonstrate the correlation between genetic mutations and sensitivity to targeted therapy. Organoids have also been used in co-clinical trials to compare drug responses in organoids to clinical responses in the corresponding patients. Numerous studies have reported the successful use of organoids to predict therapy response in cancer patients. Recently, organoids have been adopted to predict treatment response to radiotherapy and immunotherapy. The development of high throughput drug screening and organoids-on-a-chip technology can advance the use of patient-derived organoids in clinical practice and facilitate therapeutic decision-making.

Eight-hour time-restricted feeding improves endocrine and metabolic profiles in women with anovulatory polycystic ovary syndrome

Abstract: Time-restricted feeding (TRF) is a form of intermittent fasting, which is beneficial for weight loss and cardiometabolic health. Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine and metabolic diseases affecting women of childbearing age. It is associated with an increased prevalence of metabolic syndrome, cardiovascular diseases and type 2 diabetes. The effects of TRF on PCOS patients remains undefined, here we investigated the impact of TRF on women with anovulatory PCOS. Eighteen PCOS women aged between 18 and 31 with anovulation participated in a 6-week trial which were divided into two consecutive periods: (1) 1-week baseline weight stabilization period and (2) 5-week TRF period. Fifteen participants completed the study. Changes in body weight, body mass index (BMI), Waist-to-Hip Ratio, skeletal muscle mass, body fat mass (BFM), body fat percentage (BF%), visceral fat area (VFA), luteinizing hormone (LH), follicle-stimulating hormone (FSH), LH/FSH, total testosterone (TT), sex hormone-binding globulin (SHBG), free androgen index (FAI), fasting glucose, fasting insulin (FINS), homeostasis model assessment-insulin resistance (HOMA-IR), area under the curve (AUC) for insulin (AUCIns), area under the curve (AUC) for glucose (AUCGlu), AUCIns/AUCGlu Ratio, lipids, uric acid, alanine aminotransferase (ALT), aspartate aminotransferase, high-sensitivity C-reactive protein (hsCRP), insulin-like growth factor (IGF-1), menstrual cycle and eating behaviors were evaluated. Significant changes in body weight, BMI, BFM, BF%, VFA, TT, SHBG, FAI, FINS, HOMA-IR, AUCIns, AUCIns/AUCGlu Ratio, ALT, hsCRP and IGF-1 were found after the TRF period. An improvement in menstrual cycle irregularity was detected in 73.3% (11/15) patients. The diet of TRF may be beneficial to anovulatory PCOS on weight loss especially reducing body fat, improving menstruation, hyperandrogenemia, insulin resistance and chronic inflammation. Trial registration Clinicaltrial.gov, NCT04580433, registered October 8, 2020, https://clinicaltrials.gov/ct2/show/NCT04580433.

Hypoxia and the phenomenon of immune exclusion

Abstract: Over the last few years, cancer immunotherapy experienced tremendous developments and it is nowadays considered a promising strategy against many types of cancer. However, the exclusion of lymphocytes from the tumor nest is a common phenomenon that limits the efficiency of immunotherapy in solid tumors. Despite several mechanisms proposed during the years to explain the immune excluded phenotype, at present, there is no integrated understanding about the role played by different models of immune exclusion in human cancers. Hypoxia is a hallmark of most solid tumors and, being a multifaceted and complex condition, shapes in a unique way the tumor microenvironment, affecting gene transcription and chromatin remodeling. In this review, we speculate about an upstream role for hypoxia as a common biological determinant of immune exclusion in solid tumors. We also discuss the current state of ex vivo and in vivo imaging of hypoxic determinants in relation to T cell distribution that could mechanisms of immune exclusion and discover functional-morphological tumor features that could support clinical monitoring.

Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.

Abstract: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).

The evidence base for physiotherapy in myalgic encephalomyelitis/chronic fatigue syndrome when considering post-exertional malaise: a systematic review and narrative synthesis.

Abstract: Due to the inconsistent use of diagnostic criteria in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), it is unsure whether physiotherapeutic management regarded effective in ME/CFS is appropriate for patients diagnosed with criteria that consider post-exertional malaise (PEM) as a hallmark feature. To appraise current evidence of the effects of physiotherapy on symptoms and functioning in ME/CFS patients in view of the significance of PEM in the applied diagnostic criteria for inclusion. A systematic review of randomized controlled trials published over the last two decades was conducted. Studies evaluating physiotherapeutic interventions for adult ME/CFS patients were included. The diagnostic criteria sets were classified into three groups according to the extent to which the importance of PEM was emphasized: chronic fatigue (CF; PEM not mentioned as a criterion), CFS (PEM included as an optional or minor criterion) or ME (PEM is a required symptom). The main results of included studies were synthesized in relation to the classification of the applied diagnostic criteria. In addition, special attention was given to the tolerability of the interventions. Eighteen RCTs were included in the systematic review: three RCTs with CF patients, 14 RCTs with CFS patients and one RCT covering ME patients with PEM. Intervention effects, if any, seemed to disappear with more narrow case definitions, increasing objectivity of the outcome measures and longer follow-up. Currently, there is no scientific evidence when it comes to effective physiotherapy for ME patients. Applying treatment that seems effective for CF or CFS patients may have adverse consequences for ME patients and should be avoided.

Influence of COVID-19 on lifestyle behaviors in the Middle East and North Africa Region: a survey of 5896 individuals.

Abstract: Coronavirus disease (COVID-19) pandemic has affected health and lifestyle behaviors of people globally. This project aims to identify the impact of COVID-19 on lifestyle behavior of individuals in the Middle East and North Africa (MENA) region during confinement. We conducted an online survey in 17 countries (Egypt, Jordan, United Arab Emirates, Kuwait, Bahrain, Saudi Arabia, Oman, Qatar, Yemen, Syria, Palestine, Algeria, Morocco, Libya, Tunisia, Iraq, and Sudan) from the MENA region on August and September 2020. The questionnaire included self-reported information on lifestyle behaviors, including physical activity, eating habits, smoking, watching television, social media use and sleep before and during the pandemic. Logistic regression was performed to analyze the impact of COVID-19 on lifestyle behaviors. A total of 5896 participants were included in the final analysis and 62.8% were females. The BMI of the participants was 25.4 ± 5.8 kg/m2. Around 38.4% of the participants stopped practicing any physical activities during the confinement (P < 0.001), and 57.1% reported spending more than 2 h on social media (P < 0.001). There were no significant changes in smoking habits. Also, 30.9% reported an improvement in their eating habits compared with 24.8% reported worsening of their eating habits. Fast-food consumption decreased significantly in 48.8% of the study population. This direct/indirect exposure to COVID-19 was associated with an increased consumption of carbohydrates (OR = 1.09; 95% CI = 1.02–1.17; P = 0.01), egg (OR = 1.08; 95% CI = 1.02–1.16; P = 0.01), sugar (OR = 1.09; 95% CI = 1.02–1.16; P = 0.02), meat, and poultry (OR = 1.13; 95% CI = 1.06–1.20; P < 0.01). There was also associated increase in hours spent on watching television (OR = 1.07; 95% CI = 1.02–1.12; P < 0.01) and social media (OR = 1.09; 95% CI = 1.01–1.18; P = 0.03). However, our results showed a reduction in sleeping hours among those exposed to COVID-19 infection (OR = 0.85; 95% CI = 0.77–0.94; P < 0.01). The COVID-19 pandemic was associated with an increase in food consumption and sedentary life. Being exposed to COVID-19 by direct infection or through an infected household is a significant predictor of amplifying these changes. Public health interventions are needed to address healthy lifestyle behaviors during and after the COVID-19 pandemic.

Effects of time-restricted feeding in weight loss, metabolic syndrome and cardiovascular risk in obese women.

Abstract: The increasing prevalence of overweight and obesity among the worldwide population has been associated with a range of adverse health consequences such as Type 2 diabetes and cardiovascular diseases. The metabolic syndrome (MetS) is a cluster of cardiometabolic abnormalities that occur more commonly in overweight individuals. Time-restricted feeding (TRF) is a dietary approach used for weight loss and overall health. TRF may be an option for those subjects who struggle with extreme restriction diets with foods that generally do not belong to an individual's habits. The purpose of this study was to determine the effect of TRF on body composition and the association of weight loss with metabolic and cardiovascular risks in obese middle-aged women. A non-randomized controlled clinical trial was performed over 3 months in obese women (TRF group, n = 20, BMI 32.53 ± 1.13 vs. Control n = 12, BMI 34.55 ± 1.20). The TRF protocol adopted was 16 h without any energy intake followed by 8 h of normal food intake. Anthropometric measurements, body composition, blood biomarkers, cardiovascular risk in 30 years (CVDRisk30y), and quality of life were evaluated at baseline and after the 3 months. TRF was effective in reducing weight (~ 4 kg), BMI, % of body fat (%BF), waist circumference from baseline without changes in blood biomarkers associated with MetS. TRF promoted a reduction in CVDRisk30y (12%) wich was moderately correlated with %BF (r = 0.62, n = 64, p < 0.001) and %MM (r = − 0.74, n = 64, p < 0.001). TRF protocol reduces body weight without changes in biomarkers related to MetS. In addition, the anthropometric evaluation that predicts %BF and %MM could be used as an approach to follow individuals engaged in the TRF regimen since they correlate with cardiovascular risk.

Translational precision medicine: an industry perspective.

Abstract: In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Immune landscape and prognostic immune-related genes in KRAS-mutant colorectal cancer patients.

Abstract: KRAS gene is the most common type of mutation reported in colorectal cancer (CRC). KRAS mutation-mediated regulation of immunophenotype and immune pathways in CRC remains to be elucidated. 535 CRC patients were used to compare the expression of immune-related genes (IRGs) and the abundance of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment between KRAS-mutant and KRAS wild-type CRC patients. An independent dataset included 566 cases of CRC and an in-house RNA sequencing dataset were served as validation sets. An in-house dataset consisting of 335 CRC patients were used to analyze systemic immune and inflammatory state in the presence of KRAS mutation. An immue risk (Imm-R) model consist of IRG and TIICs for prognostic prediction in KRAS-mutant CRC patients was established and validated. NF-κB and T-cell receptor signaling pathways were significantly inhibited in KRAS-mutant CRC patients. Regulatory T cells (Tregs) was increased while macrophage M1 and activated CD4 memory T cell was decreased in KRAS-mutant CRC. Prognosis correlated with enhanced Tregs, macrophage M1 and activated CD4 memory T cell and was validated. Serum levels of hypersensitive C-reactive protein (hs-CRP), CRP, and IgM were significantly decreased in KRAS-mutant compared to KRAS wild-type CRC patients. An immune risk model composed of VGF, RLN3, CT45A1 and TIICs signature classified CRC patients with distinct clinical outcomes. KRAS mutation in CRC was associated with suppressed immune pathways and immune infiltration. The aberrant immune pathways and immune cells help to understand the tumor immune microenvironments in KRAS-mutant CRC patients.

Potential roles of N6-methyladenosine (m6A) in immune cells.

Abstract: N6-methyl-adenosine (m6A) is one of the most common internal modifications on RNA molecules present in mammalian cells. Deregulation of m6A modification has been recently implicated in many types of human diseases. Therefore, m6A modification has become a research hotspot for its potential therapeutic applications in the treatment of various diseases. The immune system mostly involves different types of immune cells to provide the first line of defense against infections. The immunoregulatory network that orchestrate the immune responses to new pathogens plays a pivotal role in the development of the disease. And m6A modification has been demonstrated to be a major post-transcriptional regulator of immune responses in cells. In this review, we summarize the participants involved in m6A regulation and try to reveal how m6A modification affects the immune responses via changing the immunoregulatory networks.

Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital.

Abstract: Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause) Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC) Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters AUROC of the risk stratification score on training data (AUROC = 094, positive predictive value (PPV) = 097, negative predictive value (NPV) = 080) were comparable to the prospective validation cohort (AUROC = 085, PPV = 091, NPV = 081) The most successful ML algorithm with respect to AUROC was support vector machines (median = 096, interquartile range = 085–099, PPV = 090, NPV = 058) With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation

Metformin attenuates silica-induced pulmonary fibrosis via AMPK signaling.

Abstract: Background Silicosis is one of the most common occupational pulmonary fibrosis caused by respirable silica-based particle exposure, with no ideal drugs at present. Metformin, a commonly used biguanide antidiabetic agent, could activate AMP-activated protein kinase (AMPK) to exert its pharmacological action. Therefore, we sought to investigate the role of metformin in silica-induced lung fibrosis. Methods The anti-fibrotic role of metformin was assessed in 50 mg/kg silica-induced lung fibrosis model. Silicon dioxide (SiO2)-stimulated lung epithelial cells/macrophages and transforming growth factor-beta 1 (TGF-β1)-induced differentiated lung fibroblasts were used for in vitro models. Results At the concentration of 300 mg/kg in the mouse model, metformin significantly reduced lung inflammation and fibrosis in SiO2-instilled mice at the early and late fibrotic stages. Besides, metformin (range 2-10 mM) reversed SiO2-induced cell toxicity, oxidative stress, and epithelial-mesenchymal transition process in epithelial cells (A549 and HBE), inhibited inflammation response in macrophages (THP-1), and alleviated TGF-β1-stimulated fibroblast activation in lung fibroblasts (MRC-5) via an AMPK-dependent pathway. Conclusions In this study, we identified that metformin might be a potential drug for silicosis treatment.

Transcriptome of nasopharyngeal samples from COVID-19 patients and a comparative analysis with other SARS-CoV-2 infection models reveal disparate host responses against SARS-CoV-2.

Abstract: Although it is becoming evident that individual’s immune system has a decisive influence on SARS-CoV-2 disease progression, pathogenesis is largely unknown. In this study, we aimed to profile the host transcriptome of COVID-19 patients from nasopharyngeal samples along with virus genomic features isolated from respective host, and a comparative analyses of differential host responses in various SARS-CoV-2 infection systems. Unique and rare missense mutations in 3C-like protease observed in all of our reported isolates. Functional enrichment analyses exhibited that the host induced responses are mediated by innate immunity, interferon, and cytokine stimulation. Surprisingly, induction of apoptosis, phagosome, antigen presentation, hypoxia response was lacking within these patients. Upregulation of immune and cytokine signaling genes such as CCL4, TNFA, IL6, IL1A, CCL2, CXCL2, IFN, and CCR1 were observed in lungs. Lungs lacked the overexpression of ACE2 as suspected, however, high ACE2 but low DPP4 expression was observed in nasopharyngeal cells. Interestingly, directly or indirectly, viral proteins specially non-structural protein mediated overexpression of integrins such as ITGAV, ITGA6, ITGB7, ITGB3, ITGA2B, ITGA5, ITGA6, ITGA9, ITGA4, ITGAE, and ITGA8 in lungs compared to nasopharyngeal samples suggesting the possible way of enhanced invasion. Furthermore, we found comparatively highly expressed transcription factors such as CBP, CEBP, NFAT, ATF3, GATA6, HDAC2, TCF12 which have pivotal roles in lung injury. Even though this study incorporates a limited number of cases, our data will provide valuable insights in developing potential studies to elucidate the differential host responses on the viral pathogenesis in COVID-19, and incorporation of further data will enrich the search of an effective therapeutics.

Pathophysiology of skeletal muscle disturbances in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract: Chronic Fatigue Syndrome or Myalgic Encephaloymelitis (ME/CFS) is a frequent debilitating disease with an enigmatic etiology. The finding of autoantibodies against s2-adrenergic receptors (s2AdR) prompted us to hypothesize that s2AdR dysfunction is of critical importance in the pathophysiology of ME/CFS. Our hypothesis published previously considers ME/CFS as a disease caused by a dysfunctional autonomic nervous system (ANS) system: sympathetic overactivity in the presence of vascular dysregulation by s2AdR dysfunction causes predominance of vasoconstrictor influences in brain and skeletal muscles, which in the latter is opposed by the metabolically stimulated release of endogenous vasodilators (functional sympatholysis). An enigmatic bioenergetic disturbance in skeletal muscle strongly contributes to this release. Excessive generation of these vasodilators with algesic properties and spillover into the systemic circulation could explain hypovolemia, suppression of renin (paradoxon) and the enigmatic symptoms. In this hypothesis paper the mechanisms underlying the energetic disturbance in muscles will be explained and merged with the first hypothesis. The key information is that s2AdR also stimulates the Na+/K+-ATPase in skeletal muscles. Appropriate muscular perfusion as well as function of the Na+/K+-ATPase determine muscle fatigability. We presume that dysfunction of the s2AdR also leads to an insufficient stimulation of the Na+/K+-ATPase causing sodium overload which reverses the transport direction of the sodium-calcium exchanger (NCX) to import calcium instead of exporting it as is also known from the ischemia–reperfusion paradigm. The ensuing calcium overload affects the mitochondria, cytoplasmatic metabolism and the endothelium which further worsens the energetic situation (vicious circle) to explain postexertional malaise, exercise intolerance and chronification. Reduced Na+/K+-ATPase activity is not the only cause for cellular sodium loading. In poor energetic situations increased proton production raises intracellular sodium via sodium-proton-exchanger subtype-1 (NHE1), the most important proton-extruder in skeletal muscle. Finally, sodium overload is due to diminished sodium outward transport and enhanced cellular sodium loading. As soon as this disturbance would have occurred in a severe manner the threshold for re-induction would be strongly lowered, mainly due to an upregulated NHE1, so that it could repeat at low levels of exercise, even by activities of everyday life, re-inducing mitochondrial, metabolic and vascular dysfunction to perpetuate the disease.

Analysis of viral load in different specimen types and serum antibody levels of COVID-19 patients.

Abstract: COVID-19 has caused a global pandemic and the death toll is increasing. However, there is no definitive information regarding the type of clinical specimens that is the best for SARS-CoV-2 detection, the antibody levels in patients with different duration of disease, and the relationship between antibody level and viral load. Nasopharyngeal swabs, anal swabs, saliva, blood, and urine specimens were collected from patients with a course of disease ranging from 7 to 69 days. Viral load in different specimen types was measured using droplet digital PCR (ddPCR). Meanwhile, anti-nucleocapsid protein (anti-N) IgM and IgG antibodies and anti-spike protein receptor-binding domain (anti-S-RBD) IgG antibody in all serum samples were tested using ELISA. The positive detection rate in nasopharyngeal swab was the highest (54.05%), followed by anal swab (24.32%), and the positive detection rate in saliva, blood, and urine was 16.22%, 10.81%, and 5.41%, respectively. However, some patients with negative nasopharyngeal swabs had other specimens tested positive. There was no significant correlation between antibody level and days after symptoms onset or viral load. Other specimens could be positive in patients with negative nasopharyngeal swabs, suggesting that for patients in the recovery period, specimens other than nasopharyngeal swabs should also be tested to avoid false negative results, and anal swabs are recommended. The antibody level had no correlation with days after symptoms onset or the viral load of nasopharyngeal swabs, suggesting that the antibody level may also be affected by other factors.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

Abstract: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Mitochondrial transplantation therapy for ischemia reperfusion injury: a systematic review of animal and human studies

Abstract: Mitochondria are essential organelles that provide energy for cellular functions, participate in cellular signaling and growth, and facilitate cell death. Based on their multifactorial roles, mitochondria are also critical in the progression of critical illnesses. Transplantation of mitochondria has been reported as a potential promising approach to treat critical illnesses, particularly ischemia reperfusion injury (IRI). However, a systematic review of the relevant literature has not been conducted to date. Here, we systematically reviewed the animal and human studies relevant to IRI to summarize the evidence for mitochondrial transplantation. We searched MEDLINE, the Cochrane library, and Embase and performed a systematic review of mitochondrial transplantation for IRI in both preclinical and clinical studies. We developed a search strategy using a combination of keywords and Medical Subject Heading/Emtree terms. Studies including cell-mediated transfer of mitochondria as a transfer method were excluded. Data were extracted to a tailored template, and data synthesis was descriptive because the data were not suitable for meta-analysis. Overall, we identified 20 animal studies and two human studies. Among animal studies, 14 (70%) studies focused on either brain or heart IRI. Both autograft and allograft mitochondrial transplantation were used in 17 (85%) animal studies. The designs of the animal studies were heterogeneous in terms of the route of administration, timing of transplantation, and dosage used. Twelve (60%) studies were performed in a blinded manner. All animal studies reported that mitochondrial transplantation markedly mitigated IRI in the target tissues, but there was variation in biological biomarkers and pathological changes. The human studies were conducted with a single-arm, unblinded design, in which autologous mitochondrial transplantation was applied to pediatric patients who required extracorporeal membrane oxygenation (ECMO) for IRI–associated myocardial dysfunction after cardiac surgery. The evidence gathered from our systematic review supports the potential beneficial effects of mitochondrial transplantation after IRI, but its clinical translation remains limited. Further investigations are thus required to explore the mechanisms of action and patient outcomes in critical settings after mitochondrial transplantation. Systematic review registration The study was registered at UMIN under the registration number UMIN000043347.

β-Glucan from Lentinula edodes prevents cognitive impairments in high-fat diet-induced obese mice: involvement of colon-brain axis

Abstract: Long-term high fat (HF) diet intake can cause neuroinflammation and cognitive decline through the gut-brain axis. (1, 3)/(1, 6)-β-glucan, an edible polysaccharide isolated from medical mushroom, Lentinula edodes (L. edodes), has the potential to remodel gut microbiota. However, the effects of L. edodes derived β-glucan against HF diet-induced neuroinflammation and cognitive decline remain unknown. This study aimed to evaluate the neuroprotective effect and mechanism of dietary L edodes β-glucan supplementation against the obesity-associated cognitive decline in mice fed by a HF diet. C57BL/6J male mice were fed with either a lab chow (LC), HF or HF with L. edodes β-glucan supplementation diets for 7 days (short-term) or 15 weeks (long-term). Cognitive behavior was examined; blood, cecum content, colon and brain were collected to evaluate metabolic parameters, endotoxin, gut microbiota, colon, and brain pathology. We reported that short-term and long-term L. edodes β-glucan supplementation prevented the gut microbial composition shift induced by the HF diet. Long-term L. edodes β-glucan supplementation prevented the HF diet-induced recognition memory impairment assessed by behavioral tests (the temporal order memory, novel object recognition and Y-maze tests). In the prefrontal cortex and hippocampus, the β-glucan supplementation ameliorated the alteration of synaptic ultrastructure, neuroinflammation and brain-derived neurotrophic factor (BDNF) deficits induced by HF diet. Furthermore, the β-glucan supplementation increased the mucosal thickness, upregulated the expression of tight junction protein occludin, decreased the plasma LPS level, and inhibited the proinflammatory macrophage accumulation in the colon of mice fed by HF diet. This study revealed that L. edodes β-glucan prevents cognitive impairments induced by the HF diet, which may occur via colon-brain axis improvement. The finding suggested that dietary L. edodes β-glucan supplementation may be an effective nutritional strategy to prevent obesity-associated cognitive decline.

An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation

Abstract: Background Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. Methods Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms. Results 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model. Conclusions Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.