Lung-Gut Microbiota and Tryptophan Metabolites Changes in Neonatal Acute Respiratory Distress Syndrome.

Purpose: Neonatal Acute Respiratory Distress Syndrome (NARDS) is a severe respiratory crisis threatening neonatal life. We aim to identify changes in the lung-gut microbiota and lung-plasma tryptophan metabolites in NARDS neonates to provide a differentiated tool and aid in finding potential therapeutic targets. Patients and Methods: Lower respiratory secretions, faeces and plasma were collected from 50 neonates including 25 NARDS patients (10 patients with mild NARDS in the NARDS_M group and 15 patients with moderate-to-severe NARDS in the NARDS_S group) and 25 control patients screened based on gestational age, postnatal age and birth weight. Lower airway secretions and feces underwent 16S rRNA gene sequencing to understand the microbial communities in the lung and gut, while lower airway secretions and plasma underwent LC-MS analysis to understand tryptophan metabolites in the lung and blood. Correlation analyses were performed by comparing differences in microbiota and tryptophan metabolites between NARDS and control, NARDS_S and NARDS_M groups. Results: Significant changes in lung and gut microbiota as well as lung and plasma tryptophan metabolites were observed in NARDS neonates compared to controls. Proteobacteria and Bacteroidota were increased in the lungs of NARDS neonates, whereas Firmicutes, Streptococcus, and Rothia were reduced. Lactobacillus in the lungs decreased in NARDS_S neonates. Indole-3-carboxaldehyde decreased in the lungs of NARDS neonates, whereas levels of 3-hydroxykynurenine, indoleacetic acid, indolelactic acid, 3-indole propionic acid, indoxyl sulfate, kynurenine, and tryptophan decreased in the lungs of the NARDS_S neonates. Altered microbiota was significantly related to tryptophan metabolites, with changes in lung microbiota and tryptophan metabolites having better differentiated ability for NARDS diagnosis and grading compared to gut and plasma. Conclusion: Significant changes occurred in the lung-gut microbiota and lung-plasma tryptophan metabolites of NARDS neonates. Alterations in lung microbiota and tryptophan metabolites were better discriminatory for the diagnosis and grading of NARDS.

Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach.

Relevance: The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome's regulatory function is closely associated with the disease's pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Methods: This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters. Results: This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation. Conclusions: Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

DeepMPTB: a vaginal microbiome-based deep neural network as artificial intelligence strategy for efficient preterm birth prediction.

In recent decades, preterm birth (PTB) has become a significant research focus in the healthcare field, as it is a leading cause of neonatal mortality worldwide. Using five independent study cohorts including 1290 vaginal samples from 561 pregnant women who delivered at term (n = 1029) or prematurely (n = 261), we analysed vaginal metagenomics data for precise microbiome structure characterization. Then, a deep neural network (DNN) was trained to predict term birth (TB) and PTB with an accuracy of 84.10% and an area under the receiver operating characteristic curve (AUROC) of 0.875 ± 0.11. During a benchmarking process, we demonstrated that our DL model outperformed seven currently used machine learning algorithms. Finally, our results indicate that overall diversity of the vaginal microbiota should be taken in account to predict PTB and not specific species. This artificial-intelligence based strategy should be highly helpful for clinicians in predicting preterm birth risk, allowing personalized assistance to address various health issues. DeepMPTB is open source and free for academic use. It is licensed under a GNU Affero General Public License 3.0 and is available at https://deepmptb.streamlit.app/ . Source code is available at https://github.com/oschakoory/DeepMPTB and can be easily installed using Docker ( https://www.docker.com/ ).

The area ratio of Modic changes has predictive value for postoperative surgical site infection in lumbar spine surgery: a retrospective study.

BACKGROUND: Increasing evidence suggests an association between Modic changes (MC) and subclinical infection and inflammatory reactions. However, the relationship between preoperative MC and surgical site infection (SSI) has not been fully explored. This study aims to investigate the correlation between MC and SSI. METHODS: A retrospective analysis was conducted on patients (n = 646) who underwent single-level lumbar spine surgery for lower back pain in our hospital between 2018 and 2023. According to the Centers for Disease Control and Prevention (CDC) criteria, the patients were divided into an SSI group (n = 40) and a Non-SSI group (n = 606). Univariate analysis was performed to determine the statistical differences in variables between the two groups, and the variables with significant differences were included in a multivariable logistic regression analysis to identify independent risk factors for SSI. Receiver operating characteristic (ROC) curve analysis was performed on the independent risk factors. RESULTS: The SSI group and the Non-SSI group exhibited significant differences in diabetes prevalence, MC prevalence, Total endplate score (TEPS) and area ratio of MC (P < 0.05). Age, gender, American Society of Anesthesiologists(ASA)score, hypertension, coronary heart disease (CHD), chronic obstructive pulmonary disease (COPD), MC classification, and the location of MC in the endplate showed no significant differences (P > 0.05). Multivariate binary logistic regression analysis was performed on the variables with significant differences, and the results indicated a significant correlation between TEPS (P = 0.009) and the area ratio of MC changes (P = 0.001) with SSI. ROC curve analysis was performed on the TEPS and area ratio of MC changes, and the results showed that the diagnostic value of TEPS (AUC: 0.641; CI: 0.522-0.759) is lower than the area ratio of MC (AUC: 0.722; CI: 0.621-0.824), and the combined diagnosis did not significantly improve the diagnostic value (AUC: 0.747; CI: 0.653-0.842). The area ratio of MC had moderate diagnostic value for SSI (AUC: 0.722; CI: 0.621-0.824), with a cut-off value of 24.62% determined by the Youden index (sensitivity: 69.2%; specificity: 73.1%), and for every 1% increase in the area ratio of MC changes, the risk of SSI in MC patients increased by 10.3% (OR = 1.103; CI: 1.044-1.167). CONCLUSION: The area ratio MC and the TEPS are independent risk factors for SSI after lumbar spine surgery. The predictive value of the area ratio of MC is greater than TEPS, and when the two are combined, the predictive value is not significantly improved. When the rate of MC exceeds 24.62%, caution should be exercised regarding the occurrence of SSI.

Ubiquitinomics revealed disease- and stage-specific patterns relevant for the 3PM approach in human sigmoid colon cancers.

Objective: The patients with sigmoid colorectal cancer commonly show high mortality and poor prognosis. Increasing evidence has demonstrated that the ubiquitinated proteins and ubiquitination-mediated molecular pathways influence the growth and aggressiveness of colorectal cancer. It emphasizes the scientific merits of quantitative ubiquitinomics in human sigmoid colon cancer. We hypothesize that the ubiquitinome and ubiquitination-mediated pathway networks significantly differ in sigmoid colon cancers compared to controls, which offers the promise for in-depth insight into molecular mechanisms, discovery of effective therapeutic targets, and construction of reliable biomarkers in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Methods: The first ubiquitinome analysis was performed with anti-K-ε-GG antibody beads (PTMScan ubiquitin remnant motif [K-ε-GG])-based label-free quantitative proteomics and bioinformatics to identify and quantify ubiquitination profiling between sigmoid colon cancer tissues and para-carcinoma tissues. A total of 100 human sigmoid colon cancer samples that included complete clinical information and the corresponding gene expression data were obtained from The Cancer Genome Atlas (TCGA). Ubiquitination was the main way of protein degradation; the relationships between differentially ubiquitinated proteins (DUPs) and their differently expressed genes (DEGs) and between DUPs and their differentially expressed proteins (DEPs) were analyzed between cancer tissues and control tissues. The overall survival of those DUPs was obtained with Kaplan-Meier method. Results: A total of 1249 ubiquitinated sites within 608 DUPs were identified in human sigmoid colon cancer tissues. KEGG pathway network analysis of these DUPs revealed 35 statistically significant signaling pathways, such as salmonella infection, glycolysis/gluconeogenesis, and ferroptosis. Gene Ontology (GO) analysis of 608 DUPs revealed that protein ubiquitination was involved in 98 biological processes, 64 cellular components, 51 molecule functions, and 26 immune system processes. Protein-protein interaction (PPI) network of 608 DUPs revealed multiple high-combined scores and co-expressed DUPs. The relationship analysis between DUPs and their DEGs found 4 types of relationship models, including DUP-up (increased ubiquitination level) and DEG-up (increased gene expression), DUP-up and DEG-down (decreased gene expression), DUP-down (decreased ubiquitination level) and DEG-up, and DUP-down and DEG-down. The relationship analysis between DUPs and their DEPs found 4 types of relationship models, including DUP-up and DEP-up (increased protein expression), DUP-up and DEP-down (decreased protein expression), DUP-down and DEP-up, and DUP-down and DEP-down. Survival analysis found 46 overall survival-related DUPs in sigmoid colon cancer, and the drug sensitivity of overall survival-related DUPs were identified. Conclusion: The study provided the first differentially ubiquitinated proteomic profiling, ubiquitination-involved signaling pathway network changes, and the relationship models between protein ubiquitination and its gene expression and between protein ubiquitination and its protein expression, in human sigmoid colon cancer. It offers the promise for deep insights into molecular mechanisms of sigmoid colon cancer, and discovery of effective therapeutic targets and biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment in the context of 3P medicine. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00328-2.

Quantitative phosphoproteomics reveals molecular pathway network alterations in human early-stage primary hepatic carcinomas: potential for 3P medical approach.

Objective: Hepatic carcinoma is one of the most common types of malignant tumors in the digestive system, and its biological characteristics determine its high rate of metastasis and recurrence after radical resection, leading to a poor prognosis for patients. Increasing evidence demonstrates that phosphoproteins and phosphorylation-mediated molecular pathways influence the occurrence and development of hepatic carcinoma. It is urgent need to develop early-stage biomarkers for improving diagnosis, therapy, medical service, and prognostic assessment. We hypothesize that phosphoproteome and phosphorylation-mediated signaling pathway networks significantly differ in human early-stage primary hepatic carcinomas relative to control liver tissues, which will identify the key differentially phosphorylated proteins and phosphorylation-mediated signaling pathway network alterations in human early-stage primary hepatic carcinoma to innovate predictive diagnosis, prognostic assessment, and personalized medical services and progress beyond the state of the art in the framework of predictive, preventive, and personalized medicine (PPPM). Methods: Tandem mass tag (TMT)-based quantitative proteomics coupled with TiO2 enrichment of phosphopeptides was used to identify phosphorylation profiling, and bioinformatics was used to analyze the pathways and biological functions of phosphorylation profiling between early-stage hepatic carcinoma tissues and tumor-adjacent normal control tissues. Furthermore, the integrative analysis with transcriptomic data from TCGA database obtained differently expressed genes (DEGs) corresponding to differentially phosphorylated proteins (DPPs) and overall survival (OS)-related DPPs. Results: A total of 1326 phosphopeptides derived from 858 DPPs in human early-stage primary hepatic carcinoma were identified. KEGG pathway network analysis of 858 DPPs revealed 33 statistically significant signaling pathways, including spliceosome, glycolysis/gluconeogenesis, B-cell receptor signaling pathway, HIF-1 signaling pathway, and fatty acid degradation. Gene Ontology (GO) analysis of 858 DPPs revealed that protein phosphorylation was involved in 57 biological processes, 40 cellular components, and 37 molecular functions. Protein-protein interaction (PPI) network constructed multiple high-combined scores and co-expressed DPPs. Integrative analysis of transcriptomic data and DPP data identified 105 overlapped molecules (DPPs; DEGs) between hepatic carcinoma tissues and control tissues and 125 OS-related DPPs. Overlapping Venn plots showed 14 common molecules among datasets of DPPs, DEGs, and OS-related DDPs, including FTCD, NDRG2, CCT2, PECR, SLC23A2, PNPLA7, ANLN, HNRNPM, HJURP, MCM2, STMN1, TCOF1, TOP2A, and SSRP1. The drug sensitivities of OS-related DPPs were identified, including LMOD1, CAV2, UBE2E2, RAPH1, ANXA5, HDLBP, CUEDC1, APBB1IP, VCL, SRSF10, SLC23A2, EPB41L2, ESR1, PLEKHA4, SAFB2, SMARCAD1, VCAN, PSD4, RDH16, NOP56, MEF2C, BAIAP2L2, NAGS, SRSF2, FHOD3, and STMN1. Conclusions: Identification and annotation of phosphoproteomes and phosphorylation-mediated signaling pathways in human early-stage primary hepatic carcinoma tissues provided new directions for tumor prevention and treatment, which (i) helps to enrich phosphorylation functional research and develop new biomarkers; (ii) enriches phosphorylation-mediated signaling pathways to gain a deeper understanding of the underlying mechanisms of early-stage primary hepatic carcinoma; and (iii) develops anti-tumor drugs that facilitate targeted phosphorylated sites. We recommend quantitative phosphoproteomics in early-stage primary hepatic carcinoma, which offers great promise for in-depth insight into the molecular mechanism of early-stage primary hepatic carcinoma, the discovery of effective therapeutic targets/drugs, and the construction of reliable phosphorylation-related biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized medical services in the framework of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00335-3.

Growth hormone proteoformics atlas created to promote predictive, preventive, and personalized approach in overall management of pituitary neuroendocrine tumors.

Human growth hormone (GH) is the indispensable hormone for the maintenance of normal physiological functions of the human body, including the growth, development, metabolism, and even immunoregulation. The GH is synthesized, secreted, and stored by somatotroph cells in adenohypophysis. Abnormal GH is associated with various GH-related diseases, such as acromegaly, dwarfism, diabetes, and cancer. Currently, some studies found there are dozens or even hundreds of GH proteoforms in tissue and serum as well as a series of GH-binding protein (GHBP) proteoforms and GH receptor (GHR) proteoforms were also identified. The structure-function relationship of protein hormone proteoforms is significantly important to reveal their overall physiological and pathophysiological mechanisms. We propose the use of proteoformics to study the relationship between every GH proteoform and different physiological/pathophysiological states to clarify the pathogenic mechanism of GH-related disease such as pituitary neuroendocrine tumor and conduct precise molecular classification to promote predictive preventive personalized medicine (PPPM / 3P medicine). This article reviews GH proteoformics in GH-related disease such as pituitary neuroendocrine tumor, which has the potential role to provide novel insight into pathogenic mechanism, discover novel therapeutic targets, identify effective GH proteoform biomarker for patient stratification, predictive diagnosis, and prognostic assessment, improve therapy method, and further accelerate the development of 3P medicine.

BrcaDx: precise identification of breast cancer from expression data using a minimal set of features.

Background: Breast cancer is the foremost cancer in worldwide incidence, surpassing lung cancer notwithstanding the gender bias. One in four cancer cases among women are attributable to cancers of the breast, which are also the leading cause of death in women. Reliable options for early detection of breast cancer are needed. Methods: Using public-domain datasets, we screened transcriptomic profiles of breast cancer samples, and identified progression-significant linear and ordinal model genes using stage-informed models. We then applied a sequence of machine learning techniques, namely, feature selection, principal components analysis, and k-means clustering, to train a learner to discriminate "cancer" from "normal" based on expression levels of identified biomarkers. Results: Our computational pipeline yielded an optimal set of nine biomarker features for training the learner, namely, NEK2, PKMYT1, MMP11, CPA1, COL10A1, HSD17B13, CA4, MYOC, and LYVE1. Validation of the learned model on an independent test dataset yielded a performance of 99.5% accuracy. Blind validation on an out-of-domain external dataset yielded a balanced accuracy of 95.5%, demonstrating that the model has effectively reduced the dimensionality of the problem, and learnt the solution. The model was rebuilt using the full dataset, and then deployed as a web app for non-profit purposes at: https://apalania.shinyapps.io/brcadx/. To our knowledge, this is the best-performing freely available tool for the high-confidence diagnosis of breast cancer, and represents a promising aid to medical diagnosis.

Performance of a blood-based RNA signature for gemcitabine-based treatment in metastatic pancreatic adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, and chemotherapy is a key treatment for advanced PDAC. Gemcitabine chemotherapy is still an important component of treatment; however, there is no routine biomarker to predict its efficacy. Predictive tests may help clinicians to decide on the best first-line chemotherapy. Methods: This study is a confirmatory study of a blood-based RNA signature, called the GemciTest. This test measures the expression levels of nine genes using real-time polymerase chain reaction (PCR) processes. Clinical validation was carried out, through a discovery and a validation phases, on 336 patients (mean 68.7 years; range, 37-88 years) for whom blood was collected from two prospective cohorts and two tumor biobanks. These cohorts included previously untreated advanced PDAC patients who received either a gemcitabine- or fluoropyrimidine-based regimen. Results: Gemcitabine-based treated patients with a positive GemciTest (22.9%) had a significantly longer progression-free survival (PFS) {5.3 vs. 2.8 months; hazard ratio (HR) =0.53 [95% confidence interval (CI): 0.31-0.92]; P=0.023} and overall survival (OS) [10.4 vs. 4.8 months; HR =0.49 (95% CI: 0.29-0.85); P=0.0091]. On the contrary, fluoropyrimidine-based treated patients showed no significant difference in PFS and OS using this blood signature. Conclusions: The GemciTest demonstrated that a blood-based RNA signature has the potential to aid in personalized therapy for PDAC, leading to better survival rates for patients receiving a gemcitabine-based first-line treatment.

DNA and histone modifications as potent diagnostic and therapeutic targets to advance non-small cell lung cancer management from the perspective of 3P medicine.

Lung cancer has a very high mortality in females and males. Most (~ 85%) of lung cancers are non-small cell lung cancers (NSCLC). When lung cancer is diagnosed, most of them have either local or distant metastasis, with a poor prognosis. In order to achieve better outcomes, it is imperative to identify the molecular signature based on genetic and epigenetic variations for different NSCLC subgroups. We hypothesize that DNA and histone modifications play significant roles in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Epigenetics has a significant impact on tumorigenicity, tumor heterogeneity, and tumor resistance to chemotherapy, targeted therapy, and immunotherapy. An increasing interest is that epigenomic regulation is recognized as a potential treatment option for NSCLC. Most attention has been paid to the epigenetic alteration patterns of DNA and histones. This article aims to review the roles DNA and histone modifications play in tumorigenesis, early detection and diagnosis, and advancements and therapies of NSCLC, and also explore the connection between DNA and histone modifications and PPPM, which may provide an important contribution to improve the prognosis of NSCLC. We found that the success of targeting DNA and histone modifications is limited in the clinic, and how to combine the therapies to improve patient outcomes is necessary in further studies, especially for predictive diagnostics, targeted prevention, and personalization of medical services in the 3P medicine approach. It is concluded that DNA and histone modifications are potent diagnostic and therapeutic targets to advance non-small cell lung cancer management from the perspective of 3P medicine.

A Nomogram for Predicting the Risk of Critical Limb Ischemia in Adults with Hypertension: A Retrospective Study.

Purpose: Peripheral arterial disease (PAD) presenting with underlying hypertension (HTN) poses a higher risk of bilateral lower limb amputation than PAD patients without HTN. While the role of HTN management of PAD patients has received limited attention. We analyzed the clinical characteristics of PAD in adults with HTN and explored risk factors for PAD to construct a nomogram for evaluating critical limb ischemia (CLI) and lesion severity. Methods Patients and Methods: Between January 2014 and December 2019, we retrospectively evaluated 1886 patients with peripheral artery disease with coexisting HTN. Patients were randomly divided into training (n = 1320, 70%) and validation cohorts (n = 566, 30%), and according to the subjective experience of PAD [Fontaine classification (I-II vs III-IV)], patients were further classified into intermittent claudication (IC) and CLI groups. LASSO regression and multivariate Cox proportional hazard analyses were used to construct a nomogram using variables defined in the training cohort, which was validated in the validation cohort. The evaluation of the predictive discriminative, accuracy and clinical application are further analyzed. Results: In the training cohort, optimal independent factors included age, male sex, body mass index, diabetes mellitus, heart rate, triglyceride, and uric acid (AM-BDHTU), which were included in the nomogram predicting the CLI risk (all P < 0.05). The C-index values for CLI risk in PAD with HTN patients were 0.729 (95% CI: 0.704-0.807) and 0.728 (95% CI: 0.652-0.744) in the training and validation sets, respectively. Calibration curves indicated good consistency between predicted and actual outcomes. DCA confirmed the clinical utility of the diagnostic model. Conclusion: The AM-BDHTU nomogram, constructed and validated using simple to obtain clinical variables, when combined with the Fontaine classification, effectively predicts the risk of CLI among PAD patients with HTN.

Quantification of Tumor Abnormal Proteins in the Diagnosis and Postoperative Prognostic Evaluation of Gastric Cancer.

Background: Abnormal glycosylation of proteins has been identified in almost all types of cancers and is closely related to the cancer progression, metastasis, and survival of cancer patients. This study was to explore the values of serum tumor abnormal protein (TAP), an abnormal glycochain protein, in the diagnosis and prognosis of gastric cancer (GC). Methods: A total of 335 GC patients were included as the study group, and another 335 subjects served as the control group. Tumor abnormal protein expression was compared between the 2 groups. Correlation analysis was used to assess the correlations of TAP with clinicopathological factors. Gastric cancer patients were divided into training set and test set at a ratio of 2:1. Univariate and multivariate Cox regression analyses in training set were used to evaluate the prognostic significance of TAP in GC patients and explore the independent risk factors for overall survival (OS) and disease-free survival (DFS) to establish a prognostic model, followed by testing of the model. According to the median of TAP, 335 GC patients were divided into 2 groups to plot the survival curves of OS and DFS. Results: Tumor abnormal protein expression in the study group was significantly higher than in the control group. Taking the best cut-off value of TAP (110.128 µm2) as the diagnostic criteria for GC, the sensitivity and specificity of TAP were 83.58% and 97.61%, respectively, and the area under the receiver operating characteristics (ROC) curve was 0.935, which was not inferior to computed tomography (CT). Tumor abnormal protein expression was an independent risk factor for OS and DFS. The prognostic predictive value of TAP was better than that of pathological stage in GC patients. The model with TAP was effective in predicting prognosis. Conclusion: Tumor abnormal protein is an effective indicator for early screening and prognostic evaluation of GC and can also assist the clinical diagnosis and treatment of GC.

Tangled quest of post-COVID-19 infection-caused neuropathology and what 3P nano-bio-medicine can solve?

COVID-19-caused neurological problems are the important post-CoV-2 infection complications, which are recorded in ~ 40% of critically ill COVID-19 patients. Neurodegeneration (ND) is one of the most serious complications. It is necessary to understand its molecular mechanism(s), define research gaps to direct research to, hopefully, design new treatment modalities, for predictive diagnosis, patient stratification, targeted prevention, prognostic assessment, and personalized medical services for this type of complication. Individualized nano-bio-medicine combines nano-medicine (NM) with clinical and molecular biomarkers based on omics data to improve during- and post-illness management or post-infection prognosis, in addition to personalized dosage profiling and drug selection for maximum treatment efficacy, safety with least side-effects. This review will enumerate proteins, receptors, and enzymes involved in CoV-2 entrance into the central nervous system (CNS) via the blood-brain barrier (BBB), and list the repercussions after that entry, ranging from neuroinflammation to neurological symptoms disruption mechanism. Moreover, molecular mechanisms that mediate the host effect or viral detrimental effect on the host are discussed here, including autophagy, non-coding RNAs, inflammasome, and other molecular mechanisms of CoV-2 infection neuro-affection that are defined here as hallmarks of neuropathology related to COVID-19 infection. Thus, a couple of questions are raised; for example, "What are the hallmarks of neurodegeneration during COVID-19 infection?" and "Are epigenetics promising solution against post-COVID-19 neurodegeneration?" In addition, nano-formulas might be a better novel treatment for COVID-19 neurological complications, which raises one more question, "What are the challenges of nano-bio-based nanocarriers pre- or post-COVID-19 infection?" especially in the light of omics-based changes/challenges, research, and clinical practice in the framework of predictive preventive personalized medicine (PPPM / 3P medicine).

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

Objective: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians. Methods: The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with "jieba" word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information. Results: The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively. Conclusion: Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

Muti-omics integration analysis revealed molecular network alterations in human nonfunctional pituitary neuroendocrine tumors in the framework of 3P medicine.

Nonfuctional pituitary neuroendocrine tumor (NF-PitNET) is highly heterogeneous and generally considered a common intracranial tumor. A series of molecules are involved in NF-PitNET pathogenesis that alter in multiple levels of genome, transcriptome, proteome, and metabolome, and those molecules mutually interact to form dynamically associated molecular-network systems. This article reviewed signaling pathway alterations in NF-PitNET based on the analyses of the genome, transcriptome, proteome, and metabolome, and emphasized signaling pathway network alterations based on the integrative omics, including calcium signaling pathway, cGMP-PKG signaling pathway, mTOR signaling pathway, PI3K/AKT signaling pathway, MAPK (mitogen-activated protein kinase) signaling pathway, oxidative stress response, mitochondrial dysfunction, and cell cycle dysregulation, and those signaling pathway networks are important for NF-PitNET formation and progression. Especially, this review article emphasized the altered signaling pathways and their key molecules related to NF-PitNET invasiveness and aggressiveness that are challenging clinical problems. Furthermore, the currently used medication and potential therapeutic agents that target these important signaling pathway networks are also summarized. These signaling pathway network changes offer important resources for insights into molecular mechanisms, discovery of effective biomarkers, and therapeutic targets for patient stratification, predictive diagnosis, prognostic assessment, and targeted therapy of NF-PitNET.

Carotenoids in Cancer Metastasis-Status Quo and Outlook.

Metastasis represents a major obstacle in cancer treatment and the leading cause of cancer-related deaths. Therefore, the identification of compounds targeting the multi-step and complex process of metastasis could improve outcomes in the management of cancer patients. Carotenoids are naturally occurring pigments with a plethora of biological activities. Carotenoids exert a potent anti-cancer capacity in various cancer models in vitro and in vivo, mediated by the modulation of signaling pathways involved in the migration and invasion of cancer cells and metastatic progression, including key regulators of the epithelial-mesenchymal transition and regulatory molecules, such as matrix metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), urokinase plasminogen activator (uPA) and its receptor (uPAR), hypoxia-inducible factor-1α (HIF-1α), and others. Moreover, carotenoids modulate the expression of genes associated with cancer progression and inflammatory processes as key mediators of the complex process involved in metastasis. Nevertheless, due to the predominantly preclinical nature of the known anti-tumor effects of carotenoids, and unclear results from certain carotenoids in specific cancer types and/or specific parts of the population, a precise analysis of the anti-cancer effects of carotenoids is essential. The identification of carotenoids as effective compounds targeting the complex process of cancer progression could improve the outcomes of advanced cancer patients.

Predictive Values of Blood-Based RNA Signatures for the Gemcitabine Response in Advanced Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second cause of cancer death by 2022. For nearly 80% of patients, diagnosis occurs at an advanced, nonsurgical stage, making such patients incurable. Gemcitabine is still an important component in PDAC treatment and is most often used as a backbone to test new targeted therapies and there is, to date, no routine biomarker to predict its efficacy. Samples from a phase III randomized trial were used to develop through a large approach based on blood-based liquid biopsy, transcriptome profiling, and machine learning, a nine gene predictive signature for gemcitabine sensitivity. Patients with a positive test (41.6%) had a significantly longer progression free survival (PFS) (3.8 months vs. 1.9 months p = 0.03) and a longer overall survival (OS) (14.5 months vs. 5.1, p < 0.0001). In multivariate analyses, this signature was independently associated with PFS (HR = 0.5 (0.28-0.9) p = 0.025) and OS (HR = 0.39 (0.21-0.7) p = 0.002).

Intestinal alkaline phosphatase modulation by food components: predictive, preventive, and personalized strategies for novel treatment options in chronic kidney disease.

Alkaline phosphatase (AP) is a ubiquitous membrane-bound glycoprotein that catalyzes phosphate monoesters' hydrolysis from organic compounds, an essential process in cell signaling. Four AP isozymes have been described in humans, placental AP, germ cell AP, tissue nonspecific AP, and intestinal AP (IAP). IAP plays a crucial role in gut microbial homeostasis, nutrient uptake, and local and systemic inflammation, and its dysfunction is associated with persistent inflammatory disorders. AP is a strong predictor of mortality in the general population and patients with cardiovascular and chronic kidney disease (CKD). However, little is known about IAP modulation and its possible consequences in CKD, a disease characterized by gut microbiota imbalance and persistent low-grade inflammation. Mitigating inflammation and dysbiosis can prevent cardiovascular complications in patients with CKD, and monitoring factors such as IAP can be useful for predicting those complications. Here, we review IAP's role and the results of nutritional interventions targeting IAP in experimental models to prevent alterations in the gut microbiota, which could be a possible target of predictive, preventive, personalized medicine (PPPM) to avoid CKD complications. Microbiota and some nutrients may activate IAP, which seems to have a beneficial impact on health; however, data on CKD remains scarce.

Sensitivity and specificity of combination of Hull airway reflux questionnaire and gastroesophageal reflux disease questionnaire in identifying patients with gastroesophageal reflux-induced chronic cough.

BACKGROUND: There is a need to find a simple, non-invasive and effective diagnostic tool for diagnosing gastroesophageal reflux-induced chronic cough (GERC) in clinic. This study aimed to evaluate the predictive diagnostic value of Hull airway reflux questionnaire (HARQ) and its combination with gastroesophageal reflux disease questionnaire (GerdQ) for GERC. METHODS: Chronic cough patients were enrolled and the diagnosis of GERC was established according to the chronic cough diagnosis and treatment process. The diagnostic value of HARQ and GerdQ alone or the combination of HARQ and GerdQ was analyzed. RESULTS: A total of 402 patients with chronic cough were eventually enrolled, including 166 GERC patients. When the HARQ score was used to predict the diagnosis of GERC, the area under the ROC curve was 0.796. The sensitivity and specificity were 77.19% and 77.06%, respectively. When the GerdQ was used to predict the diagnosis of GERC, the area under the ROC curve was 0.763. The sensitivity and specificity were 70.18% and 76.15%, respectively. When HARQ combined with GerdQ were used to predict the diagnosis of GERC, the area under the ROC curve was 0.848. The sensitivity and specificity were 77.19% and 79.82%, respectively. CONCLUSIONS: HARQ used to evaluate the cough hypersensitivity has a certain predictive diagnostic value for GERC. The diagnosis of GERC should be considered when the HARQ score is ≥24. The predictive diagnostic value of the combination of HARQ and GerdQ is significantly higher, which makes the diagnosis of GERC simpler, quicker and more effective.

Skin Fibroblasts as the Object for Clinical Diagnosis of Parkinson's Disease in Persons of Different Ages.

We compared the expression of Aß42 peptide, τ-protein, and α-synuclein in the substantia nigra and skin fibroblasts of elderly and senile patients with Parkinson's disease and subjects without neuropathology. Expression of markers in the studied tissues was assessed by immunohistochemical and immunocytochemical methods. The expression of Aß42 peptide, τ-protein, and α-synuclein in the substantia nigra of elderly and senile patients with Parkinson's disease was higher by 11-31 times than in subjects without neuropathology. In skin fibroblasts of patients with Parkinson's disease, the expression of Aß42 peptide and α-synuclein was 3-14 times higher than in subjects without neuropathology, and expression of τ-protein did not significantly differ in the studied groups. Thus, immunocytochemical analysis of the expression Aß42 peptide and α-synuclein in skin fibroblasts can be a simple method of early diagnosis of Parkinson's disease in elderly persons.