Notes

Systems-level biomarkers identification as well as drug repositioning throughout colorectal cancer.
is may have implications for future clinical decision-making, including the development of finer and more sensitive examinations.
Hepatocellular carcinoma (HCC) is a malignant tumor with a poor prognosis, however, biomarkers for the prognostic assessment of HCC remain suboptimal. Consequently, we aimed to develop a reliable tool for prognostic estimation of HCC.

Differentially expressed genes (DEGs) between HCC and adjacent normal tissues in 3 Gene Expression Omnibus (GEO) datasets were identified, followed by hub gene selection and least absolute shrinkage and selection operator (LASSO) Cox regression to develop a prognostic gene signature. Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent area under the curve (AUC), and integrated value of time-dependent AUC (iAUC) were used to assess the relationship between predictors and clinical outcomes in the training and validation datasets. Then we built nomograms including gene signature and clinicopathological factors to forecast the probability of death. Moreover, we performed quantitative real-time PCR (qPCR) to compare the expression of prognoss had elevated expression of immune checkpoint genes, indicating that these patients may be more suitable for immunotherapy.

We have established and validated an eight-gene based prognostic model, which could be an effective tool for the prognostic evaluation of HCC patients.
We have established and validated an eight-gene based prognostic model, which could be an effective tool for the prognostic evaluation of HCC patients.
Early and accurate diagnosis of invasive fungal infection (IFI) is pivotal for the initiation of effective antifungal therapy for patients with hematologic malignancies.

This retrospective study involved 235 patients with hematologic malignancies and pulmonary infections diagnosed as IFIs (n=118) or bacterial pneumonia (n=117). Patients were randomly divided into training (n=188) and validation (n=47) datasets. Four feature selection methods with nine classifiers were implemented to select the optimal machine learning (ML) model using five-fold cross-validation. A radiomic signature was constructed using a linear ML algorithm, and a radiomic score (Radscore) was calculated. The combined model was developed with the Radscore, the significant clinical and radiologic factors were selected using multivariable logistic regression, and the results were presented as a clinical radiomic nomogram. A prospective pilot study was also conducted to compare the classification performance of the combined nomogram with practicing radiologists.

Significant differences were found in the Radscore between IFI and bacterial pneumonia patients in the training (0.683
-0.724, P<0.001) and validation set (0.353
-0.717, P=0.002). The combined model showed good discrimination performance in the validation cohort [area under the curve (AUC) =0.844] and outperformed the clinical (AUC =0.696) and radiomics (AUC =0.767) model alone (both P<0.05).

The clinical radiomic nomogram can serve as a promising predictive tool for IFI in patients with hematologic malignancies.
The clinical radiomic nomogram can serve as a promising predictive tool for IFI in patients with hematologic malignancies.
Muscle atrophy caused by peripheral nerve injury is a common clinical disease, with no effective treatments currently available. Our previous studies have found that denervation-induced muscle atrophy can be alleviated by inhibiting histone deacetylase 4 (HDAC4). An increasing amount of evidence shows that microRNA (miRNA) and long noncoding RNA (lncRNA) are involved in the occurrence of muscle atrophy. This study aimed to find the mechanism by which HDAC4 regulates denervation-induced muscle atrophy based on lncRNA-associated competing endogenous RNA (ceRNA) networks.

We analyzed the influence of short hairpin RNA (shRNA) knockdown of HDAC4 on lncRNAs and miRNAs after denervated muscle atrophy using RNA sequencing. A Pearson's correlation heat map and principal component analysis were employed to analyze differentially expressed miRNAs and lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of target genes were conducted. The ceRNA network of lncRNA-miRNA-mRNA was constle atrophy caused by peripheral nerve injury. Bimiralisib inhibitor XR_377582.2 and ENSMUST00000143649 may be the key lncRNAs related to HDAC4 involved in the regulation of muscle atrophy.
In the ceRNA network, all nodes are directly or indirectly involved in the process by which HDAC4 regulates skeletal muscle atrophy caused by peripheral nerve injury. XR_377582.2 and ENSMUST00000143649 may be the key lncRNAs related to HDAC4 involved in the regulation of muscle atrophy.
The proximal tubule is the sensing site of sodium and phosphate and the main place for the synthesis and metabolism of 1,25(OH)
D
. We aimed to investigate the effects of high sodium on the synthesis and function of active vitamin D and local phosphate regulation in proximal tubular epithelial cells.

Human proximal tubule epithelial (HK-2) cells were treated with different concentrations of sodium/phosphate. The expression of 1α-OHase and 24-OHase was determined. Liquid chromatography/mass spectrometry (LC/MS) and enzyme-linked immunosorbent assay (ELISA) were used to detect the levels of 1,25(OH)
D
RNA sequencing and bioinformatics analysis was used to probe into the possible pathways. Chromatin samples were immunoprecipitated with antibodies against parathyroid receptor 1 (PTH1R) and Klotho.

We found that high sodium decreased the expression of 1,25(OH)
D
by reducing 1α-OHase and 24-OHase, reduced the expression of PTH1R and Klotho, and increased the intracellular calcium concentration. These bolism.
Salvage surgery for hypopharyngeal squamous cell carcinoma (HPSCC) after radiotherapy may result in several postoperative complications and the oncological outcome is unsatisfying. Therefore, identifying the risk factors for postoperative complications and oncological outcome after salvage surgery is important. This study aimed to determine which HPSCC patients might benefit from salvage surgery following previous radiotherapy.

We retrospectively analyzed 91 HPSCC patients who underwent salvage surgery due to locoregional recurrence/residual disease after radiotherapy. The pre- and intraoperative characteristics with complications and oncological outcomes were collected through medical records and telephone follow-up. Risk factors for complications were analyzed by binary logistic regression. The oncological outcomes were assessed by overall survival (OS) after salvage surgery. Kaplan-Meier curves and Cox proportional hazard regression analysis were used for univariate and multivariate survival analyses.
.450, P=0.007) were identified as risk factors for OS. The 5-year OS rates of patients without and with both risk factors were 43% and 10% (P=0.001).

Salvage surgery for locoregional recurrence/residual disease after previous radiotherapy could improve survival in selected patients with HPSCC. Patients with local recurrence/residual disease had a higher complication rate. Efforts can be made to shorten the time of curative treatment initiation and treat anemia to reduce the risk of postoperative complications in this subgroup.
Salvage surgery for locoregional recurrence/residual disease after previous radiotherapy could improve survival in selected patients with HPSCC. Patients with local recurrence/residual disease had a higher complication rate. Efforts can be made to shorten the time of curative treatment initiation and treat anemia to reduce the risk of postoperative complications in this subgroup.
The aim of this narrative review is to analyze whether or not artificial intelligence (AI) and its subsets are implemented in current clinical anesthetic practice, and to describe the current state of the research in the field. AI is a general term which refers to all the techniques that enable computers to mimic human intelligence. AI is based on algorithms that gives machines the ability to reason and perform functions such as problem-solving, object and word recognition, inference of world states, and decision-making. It includes machine learning (ML) and deep learning (DL).

We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases. The research string comprised various combinations of "artificial intelligence", "machine learning", "anesthesia", "anesthesiology". The databases were searched independently by two authors. A third reviewer would mediate any disagreement the results of the two screeners.

The application of AI has shown excellent results in both anesthesia created, provided they have excellent performance, have not yet entered daily practice. Clinical impact analyzes and external validations are needed before this happens. Therefore, qualitative research will be needed to better understand the ethical, cultural, and societal implications of integrating AI into clinical workflows.
(
),
,
(
), and
(
) are collectively known as "the Top Four Medical Journals (TFMJ)" in China. Through the analysis of Chinese scholars' publications in the TFMJ in the recent 10 years, this study aimed to clarify the current situation of high-quality medical research conducted by Chinese scholars and institutions.

Data were retrieved and downloaded manually from PubMed (2011-2020). Information on the publication year, journal, author, affiliation, and citation, etc. were extracted and analyzed using R software.

A total of 761 articles were involved in the final analysis. The number of articles published by Chinese scholars in the TFMJ was 135/29,942 (0.45%) in
, 124/14,033 (0.88%) in
, 314/16,117 (1.94%) in
, and 188/15,242 (1.23%) in
(P<0.001). Besides, the letter was the main research type, which was up to 44.54%, and the original research only accounted for 17.47%. The most popular subspecialty and subject were infectious diseases and COVID-19, respectively. The most productive researcher was Chen Wang, and Bin Cao was the most cited Chinese scholar. The most productive institute was Chinese Academy of Medical Sciences and Peking Union Medical College. The most cited study was "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China".

The presence of Chinese scholars in the TFMJ has grown, but there is still much room to improve. A Matthew effect in China's high-level scientific research was demonstrated.
The presence of Chinese scholars in the TFMJ has grown, but there is still much room to improve. A Matthew effect in China's high-level scientific research was demonstrated.
Pelvic organ prolapse (POP) is a common degenerative disease in women which may diminish quality of life. Investigating the pathological changes of the uterosacral ligament, including the functional changes of fibroblasts, is critical to understanding the pathophysiology of POP. This study was designed to isolate CD106-positive (CD106
) fibroblasts from the human uterosacral ligament and assess the function and expression of this subpopulation.

We separated CD106
fibroblasts and CD106 negative (CD106
) fibroblasts by fluorescence-activated cell sorting (FACS) and cultured them for subsequent experiments. Flow cytometric analysis was used to test the sorting efficiency, CD106 expression, and typical mesenchymal stem cell (MSC) phenotype marker expression. A colony-forming unit (CFU) assay was applied to evaluate the colony-forming ability of the fibroblasts. Trilineage differentiation capacities were assessed after
induction. The protein levels of vimentin, fibroblast specific protein-1 (FSP-1), collagen I (COL 1), matrix metallopeptidase-1 (MMP-1), and α-smooth muscle actin (α-SMA) were detected by western blot analysis.
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