Voice Changes of Patients With Nasopharyngeal Carcinoma in Eleven Years After Radiotherapy: A Cross-Sectional Study.

OBJECTIVES: The objective of this study was to assess voice changes in patients with nasopharyngeal carcinoma (NPC) using subjective and objective assessment tools and to make inferences regarding the underlying pathological causes for different phases of radiotherapy (RT). METHODS: A total of 187 (123 males and 64 females) patients with post-RT NPC with no recurrence of malignancy or other voice diseases and 17 (11 males and 6 females) healthy individuals were included in this study. The patients were equally divided into 11 groups according to the number of years after RT. The acoustic analyses, GRBAS (grade, roughness, breathiness, asthenia, and strain) scales, and Voice Handicap Index (VHI)-10 scores were collected and analyzed. RESULTS: The fundamental frequency (F0) parameters in years 1 and 2 and year 11 were significantly lower in patients with NPC than in healthy individuals. The maximum phonation times in years 1 and 11 were significantly shorter than those in healthy individuals. The jitter parameters were significantly different between year 1 and from years 8 to 11 and the healthy individuals. The shimmer parameters were significantly different between years 1, from years 9 to 11, and healthy individuals. Hoarseness was the most prominent problem compared to other items of the GRBAS. The VHI-10 scores were significantly different between years 1 and 2 and year 11 after RT in patients with NPC. CONCLUSIONS: Voice quality was worse in the first 2 years and from years 8 to 11 but remained relatively normal from years 3 to 7 after RT. Patient-reported voice handicaps began during year 3 after RT. The most prominent problem was perceived hoarseness, which was evident in the first 2 years and from years 9 to 11 after RT. The radiation-induced mucous edema, laryngeal intrinsic muscle fibrosis, nerve injuries, upper respiratory tract changes, and decreased lung capacity might be the pathological reasons for voice changes in post-RT patients with NPC.

NPM1 promotes nasopharyngeal carcinoma metastasis and stemness by recruiting Mdm2 to induce the ubiquitination-mediated degradation of p53.

Nasopharyngeal carcinoma (NPC) is clinically challenging due to the development of distant metastasis following initial therapy. Therefore, it is necessary to elucidate the mechanisms underlying metastases to develop novel therapeutic strategies. Nucleophosmin 1 (NPM1) has been directly linked to the development of human tumors and may have both tumor-suppressing and oncogenic properties. Although NPM1 is often overexpressed in solid tumors of various histopathological origins, its specific function in mediating the development of NPC is still unknown. Here, we investigated the role of NPM1 in NPC and discovered that NPM1 was elevated in clinical NPC samples and served as a predictor of the worst prognosis in NPC patients. Furthermore, the upregulation of NPM1 promoted the migration and the cancer stemness of NPC both in vitro and in vivo. Mechanistic analyses revealed that the E3 ubiquitin ligase Mdm2 was recruited by NPM1 to induce the ubiquitination-mediated proteasomal degradation of p53. Ultimately, knockdown of NPM1 suppressed the stemness and EMT signals. In summation, this study demonstrated the role and the underlying molecular mechanism of NPM1 in NPC, providing the evidence for the clinical application of NPM1 as a therapeutic target for the treatment of patients with NPC.

Predictive value of machine learning in diagnosing cognitive impairment in patients with Parkinson's disease: a systematic review and meta-analysis.

BACKGROUND: The current process used to diagnose cognitive impairment in patients with Parkinson's disease (PD) is unsatisfactory. More and more researchers had introduced machine learning into this field in recent years. This study explored the application of machine learning and its diagnostic performance in this field. METHODS: Since Parkinson's concurrent cognitive impairment is currently divided into different periods, most studies focus on the prodromal or early stages of Parkinson's cognitive impairment, and a few focuses on the dementia stage of Parkinson's. To ensure comprehensiveness, and model stability, we included patients with Parkinson's concurrent cognitive impairment in different periods who met the nadir criteria. A comprehensive literature search was carried out of the PubMed, Cochrane, Embase, and Web of Science databases. We used Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias for the machine learning models covered by the included original studies. The outcome indicators included the concordance-index (C-index), sensitivity, and specificity. A meta-analysis using the random-effects model was conducted to determine the C-index, and a double variable mixed-effects model was used to determine the sensitivity and specificity. The meta-analysis in this article was completed in STATA. RESULTS: A total of 32 articles, comprising 10,778 patients and 51 prognostic models [summary c-statistic: 0.857, 95% confidence interval (CI) (0.842-0.873)], met the selection criteria and were included in this analysis. The total sensitivity and specificity of all models were 0.77 (95% CI: 0.72-0.81) and 0.83 (95% CI: 0.80-0.85), respectively, and those of the testing test were 0.85 (95% CI: 0.79-0.89), and 0.74 (95% CI: 0.70-0.78), respectively. A large part of the model showed a high risk of bias mainly because the study design was almost retrospective investigation. CONCLUSIONS: This study constitutes a detailed mapping and assessment of the machine learning for prediction in PD patients with cognitive decline, which may provide stronger discriminative performance and can be used as a potential tool for early diagnosis.

Identification of genes and pathways related with cerebral small vessel disease based on a long non-coding RNA-mediated, competitive endogenous RNA network.

This study aimed to investigate the cerebral small vessel disease (SVD), an intrinsic disorder of the brain's perforating arterioles, which could cause serious cognitive decreasing and trigger dementia. In this study, we present a multi-step computational approach to construct a functional SVD long non-coding RNA (lncRNA)-mediated ceRNA network (LMCN) by integrating genome-wide lncRNA and mRNA expression profiles, miRNA-target interactions, and functional analyses. We used hypergeometric test to evaluate enrichment significance of miRNAs and we obtained the LMCN, which contained 27 lncRNAs, 7,229 mRNA, and 28,871 lncRNAs-mRNA interrelationship pairs. What's more, co-expression analysis was utilized to constructe a competitive endogenous RNAs (ceRNAs) interaction network which comprised of 21 lncRNAs, 129 mRNAs and 141 interaction pairs. We determined that metastasis-associated lung adenocarcinoma transcript 1 and MIR155 host gene acted synergistically to regulate mRNAs in a network module of the competitive LMCN. Moreover, 7 genes were obtained through Gene Ontology enrichment. In addition, 29 mRNA enriched pathways significantly associated with lncRNAs was obtained via Fisher test. In conclusion, we identified 7 potential lncRNAs and 29 possible lncRNA-mediated pathways associated with SVD. Thus, ceRNAs could accelerate biomarker discovery and therapeutic development in SVD.