Over the past decades, cervical cancer has been a worldwide public health problem. Population-based early cancer risk detection and prevention approaches, including vaccination, cytology screening and human papilloma virus (HPV) detection, with the aligned clinical management, have formed a well-rounded high-quality implementation system for cervical cancer control, and revolutionarily improved the quality of life of women: (1) the success of cervical cancer screening practices, (2) standardization of The Bethesda system for reporting cervicovaginal cytology, (3) improvement in the understanding of HPV pathogenesis in cervical cancer, and (4) the development of appropriate management approaches have significantly decreased the disease burden of cervical cancer worldwide. This scoping review aimed to understand the evolvement of cervical cancer screening and management guidelines, describe the Bethesda cervical cytology reporting system, and HPV vaccines and tests, and highlight the key information of present policies and practices.

Purpose: The neutrophil-to-lymphocyte ratio (NLR) is a useful prognostic marker for various diseases and surgery-induced immunosuppression. While opioids are important in general anesthesia, the association between immediate perioperative immune monitoring and opioid consumption for postoperative analgesia after video-assisted thoracoscopic surgery (VATS) is unknown. We aimed to investigate the effect of analgesic techniques on opioid-induced immune perturbation, and the feasibility of NLR as an indicator of opioid-induced immune changes. Patients and Methods: Patients were randomly assigned to two groups: Group P (n=40) or Group C (n=40). Patients in group P received ultrasound-guided paravertebral block (PVB) before surgery, and followed by sufentanil patient-controlled intravenous analgesia (PCIA) after surgery, and group C received sufentanil PCIA only. The total and differential white blood cell counts, including CD4+ T lymphocyte counts, CD8+ T lymphocyte were recorded before surgery and at 24 and 72 hours after surgery. NLR was determined using the frequencies of lymphocyte subpopulations. The cumulative dose of sufentanil were recorded at 24 and 24h after surgery while the 40-item quality of recovery questionnaire (QoR-40) score were assessed at 48h after the surgery. Results: At 24 and 48 hours after surgery, a lower sufentanil consumption, and higher QoR-40 recovery scores were found in group P than in group C (P< 0.05). In biochemical analyses, the values of NLR were lower in group P compared to group C (p< 0.0001) and ratio of CD4/CD8 were higher in group P compared to group C (p< 0.05) on day three after surgery. NLR showed excellent predictive capability for immunosuppression, with an area under the curve (AUC) of 0.92 [95% confidence interval (CI), 0.86– 0.98, P < 0.0001]. Conclusion: Opioid-sparing pain management strategies may affect postoperative immunosuppression and NLR could be a reliable indicator of opioid-related immunosuppression. Moreover, opioid-sparing pain management strategies could improve patient’s satisfaction in VATS.

Gene expression network is also a type of complex network. It is challenging to analyze the gene expression network through relevant knowledge and algorithms of a complex network. In this paper, the existing characteristics of genes are analyzed from various indexes of the gene expression network to analyze key genes and TOP genes. Firstly, gene chip data are screened, gene data with obvious characteristics are selected, and relevant clustering characteristics are analyzed. Then, the complex gene network structure is established, and gene networks with different threshold shapes and different sizes are selected. Finally, the relevant indexes and PR values after the PageRank algorithm are analyzed for complex networks under different thresholds, thus establishing the TOP gene and PR sequence.

In the detection of genome variation, the research on the internal correlation of reference genome is deepening; the detection of variation in genome sequence has become the focus of research, and it has also become an effective path to find new genes and new functional proteins. The targeted sequencing sequence is used to sequence the exon region of a specific gene in cancer gene detection, and the sequencing depth is relatively large. Traditional alignment algorithms will lose some sequences, which will lead to inaccurate mutation detection. This paper proposes a mutation detection algorithm based on feedback fast learning neural network position index. By establishing a position index relationship for ACGT in the DNA sequence, the subsequence is decomposed into the position relationship of different subsequences corresponding to the main sequence. The positional relationship of the subsequence in the main sequence is determined by the positional relationship. Analyzing SNP and InDel mutations, even structural mutations, through the position correlation of sequences has the advantages of high precision and easy implementation by personal computers. The feedback fast learning neural network is used to verify whether there is a linear relationship between two or more positions. Experimental results show that the mutation points detected by position index are more than those detected by Bcftools, Freebye, Vanscan2, and Gatk.

Cardiovascular disease (CVD) is influenced by metabolic dysfunction, chronic inflammation, and adiposity. The C-reactive protein-triglyceride-glucose index (CTI) integrates inflammatory and metabolic information, but the associations of CTI-derived adiposity indices with incident CVD remain insufficiently characterized. We aimed to examine the associations of several CTI-derived adiposity indices with incident CVD and compare their relative performance in a nationwide cohort of middle-aged and older adults. We analyzed data from 6,161 participants aged ≥ 45 years without baseline CVD from the China Health and Retirement Longitudinal Study (CHARLS). Four CTI-derived adiposity indices were evaluated: CTI-WHtR, CTI-BMI, CTI-BRI, and CTI-WWI. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVD. Dose-response relationships were assessed using restricted cubic splines (RCS). Kaplan-Meier, discrimination and reclassification, cumulative exposure, trajectory, subgroup, component-outcome, mediation, and sensitivity analyses were also performed. During a median follow-up of 9 years, 1,503 participants (24.39%) developed CVD. Higher levels of CTI-derived adiposity indices were associated with increased CVD risk. In fully adjusted models, participants in the highest quartile had higher CVD risk than those in the lowest quartile for CTI-WHtR (HR = 1.57, 95% CI 1.24-2.00), CTI-BMI (HR = 1.55, 95% CI 1.23-1.97), CTI-BRI (HR = 1.40, 95% CI 1.11-1.78), and CTI-WWI (HR = 1.37, 95% CI 1.08-1.74), with significant trends across quartiles. RCS analyses showed predominantly linear dose-response relationships. Discrimination was modest, with AUCs ranging from 0.558 for CTI-WWI to 0.573 for CTI-WHtR, compared with 0.522 for CTI. All CTI-derived indices improved continuous net reclassification improvement and integrated discrimination improvement beyond the conventional risk factor model. Higher cumulative exposure and higher trajectory clusters of CTI-derived indices were also associated with increased CVD risk. Mediation analyses suggested interrelationships between metabolic-inflammatory dysregulation and adiposity in relation to CVD. CTI-derived adiposity indices were associated with incident CVD in middle-aged and older adults, with CTI-WHtR showing the most consistent overall performance among the evaluated indices. These findings support the epidemiological relevance of integrating metabolic-inflammatory burden with adiposity-related phenotypes in cardiovascular risk assessment.