Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study.

Background: There are few research findings on the survival prognosis of spindle cell melanoma (SCM), which is an unusual kind of melanoma. The purpose of this study was to develop a thorough nomogram for predicting the overall survival (OS) of patients with SCM and to assess its validity by comparing it with the conventional American Joint Committee on Cancer (AJCC) staging system. Methods: The Surveillance, Epidemiology, and End Results database was searched, and 2,015 patients with SCM were selected for the analysis. The patients were randomly divided into training (n = 1,410) and validation (n = 605) cohorts by using R software. Multivariate Cox regression was performed to identify predictive factors. A nomogram was established based on these characteristics to predict OS in SCM. The calibration curve, concordance index (C-index), area under the receiver operating characteristic curve, and decision-curve analysis were utilized to assess the accuracy and reliability of the model. The net reclassification improvement and integrated discrimination improvement were also applied in this model to evaluate its differences with the AJCC model. Results: The developed nomogram suggests that race, AJCC stage, chemotherapy status, regional node examination status, marital status, and sex have the greatest effects on OS in SCM. The nomogram had a higher C-index than the AJCC staging system (0.751 versus 0.633 in the training cohort and 0.747 versus 0.650 in the validation cohort). Calibration plots illustrated that the model was capable of being calibrated. These criteria demonstrated that the nomogram outperforms the AJCC staging system alone. Conclusion: The nomogram developed in this study is sufficiently reliable for forecasting the risk and prognosis of SCM, which may facilitate personalized treatment recommendations in upcoming clinical trials.

Analysis and prediction of 5-year survival in patients with cutaneous melanoma: a model-based period analysis.

Background: The survival and prognosis of patients are significantly threatened by cutaneous melanoma (CM), which is a highly aggressive disease. It is therefore crucial to determine the most recent survival rate of CM. This study used population-based cancer registry data to examine the 5-year relative survival rate of CM in the US. Methods: Period analysis was used to assess the relative survival rate and trends of patients with CM in the Surveillance, Epidemiology, and End Results (SEER) database during 2004-2018. And based on the data stratified by age, gender, race and subtype in the SEER database, a generalized linear model was 12established to predict the 5-year relative survival rate of CM patients from 2019 to 2023. Results: The 5-year relative survival increased to various degrees for both total CM and CM subtypes during the observation period. The improvement was greatest for amelanotic melanoma, increasing from 69.0% to 81.5%. The 5-year overall relative survival rates of CM were 92.9%, 93.5%, and 95.6% for 2004-2008, 2009-2013, and 2014-2018, respectively. Females had a marginally higher survival rate than males for almost all subtypes, older people had lower survival rates than younger people, white patients had higher survival rates than nonwhite ones, and urban locations had higher rates of survival from CM than rural locations did. The survival rate of CM was significantly lower for distant metastasis. Conclusion: The survival rate of patients with CM gradually improved overall during 2004-2018. With the predicted survival rate of 96.7% for 2019-2023, this trend will still be present. Assessing the changes experienced by patients with CM over the previous 15 years can help in predicting the future course of CM. It also provides a scientific foundation that associated departments can use to develop efficient tumor prevention and control strategies.

Deep-learning-based survival prediction of patients with lower limb melanoma.

BACKGROUND: For the purpose to examine lower limb melanoma (LLM) and its long-term survival rate, we used data from the Surveillance, Epidemiology and End Results (SEER) database. To estimate the prognosis of LLM patients and assess its efficacy, we used a powerful deep learning and neural network approach called DeepSurv. METHODS: We gathered data on those who had an LLM diagnosis between 2000 and 2019 from the SEER database. We divided the people into training and testing cohorts at a 7:3 ratio using a random selection technique. To assess the likelihood that LLM patients would survive, we compared the results of the DeepSurv model with those of the Cox proportional-hazards (CoxPH) model. Calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index) were all used to assess how accurate the predictions were. RESULTS: In this study, a total of 26,243 LLM patients were enrolled, with 7873 serving as the testing cohort and 18,370 as the training cohort. Significant correlations with age, gender, AJCC stage, chemotherapy status, surgery status, regional lymph node removal and the survival outcomes of LLM patients were found by the CoxPH model. The CoxPH model's C-index was 0.766, which signifies a good degree of predicted accuracy. Additionally, we created the DeepSurv model using the training cohort data, which had a higher C-index of 0.852. In addition to calculating the 3-, 5-, and 8-year AUC values, the predictive performance of both models was evaluated. The equivalent AUC values for the CoxPH model were 0.795, 0.767, and 0.847, respectively. The DeepSurv model, in comparison, had better AUC values of 0.872, 0.858, and 0.847. In comparison to the CoxPH model, the DeepSurv model demonstrated greater prediction performance for LLM patients, as shown by the AUC values and the calibration curve. CONCLUSION: We created the DeepSurv model using LLM patient data from the SEER database, which performed better than the CoxPH model in predicting the survival time of LLM patients.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review.

BACKGROUND: Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS: We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS: Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION: Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Proteomics analysis to investigate the potential mechanism of theacrine against UV-induced skin photodamage.

AIM: This study aimed to explore the underlying mechanism of theacrine treatment of UV-induced skin photodamage. MATERIALS AND METHODS: Tandem Mass Tag (TMT) relative quantitative proteomics analysis was used to characterize the proteins and pathways associated with the ability of theacrine to combat photodamage in mouse skin by modeling UV irradiation of the backs of ICR mice. RESULTS: Apoptosis-related proteins and signaling pathways play a key role in the ability of theacrine to protect against skin photodamage, according to proteomic and bioinformatics analysis; molecular docking and Western blotting further revealed that theacrine was associated with apoptosis-related proteins (p53, Bcl-2, Bax, caspase-3, and cleaved-caspase-3) with strong binding affinity, which can significantly reduce skin cell apoptosis induced by UV exposure. CONCLUSION: The findings revealed that theacrine can reduce UVB-induced epidermal damage by controlling the apoptosis signaling pathway, implying that theacrine could be a useful anti-UVB damage agent.

Prognosis of the Keratinizing Squamous Cell Carcinoma of the Tongue Based on Surveillance, Epidemiology, and End Results Database.

Background: The objective of this study is to determine the prognostic factors of keratinizing squamous cell carcinoma of the tongue (KTSCC) and to establish a prognostic nomogram of KTSCC to assist clinical diagnosis and treatment. Methods: This study identified 3874 patients with KTSCC from the Surveillance, Epidemiology, and End Results (SEER) database, and these patients were randomly divided into the training (70%, (n = 2711) and validation (30%, n = 1163) cohorts. Cox regression was then used to filter variables. Nomograms were then constructed based on meaningful variables. Finally, the concordance index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration charts, and decision-curve analysis (DCA), were used to evaluate the discrimination, accuracy and effectiveness of the model. Results: A nomogram model was established for predicting the 3-, 5-, and 8-year overall survival (OS) probabilities of patients with KTSCC. The model indicated that age, radiotherapy sequence, SEER stage, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, radiotherapy status, race, lymph node dissection status, and sex were factors influencing the OS of patients with KTSCC. Verified by C-index, NRI, IDI, calibration curve, and DCA curve, our model has better discrimination, calibration, accuracy and net benefit compared to the AJCC system. Conclusions: This study identified the factors that affect the survival of KTSCC patients and established a prognostic nomogram that can help clinicians predict the 3-, 5-, and 8-year survival rates of KTSCC patients.

Using Restricted Cubic Splines to Study the Duration of Antibiotic Use in the Prognosis of Ventilator-Associated Pneumonia.

Background: Ventilator-associated pneumonia (VAP) is the most widespread and life-threatening nosocomial infection in intensive care units (ICUs). The duration of antibiotic use is a good predictor of prognosis in patients with VAP, but the ideal duration of antibiotic therapy for VAP in critically ill patients has not been confirmed. Research is therefore needed into the optimal duration of antibiotic use and its impact on VAP. Methods: The Medical Information Mart for Intensive Care database included 1,609 patients with VAP. Chi-square or Student's t-tests were used to compare groups, and Cox regression analysis was used to investigate the factors influencing the prognoses of patients with VAP. Nonlinear tests were performed on antibiotic use lasting <7, 7-10, and >10 days. Significant factors were included in the model for sensitivity analysis. For the subgroup analyses, the body mass indexes (BMIs) of patients were separated into BMI <30 kg/m2 and BMI ≥30 kg/m2, with the criterion of statistical significance set at p < 0.05. Restricted cubic splines were used to analyze the relationship between antibiotic use duration and mortality risk in patients with VAP. Results: In patients with VAP, the effects of antibiotic use duration on the outcomes were nonlinear. Antibiotic use for 7-10 days in models 1-3 increased the risk of antibiotic use by 2.6020-, 2.1642-, and 2.3263-fold relative to for >10 days, respectively. The risks in models 1-3 for <7 days were 2.6510-, 1.9933-, and 2.5151-fold higher than those in models with >10 days of antibiotic use, respectively. These results were robust across the analyses. Conclusions: The duration of antibiotic treatment had a nonlinear effect on the prognosis of patients with VAP. Antibiotic use durations of <7 days and 7-10 days both presented risks, and the appropriate duration of antibiotic use can ensure the good prognosis of patients with VAP.

Dysregulation of autophagy-associated microRNAs in condyloma acuminatum.

Condyloma acuminatum, which is caused by low-risk human papillomavirus (lrHPV) infection, is one of the most common sexually transmitted diseases. Autophagy is thought to be associated with condyloma acuminatum, but how the autophagy process is regulated remains unclear. MicroRNAs (miRNAs) are important regulators of gene transcription that play a central role in many biological processes, including autophagy and viral infection. This study was designed to identify autophagy-related miRNAs and their targets in condyloma acuminatum and to validate their expression. The levels of the autophagy proteins microtubule-associated protein 1 light chain 3 (LC3) and P62/SQSTM1 (P62) were abnormally increased in the local lesion tissue of condyloma acuminatum patients compared with healthy controls. MiRNAs and their target mRNAs in condyloma acuminatum patients were analyzed by bioinformatics. Eighty-one differentially expressed miRNAs were identified, of which 56 were downregulated and 25 were upregulated. Two of the differentially expressed miRNAs associated with autophagy, miRNA-30a-5p and miRNA-514a-3p, were analyzed further, and their target genes were identified as autophagy-related protein (Atg) 5 and Atg12 and Atg3 and Atg12, respectively. The expression levels of miRNA-30a-5p and miRNA-514a-3p were decreased and those of Atg5, Atg12 and Atg3 were increased in condyloma acuminatum patients compared with healthy controls. In addition, miRNA-30a-5p and miRNA-514a-3p expression correlated with the proliferation index Ki-67 in condyloma acuminatum. Taken together, our results suggest that the changes in autophagy levels in patients with condyloma acuminatum may be related to the changes in miRNA-30a-5p and miRNA-514a-3p expression. This study provides a theoretical basis for identifying new mechanisms that link miRNAs, HPV infection and host autophagy in vivo.