Unveiling the genetic tapestry: Rare disease genomics of spinal muscular atrophy and phenylketonuria proteins.

Rare diseases, defined by their low prevalence, present significant challenges, including delayed detection, expensive treatments, and limited research. This study delves into the genetic basis of two noteworthy rare diseases in Saudi Arabia: Phenylketonuria (PKU) and Spinal Muscular Atrophy (SMA). PKU, resulting from mutations in the phenylalanine hydroxylase (PAH) gene, exhibits geographical variability and impacts intellectual abilities. SMA, characterized by motor neuron loss, is linked to mutations in the survival of motor neuron 1 (SMN1) gene. Recognizing the importance of unveiling signature genomics in rare diseases, we conducted a quantitative study on PAH and SMN1 proteins of multiple organisms by employing various quantitative techniques to assess genetic variations. The derived signature-genomics contributes to a deeper understanding of these critical genes, paving the way for enhanced diagnostics for disorders associated with PAH and SMN1.

Method of Inoculation Influences the Survival of Salmonella enterica on Retail and Orchard Peaches.

Challenge studies associated with fruits and vegetables generally utilize wet bacterial inoculation methods. However, a recent salmonellosis outbreak in the U.S. was linked to peaches plausibly contaminated via fugitive dust from a nearby animal operation. This outbreak has highlighted the need for a suitable inert carrier which can be used for the dry transfer of Salmonella enterica to produce. The purpose of this study was 1) to examine the population stability of S. enterica and its surrogate, Enterococcus faecium, in different dry matrices during extended storage to identify suitable carriers and 2) to evaluate the survival of S. enterica on peaches based on the mode of contamination (i.e., wet vs. dry). S. enterica and E. faecium were cultivated on tryptic soy agar (TSA) and inoculated into corn-cob small animal litter, sand, or silica at 10-11 log CFU/g. Matrices were mixed by hand and stored at 25°C and 33% relative humidity for up to 120 d. S. enterica remained relatively stable in the silica and litter, with no significant decrease in population after 14 and 28 d, respectively. E. faecium significantly reduced in all matrices, with the greatest reduction observed in silica (2.86 log CFU/g after 120 d). Additional carriers would need to be assessed for E. faecium which could maintain its population stability. Silica was ultimately selected for the dry carrier of S. enterica. Peaches available at retail or from orchards were inoculated with S. enterica using the silica carrier or by spot or dip inoculation methods at 5 log CFU/peach and stored at 5°C and 80% relative humidity for up to 28 d. The population of S. enterica significantly reduced on all peaches except for the dry inoculated orchard peaches, where the population remained stable (4.62 ± 0.35 log CFU/peach after 28 d). Results from this study determined that the mode of contamination influences the survival of S. enterica on peaches and that dry inoculation methods should be considered for produce in some instances.

Epidemiological profile of neuroendocrine tumors in adults in Brazil.

Objective: Neuroendocrine tumors (NETs) are a set of diseases that originate from neuroendocrine cells, which comprises a diffuse endocrine system present in various organs of the body. These tumors are more frequent in the gastrointestinal tract (70%) and the bronchopulmonary system (20%-30%). A NET incidence rate of 1-5 per 100,000 inhabitants has been estimated for several European countries and the USA employing 20 years of data. However, no comprehensive studies on this rare neoplasm are available in Brazil. In this context, the aim of this study was to characterize the epidemiological NET profile in the country. Materials and methods: This is a retrospective descriptive observational study based on data from Hospital Cancer Records available at the Brazilian National Cancer Institute and the São Paulo Oncocentro Foundation. Demographic, clinical and treatmentrelated variables were analyzed from selected cases employing descriptive statistics. Results and Conclusion: A total of 15,859 cases were identified, most occurring in males (53.4%) and in individuals under 65 years old (63.3%). Small cell carcinoma was the most frequent histological type (46.7%). Bronchopulmonary tumors were the most frequent NETs, followed by pancreatic tumors, with cases mostly concentrated in high complexity centers in the Brazilian Southeast and treated mainly with surgery and chemotherapy, with over half of the patients diagnosed in advanced stages of the disease.

Impact of tumor size on overall survival and cancer-specific survival of early-onset colon and rectal cancer: a retrospective cohort study.

PURPOSE: This study aimed to investigate the impact of tumor size on survival in early-onset colon and rectal cancer. METHODS: Early-onset colon and rectal cancer patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Tumor size was analyzed as both continuous and categorical variables. Several statistical techniques, including restricted cubic spline (RCS), Cox proportional hazard model, subgroup analysis, propensity score matching (PSM), and Kaplan-Meier survival analysis, were employed to demonstrate the association between tumor size and overall survival (OS) and cancer-specific survival (CSS) of early-onset colon and rectal cancer. RESULTS: Seventeen thousand five hundred fifty-one (76.7%) early-onset colon and 5323 (23.3%) rectal cancer patients were included. RCS analysis confirmed a linear association between tumor size and survival. Patients with a tumor size > 5 cm had worse OS and CSS, compared to those with a tumor size ≤ 5 cm for both early-onset colon and rectal cancer. Notably, subgroup analysis showed that a smaller tumor size (≤ 50 mm) was associated with worse survival in stage II early-onset colon cancer, although not statistically significant. After PSM, Kaplan-Meier survival curves showed that the survival of patients with tumor size ≤ 50 mm was better than that of patients with tumor size > 50 mm. CONCLUSION: Patients with tumors larger than 5 cm were associated with worse survival in early-onset colon and rectal cancer. However, smaller tumor size may indicate a more biologically aggressive phenotype, correlating with poorer survival in stage II early-onset colon cancer.

Preoperative smoking and robot-assisted radical cystectomy outcomes & complications in multicenter KORARC database.

To investigate the influence of preoperative smoking history on the survival outcomes and complications in a cohort from a large multicenter database. Many patients who undergo radical cystectomy (RC) have a history of smoking; however, the direct association between preoperative smoking history and survival outcomes and complications in patients with muscle-invasive bladder cancer (MIBC) who undergo robot-assisted radical cystectomy (RARC) remains unexplored. We conducted a retrospective analysis using data from 749 patients in the Korean Robot-Assisted Radical Cystectomy Study Group (KORARC) database, with an average follow-up duration of 30.8 months. The cohort was divided into two groups: smokers (n = 351) and non-smokers (n = 398). Propensity score matching was employed to address differences in sample size and baseline demographics between the two groups (n = 274, each). Comparative analyses included assessments of oncological outcomes and complications. After matching, smoking did not significantly affect the overall complication rate (p = 0.121). Preoperative smoking did not significantly increase the occurrence of complications based on complication type (p = 0.322), nor did it increase the readmission rate (p = 0.076). There were no perioperative death in either group. Furthermore, preoperative smoking history showed no significant impact on overall survival (OS) [hazard ratio (HR) = 0.87, interquartile range (IQR): 0.54-1.42; p = 0.589] and recurrence-free survival (RFS) (HR = 1.12, IQR: 0.83-1.53; p = 0.458) following RARC for MIBC. The extent of preoperative smoking (≤ 10, 10-30, and ≥ 30 pack-years) had no significant influence on OS and RFS in any of the categories (all p > 0.05). Preoperative smoking history did not significantly affect OS, RFS, or complications in patients with MIBC undergoing RARC.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.

BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Improved clinical data imputation via classical and quantum determinantal point processes.

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.

Prognostic analysis of stage IIIC1p cervical cancer patients.

Background: Stage IIIC1p cervical cancer is characterized by marked heterogeneity and considerable variability in the postoperative prognosis. This study aimed to identify the clinical and pathological characteristics affecting the survival of patients diagnosed with stage IIIC1p cervical cancer. Methods: We retrospectively analyzed patients diagnosed with stage IIIC1p cervical cancer who underwent radical hysterectomy and lymph node dissection between March 2012 and March 2022. Overall survival (OS) was estimated using Kaplan-Meier survival curves. Univariate and multivariate Cox proportional hazards models were used to evaluate prognostic factors for OS and forest plots were used to visualize these findings. Nomogram charts were created to forecast survival rates at 3 and 5 years, and the accuracy of predictions was evaluated using Harrell's concordance index (C-index) and calibration curves. Results: The study cohort comprised 186 women diagnosed with stage IIIC1p cervical cancer. The median follow-up duration was 51.1 months (range, 30-91 months), and the estimated 5-year OS rate was 71.5%. Multivariate analysis revealed that concurrent chemoradiotherapy plus adjuvant chemotherapy (CCRT + AC), monocyte-lymphocyte ratio (MLR), ratio of lymph node metastasis (LNM), and squamous cell carcinoma antigen (SCCA) levels independently predicted OS. Conclusions: Significant prognostic disparities exist among patients diagnosed with stage IIIC1p cervical cancer. MLR, ratio of LNM, and SCCA were associated with poor OS. In contrast, the CCRT + AC treatment regimen appeared to confer a survival advantage.

Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib.

Background: Epithelial ovarian cancer (EOC) is a significant cause of mortality among gynecological cancers. While Olaparib, a PARP inhibitor, has demonstrated efficacy in EOC maintenance therapy, individual responses vary. This study aims to assess the prognostic significance of body composition and systemic inflammation markers in EOC patients undergoing initial Olaparib treatment. Methods: A retrospective analysis was conducted on 133 EOC patients initiating Olaparib therapy. Progression-free survival (PFS) was assessed through Kaplan-Meier analysis and Cox proportional hazards regression. Pre-treatment computed tomography images were utilized to evaluate body composition parameters including subcutaneous adipose tissue index (SATI), visceral adipose tissue index (VATI), skeletal muscle area index (SMI), and body mineral density (BMD). Inflammatory markers, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), serum albumin, and hemoglobin levels, were also measured. Results: The median follow-up duration was 16 months (range: 5-49 months). Survival analysis indicated that high SATI, high VATI, high SMI, high BMD, low NLR, and low PLR were associated with decreased risk of disease progression (all p < 0.05). Multivariate analysis identified several factors independently associated with poor PFS, including second or further lines of therapy (HR = 2.16; 95% CI = 1.09-4.27, p = 0.027), low VATI (HR = 3.79; 95% CI = 1.48-9.70, p = 0.005), low SMI (HR = 2.52; 95% CI = 1.11-5.72, p = 0.027), low BMD (HR = 2.36; 95% CI = 1.22-4.54, p = 0.010), and high NLR (HR = 0.31; 95% CI = 0.14-0.69, p = 0.004). Subgroup analysis in serous adenocarcinoma patients revealed distinct prognostic capabilities of SATI, VATI, SMI, PLR, and NLR. Conclusion: Body composition and inflammation variables hold promise as predictors of therapeutic response to Olaparib in EOC patients. Understanding their prognostic significance could facilitate tailored treatment strategies, potentially improving patient outcomes.

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients.

BACKGROUND: The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19). MATERIALS AND METHODS: In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data. RESULT: The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (ß = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (ß = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026). CONCLUSION: The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Effects of T2DM on postoperative outcome of patients with colorectal cancer: a study on the relationship between blood glucose control and survival rate.

To investigate the impact of type 2 diabetes (T2DM) on the prognosis of colorectal cancer (CRC). The data of 312 patients with CRC treated in the First Affiliated Hospital of Huzhou University from 2012 to 2018 were analyzed retrospectively. The patients were divided into a comorbidity group (n = 62) and a non-comorbidity group (n = 250) according to the presence of T2DM. The baseline data of the two groups were balanced by 1:2 propensity score matching (PSM). Kaplan-Meier analysis and Log-rank test were employed to compare the 5-year overall survival (OS) rates of patients. Cox regression model and inverse probability of treatment weighting (IPTW) were utilized to assess the influence of T2DM on 5-year OS of patients. Based on the results of Cox regression, a nomogram model of T2DM on 5-year OS of patients was constructed. A total of 62 patients in the comorbidity group and 124 patients in the non-comorbidity group were matched using PSM. The 5-year OS rate was lower in the comorbidity group than in the non-comorbidity group (82.23% VS 90.32%, P = 0.038). Subgroup analysis showed that the 5-year overall survival rate was higher in the good blood glucose control group than in the poor blood glucose control group (97.14% VS 62.96%, P<0.01). Multivariate Cox regression showed that the 5-year mortality risk in the comorbidity group was 2.641 times higher than that in the non-comorbidity group (P = 0.026). IPTW analysis showed that the 5-year risk of death in the comorbidity group was 2.458 times that of the non-comorbidity group (P = 0.019). The results showed that poor blood glucose control, BMI≥25 kg/m2, low differentiation, III/IV stage, and postoperative infection were independent factors affecting the 5-year overall survival rate of CRC patients (P<0.05). The ROC curve showed that the AUCs of the constructed model in predicting the 5-year OS in the training set and the testing set were 0.784 and 0.776, respectively. T2DM is identified as a risk factor for reduced 5-year survival among CRC patients, necessitating increased attention for this subgroup, particularly those with poor blood glucose control.

Nomogram models for predicting overall and cancer-specific survival in early-onset gastric cancer patients: a population-based cohort study.

To develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of early-onset gastric cancer (EOGC) patients. A total of 1077 EOGC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and an additional 512 EOGC patients were recruited from the Fourth Hospital of Hebei Medical University, serving as an external test set. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Based on these factors, two nomogram models were established, and web-based calculators were developed. These models were validated using receiver operating characteristics (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Multivariate analysis identified gender, histological type, stage, N stage, tumor size, surgery, primary site, and lung metastasis as independent prognostic factors for OS and CSS in EOGC patients. Calibration curves and DCA curves demonstrated that the two constructed nomogram models exhibited good performance. These nomogram models demonstrated superior performance compared to the 7th edition of the AJCC tumor-node-metastasis (TNM) classification (internal validation set: 1-year OS: 0.831 vs 0.793, P = 0.072; 1-year CSS: 0.842 vs 0.816, P = 0.190; 3-year OS: 0.892 vs 0.857, P = 0.039; 3-year CSS: 0.887 vs 0.848, P = 0.018; 5-year OS: 0.906 vs 0.880, P = 0.133; 5-year CSS: 0.900 vs 0.876, P = 0.109). In conclusion, this study developed two nomogram models: one for predicting OS and the other for CSS of EOGC patients, offering valuable assistance to clinicians.

Construction of gastric cancer prognostic signature based on the E26 transcription factor and the identification of novel oncogene ELK3.

The aim of the present study was to investigate the function of 29 E26 (ETS) transcription factor families in gastric cancer (GC) and determine their association with prognosis. Our analysis of the expression of the ETS family revealed that 28 genes were dysregulated in GC, and that their expression was associated with multiple clinicopathological features (P<0.05). Based on the expression signature of the ETS family, consensus clustering was performed to generate two gastric cancer subtypes. These subtypes exhibited differences in overall survival (OS, P = 0.161), disease-free survival (DFS, P<0.05) and GC grade (P<0.01). Functional enrichment analysis of the target genes associated with the ETS family indicated that these genes primarily contribute to functions that facilitate tumor progression. A systematic statistical analysis was used to construct a prognostic model related to OS and DFS in association with the ETS family. This model demonstrated that the maximum area under the curve (AUC) values for predicting OS and DFS were 0.729 and 0.670, respectively, establishing ETS as an independent prognostic factor for GC Furthermore, a nomogram was created from the prognostic signature, and its predictive accuracy was confirmed by a calibration curve. Finally, the expression and prognostic significance of the six genes comprising the model were also examined. Among these, ELK3 was found to be significantly overexpressed in GC clinical samples. Subsequent in vitro and in vivo studies verified that ELK3 regulates GC proliferation and metastasis, highlighting its potential as a therapeutic target for gastric cancer.

Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort.

Young breast cancer (YBC) patients often face a poor prognosis, hence it's necessary to construct a model that can accurately predict their long-term survival in early stage. To realize this goal, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) databases between January 2010 and December 2020, and meanwhile, enrolled an independent external cohort from Tianjin Medical University Cancer Institute and Hospital. The study aimed to develop and validate a prediction model constructed using the Random Survival Forest (RSF) machine learning algorithm. By applying the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, we pinpointed key prognostic factors for YBC patients, which were used to create a prediction model capable of forecasting the 3-year, 5-year, 7-year, and 10-year survival rates of YBC patients. The RSF model constructed in the study demonstrated exceptional performance, achieving C-index values of 0.920 in the training set, 0.789 in the internal validation set, and 0.701 in the external validation set, outperforming the Cox regression model. The model's calibration was confirmed by Brier scores at various time points, showcasing its excellent accuracy in prediction. Decision curve analysis (DCA) underscored the model's importance in clinical application, and the Shapley Additive Explanations (SHAP) plots highlighted the importance of key variables. The RSF model also proved valuable in risk stratification, which has effectively categorized patients based on their survival risks. In summary, this study has constructed a well-performed prediction model for the evaluation of prognostic factors influencing the long-term survival of early-stage YBC patients, which is significant in risk stratification when physicians handle YBC patients in clinical settings.

Cancer incidence, stage shift and survival during the 2020 COVID-19 pandemic: A population-based study in Belgium.

The COVID-19 pandemic was associated with a profound decline in cancer diagnoses in 2020 in Belgium. Disruption in diagnostic and screening services and patient reluctance to visit health facilities led to fewer new cases and concerns that cancers may be diagnosed at more advanced stages and hence have poorer prognosis. Using data from mandatory cancer registration covering all of Belgium, we predicted cancer incidence, stage distribution and 1-year relative survival for 2020 using a Poisson count model over the preceding years, extrapolated to 2020 for 11 common cancer types. We compared these expected values to the observed values in 2020 to specifically quantify the impact of the COVID-19 pandemic, accounting for background trends. A significantly lower incidence was observed for cervical, prostate, head and neck, colorectal, bladder and breast cancer, with limited or no recovery of diagnoses in the second half of 2020 for these cancer types. Changes in stage distribution were observed for cervical, prostate, bladder and ovarian and fallopian tube tumours. Generally, changes in stage distribution mainly represented decline in early-stage than in late-stage tumours. One-year relative survival was lower than predicted for lung cancer and colorectal cancer. Stage shifts are hypothesised to result from alterations in access to diagnosis, potentially due to prioritisation of symptomatic patients, and patient reluctance to contact a physician. Since there were over 5000 fewer cancer diagnoses than expected by the end of 2020, it is critical to monitor incidence, stage distribution and survival for these cancers in the coming years.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Prioritizing Disease Diagnosis in Neonatal Cohorts through Multivariate Survival Analysis: A Nonparametric Bayesian Approach.

Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships.

Trends in the survival of patients with trisomy 13 from 1995 to 2021: A population study in Japan.

It remains unclear whether recent changes in the prognosis and management of patients with trisomy 13 impact patient survival. We investigated changes in survival of patients with trisomy 13 in Japan. Data from the Vital Statistics Database in Japan was retrieved to examine the association of sex, surgical history, and years of birth and death with changes in survival patterns in 1164 patients with trisomy 13 between 1995 and 2021. The rates of deaths due to trisomy 13 increased from 9.8% to 23.1% in those over 1 year of age and from 7.3% to 19.2% in those within 24 h of birth between 1995 and 2021. The median survival time was longer in 2009-2021 than in 1996-2008 (40 vs. 84 days, p < 0.001). The median survival time and the rate of patients with surgical history increased from 91 days and 16.0% in 1996-2008 to 179 days and 28.0% in 2009-2021, respectively. Median survival time among patients with trisomy 13 has increased over the last 26 years, with almost 1 in 3 patients currently surviving for more than 1 year. The increased surgical intervention rate might have contributed to this improvement.

[Curative effect of percutaneous microwave ablation therapy on hepatocellular carcinoma survival: a 15-year real-world study].

Objective: To evaluate the long-term efficacy of percutaneous microwave ablation (MWA) therapy for hepatocellular carcinoma. Methods: 2054 cases with Barcelona Clinic Liver Cancer (BCLC) stage 0~B at the Fifth Medical Center of the Chinese People's Liberation Army General Hospital from January 2006 to September 2020 were retrospectively collected. All patients were followed up for at least 2 years. The primary endpoint of overall survival and secondary endpoints (tumor-related survival, disease-free survival, and postoperative complications) of patients treated with ultrasound-guided percutaneous MWA were analyzed. Kaplan-Meier method was used for stratified survival rate analysis. Fine-and-Gray competing risk model was used to analyze overall survival. Results: A total of 5 503 HCC nodules [mean tumor diameter (2.6±1.6) cm] underwent 3 908 MWAs between January 2006 and September 2020, with a median follow-up time of 45.6 (24.0 -79.2) months.The technical effectiveness rate of 5 375 tumor nodules was 97.5%. The overall survival rates at 5, 10, and 15-years were 61.6%, 38.8%, and 27.0%, respectively. The tumor-specific survival rates were 67.1%, 47.2%, and 37.7%, respectively. The free tumor survival rates were 25.8%, 15.7%, and 9.9%, respectively. The incidence rate of severe complications was 2.8% (108/3 908). Further analysis showed that the technical effectiveness and survival rate over the passing three time periods from January 2006-2010, 2011-2015, and 2016-September 2020 were significantly increased, with P < 0.001, especially for liver cancer 3.1~5.0 cm (P < 0.001). Conclusion: Microwave ablation therapy is a safe and effective method for BCLC stage 0-B, with significantly enhanced technical efficacy and survival rate over time.

CD163+ macrophages in the triple-negative breast tumor microenvironment are associated with improved survival in the Women's Circle of Health Study and the Women's Circle of Health Follow-Up Study.

BACKGROUND: Tumor-associated macrophages (TAMs) are a prominent immune subpopulation in the tumor microenvironment that could potentially serve as therapeutic targets for breast cancer. Thus, it is important to characterize this cell population across different tumor subtypes including patterns of association with demographic and prognostic factors, and breast cancer outcomes. METHODS: We investigated CD163+ macrophages in relation to clinicopathologic variables and breast cancer outcomes in the Women's Circle of Health Study and Women's Circle of Health Follow-up Study populations of predominantly Black women with breast cancer. We evaluated 611 invasive breast tumor samples (507 from Black women, 104 from White women) with immunohistochemical staining of tissue microarray slides followed by digital image analysis. Multivariable Cox proportional hazards models were used to estimate hazard ratios for overall survival (OS) and breast cancer-specific survival (BCSS) for 546 cases with available survival data (median follow-up time 9.68 years (IQR: 7.43-12.33). RESULTS: Women with triple-negative breast cancer showed significantly improved OS in relation to increased levels of tumor-infiltrating CD163+ macrophages in age-adjusted (Q3 vs. Q1: HR = 0.36; 95% CI 0.16-0.83) and fully adjusted models (Q3 vs. Q1: HR = 0.30; 95% CI 0.12-0.73). A similar, but non-statistically significant, association was observed for BCSS. Macrophage infiltration in luminal and HER2+ tumors was not associated with OS or BCSS. In a multivariate regression model that adjusted for age, subtype, grade, and tumor size, there was no significant difference in CD163+ macrophage density between Black and White women (RR = 0.88; 95% CI 0.71-1.10). CONCLUSIONS: In contrast to previous studies, we observed that higher densities of CD163+ macrophages are independently associated with improved OS and BCSS in women with invasive triple-negative breast cancer. Trial registration Not applicable.