Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study

BackgroundElderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms.MethodsWe downloaded the data of all KC patients aged more than 65 years during 2010–2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan–Meier (K–M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsA final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms.ConclusionTwo new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population

Recent Advances in Evaluation Tools and Associated Factors for Patient Delay in Chronic Disease Patients

Patient delay will lead to increased risk of complications, reduced treatment effectiveness and lowered quality of life in chronic disease patients. Early identifying individuals with chronic diseases at high risk of patient delay, and timely delivering targeted interventions to them may greatly improve current status of patient delay in this population. Based on a literature review, we systematically summarized several major evaluation tools (including Barriers to Access to Care Evaluation scale, Perceived Barriers to Health Care-seeking Decision, Stroke Pre-Hospital Delay Behavior Intention scale, Diabetes Mellitus Diagnosis and Treatment Delayed Cognitive Behavioral Intention Scale, and Attitudes toward Medical Help-seeking Scale developed by Fisher et al.) for patient delay in chronic disease patients, and analyzed controllable (mental and cognitive) and uncontrollable (sociodemographic and disease-specific) factors associated with patient delay, offering evidence for the assessment of patient delay and development of relevant interventions. We found that the applicability and clinical application rate of these tools are low, and their predictive efficacy and threshold have been rarely studied, and patient delay may be significantly associated with patients' insufficient knowledge of the disease, low economic level and low social support

A review of the therapeutic role of the new third-generation TKI olverembatinib in chronic myeloid leukemia

Several tyrosine kinase inhibitors (TKIs) have been developed as targeted therapies to inhibit the oncogenic activity of several tyrosine kinases in chronic myeloid leukemia (CML), acute lymphoid leukemia (ALL), gastrointestinal stromal tumor (GIST), and other diseases. TKIs have significantly improved the overall survival of these patients and changed the treatment strategy in the clinic. However, approximately 50% of patients develop resistance or intolerance to imatinib. For second-generation TKIs, approximately 30%–40% of patients need to change therapy by 5 years when they are used as first-line treatment. Clinical study analysis showed that the T315I mutation is highly associated with TKI resistance. Developing new drugs that target the T315I mutation will address the dilemma of treatment failure. Olverembatinib, as a third-generation TKI designed for the T315I mutation, is being researched in China. Preliminary clinical data show the safety and efficacy in treating CML patients harboring the T315I mutation or who are resistant to first- or second-line TKI treatment. Herein, we review the characteristics and clinical trials of olverembatinib. We also discuss its role in the management of CML patients

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Accurate power consumption assessment is of critical importance in the fast-evolving world of cloud and edge computing. These technologies enable rapid data processing and storage but they also require huge amounts of energy. This energy requirement directly impacts operational costs, as well as environmental responsibility. We are conducting research to develop a specialized cloud-edge power measurement and security model. This model delivers reliable power usage data from these systems while maintaining security for the data they process and store. A combination of simulation-based analysis and real-world experimentation helped us to deliver these results. Monte Carlo based simulations produced power usage predictions under various conditions and Load Testing validated their real-world performance. A Threat Modeling-based security study identified potential vulnerabilities and suggested protection protocols. A collaborative approach enhances power measurements accuracy and encourages secure operation of the combined cloud-edge systems. By fusing these metrics, a more efficient and secure operation of computing resources becomes possible. This research underscores the critical importance of developing advanced techniques for power metering and security in cloud-edge computing systems. Future research may focus on both expanding the model’s use to an array of larger, more complex networks, as well as the inclusion of AI driven predictive analytics to amplify accuracy of power management

A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer

BackgroundThe impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery.MethodsBC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness.ResultsThis study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p < 0.001; CSS: 50 months vs. 34 months, p < 0.001). Logistic regression analysis determined six significant surgery-benefit-related variables: T stage, radiotherapy, race, liver metastasis, brain metastasis, and breast subtype. These factors were combined to establish the nomogram and a web probability calculator (https://sunshine1.shinyapps.io/DynNomapp/), with an AUC of 0.673 in the training cohort and an AUC of 0.640 in the validation cohort. The calibration curves exhibited excellent agreement. DCA indicated that the nomogram was clinically useful. Based on this model, surgery patients were assigned into two subsets: estimated sur-non-benefit and estimated sur-benefit. Patients in the estimated sur-benefit subset were associated with longer survival (median OS: 64 months vs. 33 months, P < 0.001). Besides, there was no difference in survival between the estimated sur-non-benefit subset and the non-surgery group.ConclusionOur study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates

Clinical characteristic–assisted surgical benefit stratification for resection of primary tumor in patients with advanced primary malignant bone neoplasms: a population-based propensity score–matched analysis

BackgroundPrimary tumor resection (PTR) is the standard treatment for patients with primary malignant bone neoplasms (PMBNs). However, it remains unclear whether patients with advanced PMBNs still benefit from PTR. This study aimed to develop a prediction model to estimate the beneficial probability of PTR for this population.MethodsThis study extracted data from patients diagnosed with advanced PMBNs, as recorded in the Surveillance, Epidemiology, and End Results (SEER) database, with the period from 2004 to 2015. The patient cohort was then bifurcated into two groups: those who underwent surgical procedures and the non-surgery group. Propensity score matching (PSM) was utilized to mitigate any confounding factors in the study. The survival rates of patients from both the surgical and non-surgery groups were evaluated using Kaplan–Meier (K-M) curves analysis. Moreover, the study used this method to assess the capacity of the nomogram to distinguish patients likely to derive benefits from surgical intervention. The study was grounded in the hypothesis that patients who underwent PTR and survived beyond the median overall survival (OS) time would potentially benefit from the surgery. Subsequently, logistic regression analysis was performed to ascertain significant predictors, facilitating the development of a nomogram. This nomogram was subjected to both internal and external validation using receiver operating characteristic curves, area under the curve analysis, calibration plots, and decision curve analysis.ResultsThe SEER database provided a total of 839 eligible patients for the study, among which 536 (63.9%) underwent PTR. Following a 2:1 PSM analysis, patients were classified into two groups: 364 patients in the surgery group and 182 patients in the non-surgery group. Both K-M curves and multivariate Cox regression analysis revealed that patients who received PTR had a longer survival duration, observed both before and after PSM. Crucial factors such as age, M stage, and tumor size were identified to be significantly correlated with surgical benefits in patients with advanced PMBNs. Subsequently, a nomogram was developed that uses these independent predictors. The validation of this predictive model confirmed its high accuracy and excellent discrimination ability of the nomogram to distinguish patients who would most likely benefit from surgical intervention.ConclusionIn this study, we devised a user-friendly nomogram to forecast the likehood of surgical benefits for patients diagnosed with advanced PMBNs. This tool facilitates the identification of the most suitable candidates for PTR, thus promoting more discerning and effective use of surgical intervention in this patient population

BmC/EBPZ gene is essential for the larval growth and development of silkworm, Bombyx mori

The genetic male sterile line (GMS) of the silkworm Bombyx mori is a recessive mutant that is naturally mutated from the wild-type 898WB strain. One of the major characteristics of the GMS mutant is its small larvae. Through positional cloning, candidate genes for the GMS mutant were located in a region approximately 800.5 kb long on the 24th linkage group of the silkworm. One of the genes was Bombyx mori CCAAT/enhancer-binding protein zeta (BmC/EBPZ), which is a member of the basic region-leucine zipper transcription factor family. Compared with the wild-type 898WB strain, the GMS mutant features a 9 bp insertion in the 3′end of open reading frame sequence of BmC/EBPZ gene. Moreover, the high expression level of the BmC/EBPZ gene in the testis suggests that the gene is involved in the regulation of reproduction-related genes. Using the CRISPR/Cas9-mediated knockout system, we found that the BmC/EBPZ knockout strains had the same phenotypes as the GMS mutant, that is, the larvae were small. However, the larvae of BmC/EBPZ knockout strains died during the development of the third instar. Therefore, the BmC/EBPZ gene was identified as the major gene responsible for GMS mutation

/UV Synergistic Aging of Polyester Polyurethane Film Modified by Composite UV Absorber

The pure polyester polyurethane (TPU) film and the modified TPU (M-TPU) film containing 2.0 wt.% inorganic UV absorbers mixture (nano-ZnO/CeO2 with weight ratio of 3 : 2) and 0.5 wt.% organic UV absorbers mixture (UV-531/UV-327 with weight ratio of 1 : 1) were prepared by spin-coating technique. The accelerated aging tests of the films exposed to constant UV radiation of 400 ± 20 µW/cm2 (313 nm) with an ozone atmosphere of 100 ± 2 ppm were carried out by using a self-designed aging equipment at ambient temperature and relative humidity of 20%. The aging resistance properties of the films were evaluated by UV-Vis spectra, Fourier transform infrared spectra (FT-IR), photooxidation index, and carbonyl index analysis. The results show that the composite UV absorber has better protection for TPU system, which reduces distinctly the degradation of TPU film. O3/UV aging of the films increases with incremental exposure time. PI and CI of TPU and M-TPU films increase with increasing exposure time, respectively. PI and CI of M-TPU films are much lower than that of TPU film after the same time of exposure, respectively. Distinct synergistic aging effect exists between ozone aging and UV aging when PI and CI are used as evaluation index, respectively. Of course, the formula of these additives needs further improvement for industrial application