Calibration Curve Research Articles

Development of a potentiometric sensor for mercury (II) ion using cerium (IV) tinmolybdophosphate

ObjectivesThe primary objective of this report is to develop a heperopolyacid salt, Cerium (IV) tinmolybdophosphate (CeSnMoP), with distinctive attributes that significantly enhance its ion exchange capacity. Through deliberate adjustments in temperature, pH, and volume ratios, we have carefully prepared a range of CeSnMoP samples. One sample exhibiting an ion exchange capacity (IEC) of 5.06 ± 0.2 meq gm-1 has been identified for further extensive analysis. The second objective was to develop the potentiometric sensor by using the synthesised sample therefore it was transformed into an electrode incorporating PVC as binder material and validated as a potentiometric sensor for Mercury ions which can work as variable media.Data descriptionInstrumental analyses, such as XRD, IR, TGA, SEM and EDS, were used to elucidate the compound’s structural aspects. Distribution coefficient (Kd) studies highlighted the compound's pronounced selectivity towards Hg2+ ions. This catalyst was further utilized as an electro-active substance for detecting Hg2+ ions in an external solution. Epoxy resin played the role of a binder in the electrode formulations. The electrode, comprising a membrane with 50% exchanger material, demonstrated superior performance. This selected membrane exhibited a wide operational concentration range of 1 × 10–6 M – 1 × 10–1 M of Hg2+ ions for quantitative analysis of unknown samples of mercury ions. The lower detection limit for the calibration curve was recorded up to 2 × 10–8 M from 1 to 10–1 M. The electrode effectively sensed this metal ion within the pH range of 3.74–7.51 and exhibited a lifespan exceeding 8 months.

Which echocardiographic measure of longitudinal function is most informative?

Abstract Background The longitudinal function of the left ventricle can be quantified by measuring the myocardial excursion with or without normalising by the diastolic length to derive strain. Furthermore, these lengths can be determined from the curved myocardial length or in a straight-line from the mid-annulus to the apex parallel to the long axis. All four combinations have been advocated and used, but the optimal is unknown. Purpose Compare the prognostic information contained by these four measures of longitudinal function in a large retrospective cohort, with measurements performed by AI. Methods The cohort consisted of echocardiograms performed during 2015 at our institution for which mortality data were available. Our previously published AI based view classifier selected the apical 4-, 2-, and 3-chamber views from each study, before the endocardial contour and annular and apical points were segmented by another neural network. From this the curved and long-axis excursion was calculated, and then strain was derived using the diastolic length. Nested non-linear Cox-models including age and sex and each of the longitudinal measures were constructed, and the information contained by each variable was quantified via Chi-Square and compared using bootstrap methods. Results 1733 patient studies were included, the median age was 64 (IQR 49 to 75) years and 51% were male. A total of 213 (14%) deaths occurred. The median excursion and strains were 3.25 cm (IQR 2.68 to 3.81) and -17% (IQR -20% to -14%) along the endocardial curve and 2.36 cm (IQR 1.88 to 2.87) and -14% (IQR -17% to -11%) along the long axis. All four longitudinal measures significantly added to a base model containing age and sex (p=0.003, p=0.005, p=0.005, p=0.002). Whilst the simple long axis excursion was numerically the most prognostic, none of the measures were significantly different from each other. Conclusions In this head-to-head AI-assisted retrospective cohort study using mortality as the endpoint, there was no good evidence that more complex measures of longitudinal function had better prognostic performance.Information provided by model componentsLong axis excursion calibration curve

A nomogram model for predicting frailty in middle-aged and elderly patients with colorectal cancer:A cross-sectional study

PurposeFrailty is a syndrome caused by multiple factors and can lead to serious consequences for middle-aged and elderly patients with colorectal cancer. However, few studies have comprehensively explored predictors of frailty and built predictive models. Therefore, our aim is to develop and evaluate a predictive model for frailty in middle-aged and elderly patients with colorectal cancer. MethodsFrom July 2023 to February 2024, a total of 502 middle-aged and elderly patients with colorectal cancer participated in this survey. Patients were randomly divided into training and validation groups in a 7:3 ratio. Univariate and multiple logistic regression analysis were employed to identify potential predictors of frailty in these patients. A nomogram was constructed based on the predictive factors, and the model underwent internal validation. ResultsIn the training cohort, logistic regression revealed that self-perceived health, chronic pain, loneliness, depression, and health-promoting lifestyle were independent predictors of frailty. The Areas Under the Curve (AUC) of the training and validation groups were 0.845 and 0.851, respectively. The calibration curve of the nomogram demonstrated good consistency between predicted and actual probabilities. Decision curve analysis revealed good clinical benefit. ConclusionsThis study established a predictive model with satisfactory predictive ability, providing empirical evidence for the early detection and intervention of frailty in middle-aged and elderly patients with colorectal cancer. The nomogram model has significant potential for clinical application, as it can be integrated into routine oncology practice to identify high-risk patients early, allowing for timely and individualized interventions to improve patient outcomes.

Validation and development of a scoring system for comprehensively evaluating arterial damage in the elderly chinese

Abstract Background and aims Existing assessments on arterial damage focus on specific segments of arterial system (e.g., carotid artery), lacking a comprehensive index to evaluate overall arterial health. We aimed to integrate existing assessments for arterial damage and develop a new score, named Arterial Damage Index (ADI), to comprehensively evaluate arterial health status of the whole-body arterial system. Methods A total of 3075 community-dwelling elderly Chinese (age ≥65 years) from the North Shanghai Study (2014–2022) were included. Assessments on arteries including ankle-brachial index (ABI), pulse wave velocity (PWV), carotid intima-media thickness (CIMT) and plaques, pulse pressure (PP), and inter-arm (IASBPD) and -leg (ILSBPD) systolic blood pressure difference were included for screening. Cox proportional hazards regressions of MACEs (non-fatal myocardial infarction, non-fatal stroke, and all-cause mortality) were conducted to acquire the ADI algorithm. Internal and external validation on ADI were also performed. Results Over a median follow-up period of 6.4 years, 316 individuals (10.3%) experienced an incident of MACE. After screening, PWV, ABI, PP, CIMT, and carotid plaque were included for ADI scoring system. Survival analysis revealed a significant difference in the rates of composite events across different ADI stratifications (p for trend < .05). ADI showed superior discriminative performance (C-index 0.65±0.02) compared to any individual arterial-related indicator. Calibration curve shows the predictive model showed that the predicted and actual probabilities were broadly consistent. Additionally, ADI provided better incremental value when added to the base model (net reclassification improvement, 36.2% [95% CI, 15.3%-52.1%]; p < .05, integrated discrimination improvement, 7.9% [95% CI, 4.9%-11.0%]; p < .05) compared to any individual arterial-related indicator. These findings were confirmed in internal validation sets (p for DeLong tests >0.05), and was externally validated in 2155 participants from a community-based cardiovascular disease cohort among residents in Beijing, China. Conclusion ADI can comprehensively assess arterial damage and provide a valuable tool for further assessment of future cardiovascular risk.central illustration

A Novel PCR-Free Ultrasensitive GQD-Based Label-Free Electrochemical DNA Sensor for Sensitive and Rapid Detection of Francisella tularensis.

Biological warfare agents are infectious microorganisms or toxins capable of harming or killing humans. Francisella tularensis is a potential bioterrorism agent that is highly infectious, even at very low doses. Biosensors for biological warfare agents are simple yet reliable point-of-care analytical tools. Developing highly sensitive, reliable, and cost-effective label-free DNA biosensors poses significant challenges, particularly when utilizing traditional techniques such as fluorescence, electrochemical methods, and others. These challenges arise primarily due to the need for labeling, enzymes, or complex modifications, which can complicate the design and implementation of biosensors. In this study, we fabricated Graphene Quantum dot (GQD)-functionalized biosensors for highly sensitive label-free DNA detection. GQDs were immobilized on the surface of screen-printed gold electrodes via mercaptoacetic acid with a thiol group. The single-stranded DNA (ssDNA) probe was also immobilized on GQDs through strong π-π interactions. The ssDNA probe can hybridize with the ssDNA target and form double-stranded DNA, leading to a decrease in the effect of GQD but a positive shift associated with the increase in DNA concentration. The specificity of the developed system was observed with different microorganism target DNAs and up to three-base mismatches in the target DNA, effectively distinguishing the target DNA. The response time for the target DNA molecule is approximately 1010 s (17 min). Experimental steps were monitored using UV/Vis spectroscopy, Atomic Force Microscopy (AFM), and electrochemical techniques to confirm the successful fabrication of the biosensor. The detection limit can reach 0.1 nM, which is two-five orders of magnitude lower than previously reported methods. The biosensor also exhibits a good linear range from 105 to 0.01 nM and has good specificity. The biosensor's detection limit (LOD) was evaluated as 0.1 nM from the standard calibration curve, with a correlation coefficient of R2 = 0.9712, showing a good linear range and specificity. Here, we demonstrate a cost-effective, GQD-based SPGE/F. tularensis DNA test suitable for portable electrochemical devices. This application provides good perspectives for point-of-care portable electrochemical devices that integrate sample processing and detection into a single cartridge without requiring a PCR before detection. Based on these results, it can be concluded that this is the first enzyme-free electrochemical DNA biosensor developed for the rapid and sensitive detection of F. tularensis, leveraging the nanoenzyme and catalytic properties of GQDs.

Analysis of Risk Factors for Multidrug-Resistant Organism (MDRO) Infections and Construction of a Risk Prediction Model in a Cancer Specialty Hospital.

Aims/Background Patients receiving treatment in specialized cancer hospitals are particularly susceptible to multidrug-resistant organisms (MDRO) infections due to factors such as weakened immune systems caused by intensive treatments and prolonged hospital stays. This study aims to investigate the risk factors for MDRO infections in the cancer specialty hospital setting and to develop a corresponding risk prediction model. Methods Patients diagnosed with MDRO infections were selected for the MDRO infection group (n = 238), and those without for the non-MDRO infection group (n = 238). Non-parametric tests, chi-square tests, and multivariate logistic regression analysis were used to identify the primary risk factors for MDRO infections. With the aid of analysis utilizing R software 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria), we developed a nomogram prediction model, which was evaluated using the receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA). Results Age, antibiotic application time, and central venous catheterization were independent risk factors for MDRO infection (p < 0.05). The constructed nomogram prediction model for patients with MDRO infection has a C-index of 0.8640. The ROC curve results showed that the prediction model has a specificity of 0.7700, a sensitivity of 0.8800, and an area under the curve (AUC) of 0.8800. Conclusion This study identifies significant risk factors for MDRO infections in a cancer specialty hospital setting and offers a clinically useful prediction model, which may aid in targeted preventive measures and optimization of antibiotics usage, thereby potentially reducing the incidence and impact of these infections.

Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

PurposeTo develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.MethodsThis is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility.ResultsThe T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram.ConclusionA nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

Evaluation of thiamine and riboflavin in different varieties of rice grown in Kurigram district, Bangladesh

ContextRice is indeed a staple food in many parts of the world, including Bangladesh. This research aimed to evaluate the two essential vitamin levels in different processed rice sunned, parboiled, cooked sunned, cooked parboiled, and washed cooked rice. ObjectiveThe study focused on analyzing the thiamine (vitamin B1) and riboflavin (vitamin B2) contents in 10 different varieties of paddy samples grown in the Kurigram district of Bangladesh. MethodsA high performance liquid chromatographic (HPLC) technique assembled with fluorescence detector was employed for these analyses. Two calibration curves were generated to analyze vitamin B1 and B2 (for thiamine the regression coefficient, r2 = 0.9992 and riboflavin r2 = 0.9989). The study demonstrated a recovery rate of 96.88 % for vitamin B1 and 104.24 % for vitamin B2. Overall, these findings provide valuable insights into the vitamin B1 and B2 contents across different rice samples. Results and conclusionThe results showed that the parboiled samples had the highest thiamine content, ranging from 342.50 ± 0.3 to 673.0 ± 7.5 μg/100 g, compared to the sunned samples, which had a range of 209.5 ± 0.3 to 337.4 ± 4.3 μg/100 g. Conversely, the sunned samples had the highest riboflavin content, ranging from 26.4 ± 0.2 to 59.6 ± 0.3 μg/100 g, while the parboiled samples had a range of 3.0 ± 0.1 to 26.6 ± 0.01 μg/100 g. Additionally, it was observed that the steaming process increased the thiamine content and reduced the riboflavin content in parboiled rice samples but decreased both the thiamine and riboflavin in sunned rice samples. Another study showed a gradual decrease in the levels of both vitamins B1 and B2 in sunned, cooked sunned and washed cooked sunned rice samples. SignificanceThese findings provide valuable insights into the impact of processing methods, such as parboiling and steaming, on the thiamine and riboflavin contents in rice. The results suggest that parboiled rice may be a preferable choice for individuals seeking higher thiamine content in their diet, while sunned rice may contain higher levels of riboflavin. The results shed light on the nutritional profiles of different rice varieties and highlight the potential impact of processing techniques on the vitamin content of rice.

A novel method for the simultaneous determination of flavonoids in Pteridium aquilinum via SPE combined with UPLC-DAD via QAMS

A novel analytical method based on ultra-performance liquid chromatography-diode array detector (UPLC-DAD), solid-phase extraction (SPE), and quantitative analysis of multiple components by single marker (QAMS) was developed for the first simultaneous determination of 8 flavonoids in Pteridium aquilinum. The combination of QAMS and SPE demonstrated the convenient and economic advantages of the QAMS method while also exhibiting straightforward and cost-efficient advantages in terms of both the time and solvent usage of the SPE. The contents of the 8 flavonoids in P. aquilinum ranged from 0.59 to 697.08 μg/g. There were no substantial differences in the quantification when comparing the QAMS method and the external standard method (ESM). The standard deviations (SDs, %) obtained by QAMS and ESM were all less than 5 %. SPE increased the concentration of flavonoids by 4.18–18.9 times. The limits of detection (LODs) and quantification (LOQs) were in the ranges of 0.01–0.15 and 0.06–0.33 ng/g, respectively. Method validation revealed the linearity of the calibration curves (R2 ≥ 0.9989), with recoveries between 97.02 % and 105 %, and the relative standard deviations (RSD) were all lower than 5 %. In addition, rhoifolin (4), apigenin (6), and amentoflavone (8) were first reported in P. aquilinum. The results suggested that this method was simple, reliable, and feasible for determining the flavonoids in P. aquilinum samples and could be further applied to various foods and herbal medicine samples.

Identification of diagnostic markers pyrodeath-related genes in non-alcoholic fatty liver disease based on machine learning and experiment validation

Non-alcoholic fatty liver disease (NAFLD) poses a global health challenge. While pyroptosis is implicated in various diseases, its specific involvement in NAFLD remains unclear. Thus, our study aims to elucidate the role and mechanisms of pyroptosis in NAFLD. Utilizing data from the Gene Expression Omnibus (GEO) database, we analyzed the expression levels of pyroptosis-related genes (PRGs) in NAFLD and normal tissues using the R data package. We investigated protein interactions, correlations, and functional enrichment of these genes. Key genes were identified employing multiple machine learning techniques. Immunoinfiltration analyses were conducted to discern differences in immune cell populations between NAFLD patients and controls. Key gene expression was validated using a cell model. Analysis of GEO datasets, comprising 206 NAFLD samples and 10 controls, revealed two key PRGs (TIRAP, and GSDMD). Combining these genes yielded an area under the curve (AUC) of 0.996 for diagnosing NAFLD. In an external dataset, the AUC for the two key genes was 0.825. Nomogram, decision curve, and calibration curve analyses further validated their diagnostic efficacy. These genes were implicated in multiple pathways associated with NAFLD progression. Immunoinfiltration analysis showed significantly lower numbers of various immune cell types in NAFLD patient samples compared to controls. Single sample gene set enrichment analysis (ssGSEA) was employed to assess the immune microenvironment. Finally, the expression of the two key genes was validated in cell NAFLD model using qRT-PCR. We developed a prognostic model for NAFLD based on two PRGs, demonstrating robust predictive efficacy. Our findings enhance the understanding of pyroptosis in NAFLD and suggest potential avenues for therapeutic exploration.

Development and validation of a clinical and laboratory-based nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging global infectious disease with a high mortality rate. Clinicians lack a convenient tool for early identification of critically ill SFTS patients. The aim of this study was to construct a simple and accurate nomogarm to predict the prognosis of SFTS patients.MethodsWe retrospectively analyzed the clinical data of 372 SFTS patients collected between May 2015 and June 2023, which were divided 7:3 into a training set and an internal validation set. We used LASSO regression to select predictor variables and multivariable logistic regression to identify independent predictor variables. Prognostic nomograms for SFTS were constructed based on these factors and analysed for concordance index, calibration curves and area under the curve (AUC) to determine the predictive accuracy and consistency of the model.ResultsIn the training set, LASSO and multivariate logistic regression analyses showed that age, SFTSV RNA, maximum body temperature, pancreatitis, gastrointestinal bleeding, pulmonary fungal infection (PFI), BUN, and PT were independent risk factors for death in SFTS patients. There was a strong correlation between neurological symptoms and mortality (P < 0.001, OR = 108.92). Excluding neurological symptoms, nomograms constructed based on the other eight variables had AUCs of 0.937 and 0.943 for the training and validation sets, respectively. Furthermore, we found that age, gastrointestinal bleeding, PFI, bacteraemia, SFTSV RNA, platelets, and PT were the independent risk factors for neurological symptoms, with SFTSV RNA having the highest diagnostic value (AUC = 0.785).ConclusionsThe nomogram constructed on the basis of eight common clinical variables can easily and accurately predict the prognosis of SFTS patients. Moreover, the diagnostic value of neurological symptoms far exceeded that of other predictors, and SFTSV RNA was the strongest independent risk factor for neurological symptoms, but these need to be further verified by external data.

Primary diffuse large B-cell lymphoma of bone in adults: A SEER population-based study.

Primary diffuse large B-cell lymphoma of the bone (PB-DLBCL) is an extremely rare type of extra-nodal lymphoma. The clinical characteristics, management, and survival outcomes of adult PB-DLBCL patients remain poorly defined. To explore the clinical manifestations, staging, therapeutic options, prognostic factors and outcomes of adult patients with PB-DLBCL and to create a model to predict survival outcomes. Data of adult PB-DLBCL patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program 18 registries database from 2000 to 2018. The Kaplan-Meier survival analysis was conducted to calculate survival rates. Univariate Cox regression, best subset selection (BESS), and least absolute shrinkage and selection operator (LASSO), followed by backward stepwise multivariable Cox regression, were used to construct the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curves and decision curve analysis (DCA). Diffuse large B-cell lymphoma (DLBCL) (67.51%) was the most frequent type of primary bone lymphoma. The most involved sites were the spine and lower-limb long bones. For the whole cohort, the 3-, 5-, 10- and 15-year overall survival (OS) rates were 74.9%, 70.5%, 60.0%, and 49.9%, and corresponding disease-specific survival (DSS) rates were 79.7%, 77.8%, 75.1%, and 71.4%, respectively. For OS, age, Ann Arbor stage, primary site and therapy were confirmed as final factors to develop the nomogram in adult PB-DLBCL patients, whereas for DSS, Age, marital status, Ann Arbor stage, number of bone lesions, therapy and year of diagnosis were confirmed as final factors in developing the nomogram. The nomograms demonstrated good accuracy and clinical utility. Established nomograms can accurately predict the survival of patients with PB-DLBCL and help clinicians optimize treatment.

Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.

To establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer. A total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3mm and 5mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA). ITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models. The clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer.

Risk factor analysis and prediction model construction for contralateral central lymph node metastasis in unilateral papillary thyroid carcinoma

ObjectiveTo investigate contralateral central lymph node metastasis (CCLNM) in patients with unilateral papillary thyroid carcinoma (UPTC). To provide a reference for clinical decision-making, a prediction model for the probability of CCLNM was established.MethodThe clinicopathological data of 221 UPTC patients who underwent surgical treatment were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for CCLNM according to clinicopathological characteristics, construct a prediction model to construct a visual nomogram, and evaluate the model.ResultsAccording to univariate and multivariate logistic regression analyses, sex (P = 0.01, OR: 3.790, 95% CI: 1.373–10.465), extrathyroidal tumor extension (ETE) (P = 0.040, OR: 6.364, 95% CI: 1.083–37.381), tumor diameter (P = 0.010, OR: 3.674, 95% CI: 1.372–9.839) and ipsilateral central lymph node metastasis (ICLNM) (P < 0.001, OR: 38.552, 95% CI: 2.675–27.342) were found to be independent risk factors for CCLNM and were used to construct a nomogram for internal verification. The ROC curve had an AUC of 0.852 in the training group and an AUC of 0.848 in the verification group, and the calibration curve indicated that the prediction probability of the model was consistent with the actual probability. Finally, the analysis of the decision curve showed that the model has good application value in clinical decision-making.ConclusionSex, ETE, tumor size, and ICLNM emerged as independent risk factors for CCLNM in UPTC patients. A predictive model was therefore developed, harnessing these variables to enable an objective, personalized estimation of CCLNM risk. This tool offers valuable insights to inform surgical planning and optimize treatment strategies for UPTC management.

Development and validation of a novel nomogram to predict worsening of gastroesophageal reflux symptoms after laparoscopic sleeve gastrectomy using Lasso-logistic regression

Background Gastroesophageal reflux disease (GERD) is among the most common complications of bariatric surgery. This study aimed to analyse the risk factors affecting the worsening of GERD symptoms after laparoscopic sleeve gastrectomy (LSG), and to establish and validate a related nomogram model. Methods The study recruited 236 participants and randomly divided them into training and validation sets in a ratio of 7:3. LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to construct the column line graphs. The performance of the nomogram was evaluated and validated by analyzing the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results In this study, Lasso-logistic regression was applied to select 5 predictors from the relevant variables, which were body mass index (BMI), diabetes, hiatal hernia, GERD, and triglyceride levels. These 5 predictor variables constructed a model with moderate predictive power, with an area under the ROC curve of 0.779 for the training set and 0.796 for the validation set. Decision curve analysis showed that in external validation, if the risk thresholds were between 4 and 98% and 14–95%, then the nomogram can be applied to the clinic. Conclusions We have developed and validated a nomogram that effectively predicts the risk of worsening gastroesophageal reflux symptoms following LSG.

Flow injection chemiluminescence determination of lambda-cyhalothrin and clodinafop-propargyl in freshwater samples

ABSTRACT A flow injection with chemiluminescence (CL) detection system applying tris-2,2′-bipyridylruthenium(II)–potassium permanganate ([Ru(bpy)3]2+–KMnO4) CL reaction was established to determine lambda-cyhalothrin (LCT) and clodinafop-propargyl (CP) pesticides in freshwater samples. Experimental parameters were optimised, and solid phase extraction was employed as phase separation techniques. Linearity of calibration curves for the standard solutions ranges of LCT and CP were of 0.001–1.0 mg/L (y = 1230.3x +23.895, R 2 = 0.9998 (n = 7)) and 0.001–2.0 mg/L (y = 680.12x +18.5, R 2 = 0.9998 (n = 9)), respectively. The respective limits of detection (LODs) and limits of quantification (LOQs) for LCT and CP were found as 3.4 × 10−4 and 3.5 × 10−4 mg/L (3σ blank) and 9.3 × 10−4 and 9.6 × 10−4 mg/L (10σ blank). The injection throughput was 115 h−1. As a consequence of applying tests of significance, the obtained results by the suggested method were not significantly different from those of the reported methods at a 95% confidence level. Recoveries for LCT and CP were achieved over the ranges of 93.8–108% (relative standard deviation (RSD) = 1.7–3.4%) and 93.4–108.2% (RSD = 2.2–3.5%), respectively. The most possible CL reaction mechanism was investigated and formulated.

The correlated factors of anxiety and depression among Chinese hospital staff during the COVID-19 local outbreak.

Hospital staff in the COVID-19 local outbreak were facing different situations, their mental status and influencing factors were also different. The aim of this study is to investigate the anxiety and depression of hospital staff and its potential influence factors during the COVID-19 local outbreak. This was a cross-sectional survey based on a hospital with a local outbreak of COVID-19. We collected the demographic characteristics, COVID-19-related issues, self-rating anxiety scale (SAS), and self-rating depression scale (SDS) of participants through an anonymous questionnaire. Factors associated with anxiety and depression were explored through univariate and multivariate analyses. We also constructed nomograms and calibration curves to predict the probability of anxiety and depression. A total of 800 people completed the questionnaire. 239 (29.9%) of them were doctors, 249 (31.1%) of them were nurses and 312 (39.0%) of them were others. There were 173 (21.6%) cases had anxiety, including 36 (20.8%) doctors, 76 (43.9%) nurses and 61 (35.3%) from other occupations and 281 (35.1%) cases had depression, including 64 (22.8%) doctors, 101 (35.9%) nurses, and 116 (41.3%) from other occupations. Nurses had higher SAS and SDS score than doctors and others (F = 17.856, P < .001 and F = 14.376, P < .001). In addition, multivariate analysis found that occupation, education level, health condition, and reduced sleep were significant influences on anxiety and depression. At the same time, reduced income was also significantly associated with anxiety. During the local outbreak of COVID-19, hospital staff still had varying degrees anxiety and depression. Occupation, education level, health condition and reduced sleep were both significant influencing factors for anxiety and depression. The mental state of hospital staff, including nonmedical-related staff should still be taken seriously.

Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases.

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients. Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90). We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group. Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Preoperative prediction of early recurrence in patients with BRAF mutant colorectal cancer using a intergrated nomogram

Objective To explore the predictive value of radiomics nomogram combining with CT radiomics features and clinical features for postoperative early recurrence in patients with BRAF-mutant colorectal cancer. Methods A total of 220 patients with surgically and pathologically confirmed BRAF-mutant colorectal cancer from 2 institutions were retrospectively included. All patients from institution 1 were randomized at a 7:3 ratio into a training cohort (n = 108) and an internal validation cohort (n = 45), and patients from institution 2 were used as an external validation cohort (n = 67). The association between the radiomics features and early recurrence was assessed in the training cohort and verified in the validation cohort. Furthermore, the performance of the radiomics nomogram was evaluated by combining the rad-score and clinical risk factors. The predictive performance was evaluated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis. Results The dierenees in the Lymphocyte/monocyte ratio (LMR) and Peripheral nerve infiltration (PNI) were statistically significant between the early recurrence in BRAF-mutant colorectal cancer groups and the early non-recurrence in BRAF-mutant colorectal cancer groups (P < 0.05); The two groups showed no significant differenee in clinical parameters including age, sex, and biochemistry serum markers (P > 0.05). Comparing with the pure radiomics or clinical data, combined models can be seen that the addition of LMR and PNI further improveed the predictive efficiency of the model. The rad-score based on LR, generated by 4 selected radiomics features, demonstrated a favorable ability to predict early recurrence in both the training (AUC 0.81), internal validation (AUC 0.73), external validation (AUC 0.63) cohorts. Subsequently, integrating two independent predictors into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.88 and 0.81 in both cohorts. Conclusions The proposed CT-based radiomics signature is associated with early recurrence among the patients with BRAF-mutant colorectal cancer. The present study also proposes a combined model can potentially be applied in the individual preoperative prediction of early recurrence in patients with BRAF-mutant colorectal cancer. Advances in knowledge CT-based radiomics showed satisfactory diagnostic significance for early recurrence in patients with BRAF-mutant colorectal cancer. Key baseline clinical characteristics were associated with early recurrence in patients with BRAF-mutant colorectal cancer. The combined model may be applied in the individual preoperative prediction of early recurrence in patients with BRAF-mutant colorectal cancer.

Construction of Survival Nomogram for Ventilator-Associated Pneumonia Patients: Based on MIMIC Database.

Objective: To construct and validate a predictive nomogram model for the survival of patients with ventilator-associated pneumonia (VAP) to enhance prediction of 28-day survival rate in critically ill patients with VAP. Methods: A total of 1,438 intensive care unit (ICU) patients with VAP were screened through Medical Information Mart for Intensive Care (MIMIC)-IV. On the basis of multi-variable Cox regression analysis data, nomogram performance in predicting survival status of patients with VAP at ICU admission for 7, 14, and 28 days was evaluated using the C-index and area under the curve (AUC). Calibration and decision curve analysis curves were generated to assess clinical value and effectiveness of model, and risk stratification was performed for patients with VAP. Result: Through stepwise regression screening of uni-variable and multi-variable Cox regression models, independent prognostic factors for predicting nomogram were determined, including age, race, body temperature, Sequential Organ Failure Assessment score, anion gap, bicarbonate concentration, partial pressure of carbon dioxide, mean corpuscular hemoglobin, and liver disease. The model had C-index values of 0.748 and 0.628 in the train and test sets, respectively. The receiver operating characteristic curve showed that nomogram had better performance in predicting 28-day survival status in the train set (AUC = 0.74), whereas it decreased in the test set (AUC = 0.66). Calibration and decision curve analysis curve results suggested that nomogram had favorable predictive performance and clinical efficacy. Kaplan-Meier curves showed significant differences in survival between low, medium, and high-risk groups in the total set and training set (log-rank p < 0.05), further validating the effectiveness of the model. Conclusion: The VAP patient admission ICU 7, 14, and 28-day survival prediction nomogram was constructed, contributing to risk stratification and decision-making for such patients. The model is expected to play a positive role in supporting personalized treatment and management of VAP.