Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma.

BACKGROUND: The growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm. METHODS: Serum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients' survival and TNM status by online databases Kaplan-Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software. RESULTS: We characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets-IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12-found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis. CONCLUSIONS: In conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Construction of a nomogram model for predicting the outcome of debulking surgery for ovarian cancer on the basis of clinical indicators.

Objective: This study aimed to investigate the risk factors affecting satisfaction with debulking surgery for ovarian cancer and establish a preoperative clinical predictive model. Methods: Clinical data from 131 patients who underwent ovarian cancer debulking surgery at Jiangnan University Affiliated Hospital between 2016 and 2022 were collected. Patients were randomly separated into an experimental group and a control group in a 7:3 ratio. On the basis of intraoperative outcomes, patients were grouped as either surgery-satisfactory or surgery-unsatisfactory. Clinical indicators were compared through single-factor analysis between groups. Significantly different factors (p < 0.1) were further analyzed through multivariate logistic regression. A predictive nomogram model was developed and validated by receiver operating characteristic (ROC), calibration, and clinical decision curves. Results: Single-factor analysis revealed the significance of factors such as albumin levels, alkaline phosphatase (ALP), ECOG scores, CA125, HE4, and lymph node metastasis. Multivariate regression analysis identified albumin levels, ALP, ECOG scores, HE4, and lymph node metastasis as independent risk factors for satisfactory surgical outcomes in patients with ovarian cancer undergoing debulking surgery as (p < 0.05). A clinical predictive model was successfully constructed. ROC curves showed AUC values of 0.818 and 0.796 for the experimental and validation groups, respectively. Internal validation through the bootstrap method confirmed the model's fit in both groups. Meanwhile, the clinical decision curve demonstrated the model's high utility. Conclusion: Independent risk factors associated with satisfactory tumor reduction in patients with ovarian cancer undergoing debulking surgery included decreased albumin levels, ALP > 137 U/L, ECOG = 1 score, HE4 > 140 pmol/L, and lymph node metastasis. Constructing a clinical predictive model through logistic regression analysis enables individualized testing and maximizes clinical benefits.

Association of serum trefoil factor 3 and leptin levels with obesity: A case-control study.

BACKGROUND: Obesity has a detrimental impact on individuals, communities, and healthcare systems. Trefoil factor 3 is a secretory protein involved in metabolic processes related to weight regulation. However, its relation with obesity is not fully understood. OBJECTIVE: We aimed to assess the serum trefoil factor 3 level and to immunohistochemical detect the leptin in obese patients to evaluate their relation to obesity pathogenesis. METHODS: As a case-control study, we enrolled 83 non-obese persons as a control group with a BMI (18.5-24.9) and 83 obese persons as a patient group with a BMI > 30. All the study volunteers are subjected to anthropometric measurements, glucose, and lipid profile analysis by colorimetric methods. Serum trefoil factor 3 level was estimated by ELISA and leptin hormone was detected immunohistochemically in the blood using cell block technique. RESULTS: ROC curve analysis for TFF3 showed a good relation with obesity with an AUC of 0.891 and a cut-off value of > 96 ng/ml. There was a significant positive correlation between TFF3 and fasting blood sugar, total cholesterol, and triglycerides. The logistic regression analysis showed that TFF3 is a good risk factor for obesity incidence [p = 0.008; OR = 1.117; (95 % CI): 1.029-1.213]. This was confirmed by multiple linear regression that gave an equation for the possibility of predicting BMI using several factors including TFF3 [BMI = 0.821 + 0.051 × TFF3 + 0.044 × FBS + 0.85 × TC]. The more surprising was the ability of the immunohistochemistry cell block technique to detect leptin antigens associated with an obese person blood not only adipose tissue or serum. CONCLUSION: Leptin hormone and TFF3 could be good indicators for obesity incidence. Further research with a larger sample size and in different populations could completely approve our results.

Canine Cancer Diagnostics by X-ray Diffraction of Claws.

We report the results of X-ray diffraction (XRD) measurements of the dogs' claws and show the feasibility of using this approach for early, non-invasive cancer detection. The obtained two-dimensional XRD patterns can be described by Fourier coefficients, which were calculated for the radial and circular (angular) directions. We analyzed these coefficients using the supervised learning algorithm, which implies optimization of the random forest classifier by using samples from the training group and following the calculation of mean cancer probability per patient for the blind dataset. The proposed algorithm achieved a balanced accuracy of 85% and ROC-AUC of 0.91 for a blind group of 68 dogs. The transition from samples to patients additionally improved the ROC-AUC by 10%. The best specificity and sensitivity values for 68 patients were 97.4% and 72.4%, respectively. We also found that the structural parameter (biomarker) most important for the diagnostics is the intermolecular distance.

An alternative parameterization for the binormal ROC curve, with applications to sizing and simulation studies.

Because the conventional binormal ROC curve parameters are in terms of the underlying normal diseased and nondiseased rating distributions, transformations of these values are required for the user to understand what the corresponding ROC curve looks like in terms of its shape and size. In this paper I propose an alternative parameterization in terms of parameters that explicitly describe the shape and size of the ROC curve. The proposed two parameters are the mean-to-sigma ratio and the familiar area under the ROC curve (AUC), which are easily interpreted in terms of the shape and size of the ROC curve, respectively. In addition, the mean-to-sigma ratio describes the degree of improperness of the ROC curve and the AUC describes the ability of the corresponding diagnostic test to discriminate between diseased and nondiseased cases. The proposed parameterization simplifies the sizing of diagnostic studies when conjectured variance components are used and simplifies choosing the binormal a and b parameter values needed for simulation studies.

Reliability of EuroSCORE II on Prediction of Thirty-Day Mortality and Long-Term Results in Patients Treated with Sutureless Valves.

Background: EuroSCORE II (ES2) is a reliable tool for preoperative cardiac surgery mortality risk prediction; however, a patient's age, a surgical procedure's weight and the new devices available may cause its accuracy to drift. We sought to investigate ES2 performance related to the surgical risk and late mortality estimation in patients who underwent aortic valve replacement (AVR) with sutureless valves. Methods: Between 2012 and 2021, a total of 1126 patients with isolated aortic stenosis who underwent surgical AVR by means of sutureless valves were retrospectively collected from six European centers. Patients were stratified into three groups according to the EuroSCORE II risk classes (ES2 < 4%, ES2 4-8% and ES2 > 8%). The accuracy of ES2 in estimating mortality risk was assessed using the standardized mortality ratio (O/E ratio), ROC curves (AUC) and Hosmer-Lemeshow (HL) test for goodness-of-fit. Results: The overall observed mortality was 3.0% (predicted mortality ES2: 5.39%) with an observed/expected (O/E) ratio of 0.64 (confidential interval (CI): 0.49-0.89). In our population, ES2 showed a moderate discriminating power (AUC 0.65, 95%CI 0.56-0.72, p < 0.001; HL p = 0.798). Good accuracy was found in patients with ES2 < 4% (O/E ratio 0.54, 95%CI 0.23-1.20, AUC 0.75, p < 0.001, HL p = 0.999) and for patients with an age < 75 years (O/E ratio 0.98, 95%CI 0.45-1.96, AUC 0.76, p = 0.004, HL p = 0.762). Moderate discrimination was observed for ES2 in the estimation of long-term risk of mortality (AUC 0.64, 95%CI: 0.60-0.68, p < 0.001). Conclusions: EuroSCORE II showed good accuracy in patients with an age < 75 years and patients with ES2 < 4%, while overestimating risk in the other subgroups. A recalibration of the model should be taken into account based on the complexity of actual patients and impact of new technologies.

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved.

In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.

Diagnostic accuracy, reliability, and construct validity of the German quick mild cognitive impairment screen.

BACKGROUND: Early detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations - the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity. METHODS: We retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of 'mild neurocognitive disorder' (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the 'Brain-IT' project. Using Youden's Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach's α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain. RESULTS: The optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach's α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments. CONCLUSION: Our findings corroborate the existing evidence of the Qmci's good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted.

Comparative evaluation of transverse width indices for diagnosing maxillary transverse deficiency.

OBJECTIVES: This study aimed to compare and evaluate different transverse width indices for diagnosing maxillary transverse deficiency (MTD), a common malocclusion characterized by uncoordinated dental arches, crossbites, and tooth crowding. MATERIALS AND METHODS: Sixty patients aged 7-12 years were included in the study, with 20 patients diagnosed with MTD and 40 normal controls. Transverse width indices, including maxillary width at the buccal alveolar crest and lingual midroot level, as well as at the jugal process width, were measured. Differences between these indices and their corresponding mandibular indices were used as standardized transverse width indices. The reference range of these indices was determined and evaluated. Receiver operating characteristic (ROC) analysis was performed to evaluate their diagnostic ability. RESULTS: The transverse width indices and standardized transverse width indices of the MTD group were significantly smaller than those of the control group, except for the jugal process width. The evaluation of the reference range and ROC analysis revealed that the difference of the maxillomandibular width at buccal alveolar crest was the most accurate diagnostic method. CONCLUSIONS: The jugal point analysis method may not be suitable for diagnosing MTD. Instead, measuring the difference in maxillomandibular width at the buccal alveolar crest proves to be a more reliable and accurate diagnostic method for MTD.

Choroid plexus volume as a novel candidate neuroimaging marker of the Alzheimer's continuum.

BACKGROUND: Enlarged choroid plexus (ChP) volume has been reported in patients with Alzheimer's disease (AD) and inversely correlated with cognitive performance. However, its clinical diagnostic and predictive value, and mechanisms by which ChP impacts the AD continuum remain unclear. METHODS: This prospective cohort study enrolled 607 participants [healthy control (HC): 110, mild cognitive impairment (MCI): 269, AD dementia: 228] from the Chinese Imaging, Biomarkers, and Lifestyle study between January 1, 2021, and December 31, 2022. Of the 497 patients on the AD continuum, 138 underwent lumbar puncture for cerebrospinal fluid (CSF) hallmark testing. The relationships between ChP volume and CSF pathological hallmarks (Aß42, Aß40, Aß42/40, tTau, and pTau181), neuropsychological tests [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Neuropsychiatric Inventory (NPI), and Activities of Daily Living (ADL) scores], and multimodal neuroimaging measures [gray matter volume, cortical thickness, and corrected cerebral blood flow (cCBF)] were analyzed using partial Spearman's correlation. The mediating effects of four neuroimaging measures [ChP volume, hippocampal volume, lateral ventricular volume (LVV), and entorhinal cortical thickness (ECT)] on the relationship between CSF hallmarks and neuropsychological tests were examined. The ability of the four neuroimaging measures to identify cerebral Aß42 changes or differentiate among patients with AD dementia, MCI and HCs was determined using receiver operating characteristic analysis, and their associations with neuropsychological test scores at baseline were evaluated by linear regression. Longitudinal associations between the rate of change in the four neuroimaging measures and neuropsychological tests scores were evaluated on the AD continuum using generalized linear mixed-effects models. RESULTS: The participants' mean age was 65.99 ± 8.79 years. Patients with AD dementia exhibited the largest baseline ChP volume than the other groups (P < 0.05). ChP volume enlargement correlated with decreased Aß42 and Aß40 levels; lower MMSE and MoCA and higher NPI and ADL scores; and lower volume, cortical thickness, and cCBF in other cognition-related regions (all P < 0.05). ChP volume mediated the association of Aß42 and Aß40 levels with MMSE scores (19.08% and 36.57%), and Aß42 levels mediated the association of ChP volume and MMSE or MoCA scores (39.49% and 34.36%). ChP volume alone better identified cerebral Aß42 changes than LVV alone (AUC = 0.81 vs. 0.67, P = 0.04) and EC thickness alone (AUC = 0.81 vs.0.63, P = 0.01) and better differentiated patients with MCI from HCs than hippocampal volume alone (AUC = 0.85 vs. 0.81, P = 0.01), and LVV alone (AUC = 0.85 vs.0.82, P = 0.03). Combined ChP and hippocampal volumes significantly increased the ability to differentiate cerebral Aß42 changes and patients among AD dementia, MCI, and HCs groups compared with hippocampal volume alone (all P < 0.05). After correcting for age, sex, years of education, APOE ε4 status, eTIV, and hippocampal volume, ChP volume was associated with MMSE, MoCA, NPI, and ADL score at baseline, and rapid ChP volume enlargement was associated with faster deterioration in NPI scores with an average follow-up of 10.03 ± 4.45 months (all P < 0.05). CONCLUSIONS: ChP volume may be a novel neuroimaging marker associated with neurodegenerative changes and clinical AD manifestations. It could better detect the early stages of the AD and predict prognosis, and significantly enhance the differential diagnostic ability of hippocampus on the AD continuum.

Sample size calculation for data reliability and diagnostic performance: a go-to review.

Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen's κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen's κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and ß errors. For the most frequent cases, we provide example of software freely available on the Internet.Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology.Key points• Sample size is a concept related to precision and statistical power.• It has ethical implications, especially when patients are exposed to risks.• Sample size should always be calculated before starting a study.• This review offers simple, go-to methods for sample size calculations.

Expression levels and clinical significance of serum miR-19a/CCL20 in patients with acute cerebral infarction.

Acute cerebral infarction (ACI) is a lethal disease whose early diagnosis is critical for treatment. microRNA (miR)-19a targets CC chemokine ligand 20 (CCL20) in myocardial infarction. We investigated the expression patterns of serum miR-19a and CCL20 of ACI patients and assessed their clinical values. Serum samples of 50 healthy subjects and110 ACI patients were collected. Serum levels of miR-19a, CCL20 mRNA, and biochemical indexes were assessed. miR-19a downstream target gene and the binding relationship between miR-19a and CCL20 were predicted and verified. miR-19a and CCL20 mRNA were subjected to correlation and diagnostic efficiency analysis. miR-19a was poorly expressed in the serum of ACI patients, especially in patients with unstable plaque and large infarction. tumor necrosis factor-α, low-density lipoprotein, and platelet/lymphocyte ratio negatively correlated with serum miR-19a level and positively correlated with CCL20. Dual-luciferase assay revealed that miR-19a could negatively regulate CCL20 expression. CCL20 was highly expressed in the serum of ACI patients. The area under receiver-operating characteristic curve of miR-19a combined with CCL20 was 0.9741 (98.00% specificity, 90.91% sensitivity), higher than their single diagnosis. Collectively, miR-19a had high diagnostic value for ACI and could target to restrain CCL20. The combination of miR-19a and CCL20 improved diagnostic value for ACI.

Repeated Sieving for Prediction Model Building with High-Dimensional Data.

Background: The prediction of patients' outcomes is a key component in personalized medicine. Oftentimes, a prediction model is developed using a large number of candidate predictors, called high-dimensional data, including genomic data, lab tests, electronic health records, etc. Variable selection, also called dimension reduction, is a critical step in developing a prediction model using high-dimensional data. Methods: In this paper, we compare the variable selection and prediction performance of popular machine learning (ML) methods with our proposed method. LASSO is a popular ML method that selects variables by imposing an L1-norm penalty to the likelihood. By this approach, LASSO selects features based on the size of regression estimates, rather than their statistical significance. As a result, LASSO can miss significant features while it is known to over-select features. Elastic net (EN), another popular ML method, tends to select even more features than LASSO since it uses a combination of L1- and L2-norm penalties that is less strict than an L1-norm penalty. Insignificant features included in a fitted prediction model act like white noises, so that the fitted model will lose prediction accuracy. Furthermore, for the future use of a fitted prediction model, we have to collect the data of all the features included in the model, which will cost a lot and possibly lower the accuracy of the data if the number of features is too many. Therefore, we propose an ML method, called repeated sieving, extending the standard regression methods with stepwise variable selection. By selecting features based on their statistical significance, it resolves the over-selection issue with high-dimensional data. Results: Through extensive numerical studies and real data examples, our results show that the repeated sieving method selects far fewer features than LASSO and EN, but has higher prediction accuracy than the existing ML methods. Conclusions: We conclude that our repeated sieving method performs well in both variable selection and prediction, and it saves the cost of future investigation on the selected factors.

Exosomal miRNA-21-5p and miRNA-21-3p as key biomarkers of myocardial infarction.

Objective: Coronary artery disease (CAD) is a debilitating condition that can lead to myocardial infarction (MI). Exosomal miRNAs (exo-miRNA) can be diagnostic biomarkers for detecting MI. Here, we conduct a study to evaluate the efficacy of exo-miRNA-21-5p/3p for early detection of MI. Methods: A total of 135 CAD patients and 150 healthy subjects participated in this study. Additionally, we randomly divided 26 male Wistar rats (12 weeks old) into two groups: control and induced MI. Angiographic images were used to identify patients and healthy individuals of all genders. In the following, serum exosomes were obtained, and exo-miRNA-21-5p/3p was measured by reverse-transcriptase polymerase chain reaction. Results: We observed an upregulation of exo-miRNA-21-5p/3p in CAD patient and MI-induced animal groups compared to controls. Analysis of the ROC curves defined 82% and 88% of the participants' exo-miRNA-21-5p and exo-miRNA-21-3p diagnostic power, respectively, which in the animal model was 92 and 82. Conclusion: This study revealed that the mean expression levels of exo-miRNA-21-5p/3p were significantly increased in CAD patients and animal models of induced MI. Also, these results are associated with the atherogenic lipid profile of CAD patients, which may play an important role in the progression of the disease. Therefore, they can be considered as novel biomarkers.

Validation and refinement of a predictive nomogram using artificial intelligence: assessing in-hospital mortality in patients with large hemispheric cerebral infarction.

Background: Large Hemispheric Infarction (LHI) poses significant mortality and morbidity risks, necessitating predictive models for in-hospital mortality. Previous studies have explored LHI progression to malignant cerebral edema (MCE) but have not comprehensively addressed in-hospital mortality risk, especially in non-decompressive hemicraniectomy (DHC) patients. Methods: Demographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group. Results: This study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range. Conclusion: We have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.

The interplay of psychological resilience and adolescent mobile phone addiction in Henan province, China: insights from latent class analysis.

Background: The aim of this study was to classify distinct subgroups of adolescents based on the severity levels of their mobile phone addiction and to investigate how these groups differed in terms of their psychosocial characteristics. We surveyed a total of 2,230 adolescents using three different questionnaires to assess the severity of their mobile phone addiction, stress, anxiety, depression, psychological resilience, and personality. Latent class analysis was employed to identify the subgroups, and we utilized Receiver Operating Characteristic (ROC) curves and multinomial logistic regression for statistical analysis. All data analyses were conducted using SPSS 26.0 and Mplus 8.5. Methods: We classified the subjects into subgroups based on their mobile phone addiction severity, and the results revealed a clear pattern with a three-class model based on the likelihood level of mobile phone addiction (p < 0.05). We examined common trends in psychosocial traits such as age, grade at school, parental education level, anxiety levels, and resilience. ROC analysis of sensitivity versus 1-specificity for various mobile phone addiction index (MPAI) scores yielded an area under the curve (AUC) of 0.893 (95% CI, 0.879 to 0.905, p < 0.001). We also determined diagnostic value indices for potential cutoff points ranging from 8 to 40. The optimal cutoff value for MPAI was found to be >14, which corresponded to the maximum Youden index (Youden index = 0.751). Results: The latent classification process in this research confirmed the existence of three distinct mobile phone user groups. We also examined the psychosocial characteristics that varied in relation to the severity levels of addiction. Conclusion: This study provides valuable insights into the categorization of adolescents based on the severity of mobile phone addiction and sheds light on the psychosocial characteristics associated with different addiction levels. These findings are expected to enhance our understanding of mobile phone addiction traits and stimulate further research in this area.

Steroidomics-Based Screening for Primary Aldosteronism: Impact of antihypertensive Drugs.

BACKGROUND: Diagnosis of primary aldosteronism (PA) is complicated by the need to withdraw antihypertensive medications that interfere with test results, particularly renin. This study examined whether machine learning-based steroid-probability scores offer a renin measurement-independent approach for testing less prone to interference than the aldosterone-to-renin ratio (ARR). METHODS: This prospective multicenter cohort study involved the use of plasma steroidomics and the ARR in 839 patients tested for PA, including 190 with and 578 without PA (71 indeterminate). Receiver operating characteristic curves for steroid-probability scores and the ARR were examined with and without interfering medications. Impacts of individual medications on plasma aldosterone, 18-oxocortisol, 18-hydroxycortisol, steroid-probability scores, renin, and ARRs were examined by multivariable and paired analyses in patients with and without PA. RESULTS: Receiver operating characteristic curves indicated a significant impact of interfering antihypertensive medications on the diagnostic performance of the ARR and minimal impact on steroid-probability scores. Mineralocorticoid receptor antagonists increased plasma aldosterone, 18-oxocortisol, and 18-hydroxycortisol in patients without PA and resulted in false-positive test results for steroid-probability scores and false-negative results for the ARR. Diuretics increased aldosterone, 18-oxocortisol, and steroid-probability scores in patients without PA, whereas angiotensin-converting enzyme inhibitors decreased aldosterone, steroid-probability scores, and ARRs. Beta-adrenoceptor blockers, dihydropyridine calcium channel blockers, and angiotensin receptor blockers had negligible impact on mineralocorticoids and steroid-probability scores. CONCLUSIONS: Among antihypertensive drugs that impact plasma aldosterone, 18-oxocortisol, and 18-hydroxycortisol, mineralocorticoid receptor antagonists stood out as a cause of false-positive results for derived steroid-probability scores. Other antihypertensives have minimal or no impact, an advantage for use of steroid-probability scores over the ARR when those medications cannot be withdrawn. REGISTRATION: URL: https://drks.de/; Unique identifier: DRKS00017084.

Assessment of ID family proteins expression in colorectal cancer of Iraqi patients.

BACKGROUND: Colorectal cancer (CRC) is the second most deathly worldwide and third most common cancer, CRC is a very heterogeneous disease where tumors can form by both environmental and genetic risk factors and includes epigenetic and genetic alternations. Inhibitors of DNA binding proteins (ID) are a class of helix-loop-helix transcription regulatory factors; these proteins are considered a family of four highly preserved transcriptional regulators (ID1-4), shown to play significant roles in many processes that are associated with tumor development. ID family plays as negatively dominant antagonists of other essential HLH proteins, concluding the creation of non-functional heterodimers and regulation of the transcription process. MATERIALS AND METHODS: 120 Fresh tissue and blood samples Forty (40) samples of fresh tissue and blood were collected from patients diagnosed with CRC, twenty (20) samples were collected from a patient diagnosed as healthy. The (qRT-PCR) method is a sensitive technique for the quantifying of steady-state mRNA levels that used to evaluation the expression levels of ID (1-4) gene. RESULTS: The findings indicate downregulation in ID1 in tissue with a highly significant change between patients and control groups, where upregulation in the ID1 gene is shown in blood samples.ID2 gene also demonstrated high significant change where show upregulation in tissue and downregulation in blood sample. ID3 and ID4 genes show downregulation in tissue and blood samples with a significant change in ID3 blood samples between patient and blood groups. CONCLUSION: Because of the regulation function of the ID family in many processes, the up or down regulation of IDs genes in tumors Proves how important its tumor development, and therefore those proteins can be used as an indicator for CRC.

Cytokine levels in gingival tissues as an indicator to understand periodontal disease severity.

Cytokines regulate periodontal pathogenesis and are relevant estimates of current disease activity. There is sparse information on status of cytokine protein levels in periodontal pocket (gingival) tissues. The current study analysed proteins and transcripts of selected cytokines in varying severity of periodontal disease and elucidated cytokine/cytokine ratios that best indicated periodontal disease severity, in gingival tissues. A total of 92 participants comprising of generalised moderate periodontitis (GMP, n = 18), generalised severe periodontitis (GSP, n = 46) and periodontally healthy controls (PHC, n = 25) were recruited for the study. Interproximal gingival tissue samples were utilised for cytokine protein estimation and mRNA quantification by qRT-PCR and ELISA respectively. Selected key pro and anti-inflammatory cytokines, also representative of various Th subsets were analysed. ROC curve analysis was performed and Youden index was calculated for individual cytokines and pro/anti-inflammatory cytokine ratio to estimate the best indicator of periodontal severity/progression in tissues. IL-1ß, TGF-ß and IFN-γ cytokine protein levels varied significantly (p ≤ 0.05) with severity of periodontal disease between groups. On comparison between deep and shallow sites within same participant, deep sites showed significant elevation of TGF-ß (p ≤ 0.01) and IFN-γ (p ≤ 0.05) and IL-17 cytokines and shallow sites showed elevation of IL-4(p ≤ 0.01) and IL-1ß (p ≤ 0.05) cytokines. Analysis of transcripts showed IFN-γ and IL-1ß transcript predominance in GSP (p = 0.01) compared to PHC. ROC analysis illustrated 97% sensitivity, 93% specificity with Youden index of 90% for IL-1ß cytokine and 81%sensitivity, 79% specificity with a Youden index of 60% for IL-1ß/TGF-ß ratio In periodontal pocket tissue, a lack of distinct predominance of specific cytokines between study groups or between shallow and deep sites affected by periodontal disease was observed. However, ROC analysis of cytokines revealed IL-1ß cytokine and IL-1ß/TGF-ß ratio as promising indicators of periodontal disease severity in gingival tissues.