Spinal cord injury (SCI) is a rare condition with a heterogeneous presentation, making the prediction of recovery challenging. However, serological markers have been shown to be associated with severity and long-term recovery following SCI. Therefore, our investigation aimed to assess the feasibility of translating this association into a prediction of the lower extremity motor scores (LEMS) at chronic stage (52 weeks after initial injury) in patients with SCI using routine serological markers. Serological markers, assessed within the initial seven days post-injury in the observational cohort study from the Trauma Hospital Murnau underwent diverse feature engineering approaches. These involved arithmetic measurements such as mean, median, minimum, maximum, and range, as well as considerations of the frequency of marker testing and whether values fell within the normal range. To predict LEMS scores at the chronic stage, eight different regression models (including linear, tree-based, and ensemble models) were used to quantify the predictive value of serological markers relative to a baseline model that relied on the very acute LEMS score and patient age alone. The inclusion of serological markers did not improve the performance of the prediction model. The best-performing approach including serological markers achieved a mean absolute error (MAE) of 6.59 (2.14), which was equivalent to the performance of the baseline model. As an alternative approach, we trained separate models based on the LEMS observed at the very acute stage after injury. Specifically, we considered individuals with an LEMS of 0 or an LEMS exceeding zero separately. This strategy led to a mean improvement in MAE across all cohorts and models, of 1.20 (2.13). We conclude that, in our study, routine serological markers hold limited power for prediction of LEMS. However, the implementation of model stratification by the very acute LEMS markedly enhanced prediction performance. This observation supports the inclusion of clinical knowledge in the modeling of prediction tasks for SCI recovery. Additionally, it lays the path for future research to consider stratified analyses when investigating the predictive power of potential biomarkers.

Background: Endometrial cancer is associated with changes in blood cell counts and with high levels of inflammatory markers, thus reflecting the tumor\'s impact on various biological processes and suggesting their potential as biomarkers for endometrial cancer diagnosis, prognosis, and treatment response. The neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in peripheral blood sampled preoperatively from patients have been reported to be independently associated with the prognosis of different types of malignancies. Objectives: This study aimed to compare several blood markers-red blood cells, white blood cells, platelet parameters, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, C-reactive protein, and fibrinogen-in patients with benign or malignant endometrial tumors. Material and methods: Our retrospective study included 670 patients (192 diagnosed with endometrial cancer and 478 with endometrial hyperplasia), and we compared the serological parameters discussed above with those sampled the day before surgery. Results: Analysis of complete blood count indices revealed no significant differences in red blood cell or total white blood cell parameters between the endometrial cancer group and the endometrial hyperplasia group. However, a distinct pattern emerged in the white blood cell differential. The endometrial cancer group showed a statistically significant decrease in lymphocyte count compared with the endometrial hyperplasia group. In contrast, the endometrial cancer group showed significantly higher mean platelet counts and increased mean platelet volume compared with controls. Furthermore, the endometrial cancer group demonstrated a marked inflammatory response, as evidenced by significantly elevated levels of C-reactive protein, fibrinogen, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio compared with the endometrial hyperplasia group. Conclusions: The current research revealed statistically significant differences in multiple serological biomarkers between the two groups. These findings support the initial hypothesis regarding the potential utility of these biomarkers in endometrial cancer diagnosis, prognosis, and treatment response, highlighting the existence of biomarkers affordable for analysis under any health system, regardless of the country\'s level of development.

OBJECTIVE: The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.
METHODS: A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi\'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.
RESULTS: The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.
CONCLUSIONS: Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Background and Objectives: To investigate the role of preoperative albumin-to-alkaline phosphatase ratio (AAPR) in predicting pathologic node-positive (pN+) disease in penile cancer (PC) patients undergoing inguinal lymph node dissection (ILND). Materials and Methods: Clinical data of patients with squamous cell carcinoma (SCC) PC + ILND at a single high-volume institution between 2016 and 2021 were collected and retrospectively analyzed. An AAPR was obtained from preoperative blood analyses performed within 30 days from their scheduled surgery. A ROC curve analysis was used to assess AAPR cutoff, in addition to the Youden Index. Logistic regression analysis was utilized for an odds ratio (OR), 95% confidence interval (CI) calculations, and an estimate of pN+ disease. A p value < 0.05 was considered to be as statistically significant. Results: Overall, 42 PC patients were included in the study, with a mean age of 63.6 ± 12.9 years. The AAPR cut-off point value was determined to be 0.53. The ROC curve analysis reported an AUC of 0.698. On multivariable logistic regression analysis lymphovascular invasion (OR = 5.38; 95% CI: 1.47-9.93, p = 0.022), clinical node-positive disease (OR = 13.68; 95% CI: 4.37-43.90, p < 0.009), and albumin-to-alkaline phosphatase ratio ≤ 0.53 (OR = 3.61; 95% CI: 1.23-12.71, p = 0.032) were predictors of pN+ involvement. Conclusions: Preoperative AAPR may be a potentially valuable prognostic marker of pN+ disease in patients who underwent surgery for PC.

Metabolic dysfunction-associated fatty liver disease is a chronic liver condition associated with metabolic abnormalities characterized by hepatic steatosis that can progress to metabolic-related steatohepatitis, liver fibrosis, cirrhosis, and even hepatocellular carcinoma. Currently, a liver biopsy is still the gold standard for diagnosis but due to its invasiveness and risk of complications, the development of serological diagnostic indicators to achieve non-invasive diagnosis has been a hot research topic in recent years. Herein, well-researched serological non-invasive diagnostic indicators present now for fatty livers are reviewed.
代谢相关脂肪性肝病是以代谢异常相关的肝脂肪变性为特征的慢性肝病，可进展至代谢相关脂肪性肝炎、肝纤维化和肝硬化，甚至肝细胞癌。目前，肝活组织检查仍是诊断的金标准，但由于肝活组织检查的有创性和并发症风险，开发血清学诊断指标以实现无创性诊断是近年来研究的热点。现对目前研究较充分的脂肪肝无创性血清学诊断指标进行综述。.

UNASSIGNED: Non-puerperal mastitis (NPM) accounts for approximately 4-5% of all benign breast lesions. Ultrasound is the preferred method for screening breast diseases; however, similarities in imaging results can make it challenging to distinguish NPM from invasive ductal carcinoma (IDC). Our objective was to identify convenient and objective hematological markers to distinguish NPM from IDC.
UNASSIGNED: We recruited 89 patients with NPM, 88 with IDC, and 86 with fibroadenoma (FA), and compared their laboratory data at the time of admission. LASSO regression, univariate logistic regression, and multivariate logistic regression were used to screen the parameters for construction of diagnostic models. Receiver operating characteristic curves, calibration curves, and decision curves were constructed to evaluate the accuracy of this model.
UNASSIGNED: We found significant differences in routine laboratory data between patients with NPM and IDC, and these indicators were candidate biomarkers for distinguishing between the two diseases. Additionally, we evaluated the ability of some classic hematological markers reported in previous studies to differentiate between NPM and IDC, and the results showed that these indicators are not ideal biomarkers. Furthermore, through rigorous LASSO and logistic regression, we selected age, white blood cell count, and thrombin time to construct a differential diagnostic model that exhibited a high level of discrimination, with an area under the curve of 0.912 in the training set and with 0.851 in the validation set. Furthermore, using the same selection method, we constructed a differential diagnostic model for NPM and FA, which also demonstrated good performance with an area under the curve of 0.862 in the training set and with 0.854 in the validation set. Both of these two models achieved AUCs higher than the AUCs of models built using machine learning methods such as random forest, decision tree, and SVM in both the training and validation sets.
UNASSIGNED: Certain laboratory parameters on admission differed significantly between the NPM and IDC groups, and the constructed model was designated as a differential diagnostic marker. Our analysis showed that it has acceptable efficiency in distinguishing NPM from IDC and may be employed as an auxiliary diagnostic tool.

Inflammatory bowel disease (IBD) is a common gastrointestinal tract disease and can be divided into two major groups: ulcerative colitis (UC) and Crohn\'s disease (CD). These two entities can be diagnosed from a combination of invasive and non-invasive tests as well as a thorough history and physical examination. However, invasive tests are preferred for a definitive diagnosis since the two entities have characteristic features of colonoscopy and biopsy. In this review, the following will be discussed: how non-invasive tests could help detect the presence of IBD, how markers help monitor disease progression, and how the disease responds to treatment. Some of the common markers that are discussed in detail include perinuclear antineutrophil cytoplasmatic antibodies (p-ANCA), anti-Saccharomyces cerevisiae antibodies (ASCA), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), calprotectin, lactoferrin, lipocalin-2 (LCN2), and several other novel markers that are based on bacterial antigens. The best non-invasive tests available for detecting the presence of IBD are serological and fecal markers. Detecting these markers has helped doctors significantly by bringing to their attention the possibility of the presence of IBD. The serological testing can also help distinguish the two forms of IBD since a different combination of markers is elevated in UC and CD. In addition, the symptoms of IBD are non-specific and usually overlap with other gastrointestinal tract disorders, so by finding these serological markers, doctors can proceed with further invasive testing that would give them a definitive diagnosis. That way, invasive testing, such as colonoscopy with biopsy, can be avoided in patients with no suspicion of IBD. The common markers used in the clinical setting to point out the presence of IBD are discussed in detail in this review. Recently, more specific markers derived from bacterial antigens are also used, and their role is discussed, too.

UNASSIGNED: HBV and HCV infections are a significant public health issue in developing countries with weak healthcare systems, high poverty rates, illiteracy, low HBV immunization coverage, and low public health education. A study assessed the sero epidemiology of HBV antigen, anti- HCV markers, biochemical and heamatological indices of 559 participants in Dambam local government during hepatitis day. A structured questionnaire was administered to assess demographic information and risk factors. Rapid latex immunochromtographic kits were used for HBV, HCV, and HBV Combo serological markers, with positive and negative control included in each batch analysis. Descriptive statistics analysis was conducted on the data.
UNASSIGNED: The 559 study participants, had a mean age of 35.5+10.9years, majority within the age- group, 18-39years 279(49.04%), female accounted for 291(52.1%) compared to male 268(47.9), educational background, tertiary 244(43.6%), married, 356(68.7%) and student were 254(45.4%). Seroprevalence of HBsAg was 10.7%, serological markers as follows, HbsAb 1.7%, HbeAg 13.3%, HbeAb 60.0% HbcAb 95.0% and Anti-HCV of 3.4%. Gender breakdown(M vs F) of HBV(13.4% vs 8.2%) and HCV(3.0% vs 3.8%). Significant association was observed in the seroprevalence of HBV and HCV with age-group, gender, marital status and occupation(<0.05). No significant difference was observed with the risk factors of HBV and HCV. Biochemical and heamatological indices showed a significant difference between seropositive and negative study participants(<0.05).
UNASSIGNED: The study\'s findings affirmed the endemicity of HBV infection and the increasing trend of HCV infection in Bauchi state, posing serious public health concerns. HBV serological markers suggest a low HBV immunization coverage rate and exposure of participants to the viral etiology in the community. Strengthening immunization coverage and population-based surveillance is strategic in the prevention and control of viral hepatitis in Bauchi state.

Nonalcoholic fatty liver disease (NAFLD) currently represents one of the most common liver diseases worldwide. Early diagnosis and disease staging is crucial, since it is mainly asymptomatic, but can progress to nonalcoholic steatohepatitis (NASH) or cirrhosis or even lead to the development of hepatocellular carcinoma. Over time, efforts have been put into developing noninvasive diagnostic and staging methods in order to replace the use of a liver biopsy. The noninvasive methods used include imaging techniques that measure liver stiffness and biological markers, with a focus on serum biomarkers. Due to the impressive complexity of the NAFLD\'s pathophysiology, biomarkers are able to assay different processes involved, such as apoptosis, fibrogenesis, and inflammation, or even address the genetic background and \"omics\" technologies. This article reviews not only the currently validated noninvasive methods to investigate NAFLD but also the promising results regarding recently discovered biomarkers, including biomarker panels and the combination of the currently validated evaluation methods and serum markers.

UNASSIGNED: To explore whether machine learning models using serological markers can predict the relapse of Ulcerative colitis (UC).
UNASSIGNED: This clinical cohort study included 292 UC patients, and serological markers were obtained when patients were discharged from the hospital. Subsequently, four machine learning models including the random forest (RF) model, the logistic regression model, the decision tree, and the neural network were compared to predict the relapse of UC. A nomogram was constructed, and the performance of these models was evaluated by accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).
UNASSIGNED: Based on the patients\' characteristics and serological markers, we selected the relevant variables associated with relapse and developed a LR model. The novel model including gender, white blood cell count, percentage of leukomonocyte, percentage of monocyte, absolute value of neutrophilic granulocyte, and erythrocyte sedimentation rate was established for predicting the relapse. In addition, the average AUC of the four machine learning models was 0.828, of which the RF model was the best. The AUC of the test group was 0.889, the accuracy was 76.4%, the sensitivity was 78.5%, and the specificity was 76.4%. There were 45 variables in the RF models, and the relative weight coefficients of these variables were determined. Age has the greatest impact on classification results, followed by hemoglobin concentration, white blood cell count, and platelet distribution width.
UNASSIGNED: Machine learning models based on serological markers had high accuracy in predicting the relapse of UC. The model can be used to noninvasively predict patient outcomes and can be an effective tool for determining personalized treatment plans.