Introduction: Patients with chronic pancreatitis (CP) as well as with pancreatic head carcinoma (CA) undergo the surgical intervention named \"pylorus-preserving pancreatoduodenectomy according to Traverso-Longmire (PPPD)\", which allowed a comparative analysis of the postoperative courses. The hypothesis was that patients with CA would have worse general as well as immune status than patients with CP due to the severity of the tumor disease and that this would be reflected in the more disadvantageous early postoperative outcome after PPPD. Methods: With the aim of eliciting the influence of the different diagnoses, the surgical outcome of all consecutive patients who underwent surgery at the Dept. of General, Abdominal, Vascular and Transplant Surgery at the University Hospital at Magdeburg between 2002 and 2015 (inclusion criterion) was recorded and comparatively evaluated. Early postoperative outcome was characterized by general and specific complication rate indicating morbidity, mortality, and microbial colonization rate, in particular surgical site infection (SSI, according to CDC criteria). In addition, microbiological findings of swabs and cultures from all compartments as well as preoperative and perioperative parameters from patient records were retrospectively documented and used for statistical comparison in this systematic retrospective unicenter observational study (design). Results: In total, 192 cases with CA (68.1%) and 90 cases with CP (31.9%) met the inclusion criteria of this study. Surprisingly, there were similar specific complication rates of 45.3% (CA) vs. 45.6% (CP; p = 0.97) and in-hospital mortality, which differed only slightly at 3.65% (CA) vs. 3.3% (CP; p = 0.591); the overall complication rate tended to be higher for CA at 23.4% vs. 14.4% (CP; p = 0.082). Overall, potentially pathogenic germs were detected in 28.9% of all patients in CP compared to 32.8% in CA (p = 0.509), and the rate of SSI was 29.7% (CA) and 24.4% (CP; p = 0.361). In multivariate analysis, CA was found to be a significant risk factor for the development of SSI (OR: 2.025; p = 0.048); the underlying disease had otherwise no significant effect on early postoperative outcome. Significant risk factors in the multivariate analysis were also male sex for SSI and microbial colonization, and intraoperatively transfused red cell packs for mortality, general and specific complications, and surgical revisions. Conclusions: Based on these results, a partly significant, partly trending negative influence of the underlying disease CA, compared to CP, on the early postoperative outcome was found, especially with regard to SSI after PPPD. This influence is corroborated by the international literature.

OBJECTIVE: This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes.
METHODS: With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost.
RESULTS: During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction.
CONCLUSIONS: The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies.
CONCLUSIONS: This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Multiple myeloma (MM) is an incurable hematological cancer. It is preceded by monoclonal gammopathy of uncertain significance (MGUS)-an asymptomatic phase. It has been demonstrated that early detection increases the 5-year survival rate. However, blood-based biomarkers that enable early disease detection are lacking. Metabolomic and lipoprotein subfraction variable profiling is gaining traction to expand our understanding of disease states and, more specifically, for identifying diagnostic markers in patients with hematological cancers. This study aims to enhance our understanding of multiple myeloma (MM) and identify candidate metabolites, allowing for a more effective preventative treatment. Serum was collected from 25 healthy controls, 20 patients with MGUS, and 30 patients with MM. 1H-NMR (Nuclear Magnetic Resonance) spectroscopy was utilized to evaluate serum samples. The metabolite concentrations were examined using multivariate, univariate, and pathway analysis. Metabolic profiles of the MGUS patients revealed lower levels of alanine, lysine, leucine but higher levels of formic acid when compared to controls. However, metabolic profiling of MM patients, compared to controls, exhibited decreased levels of total Apolipoprotein-A1, HDL-4 Apolipoprotein-A1, HDL-4 Apolipoprotein-A2, HDL Free Cholesterol, HDL-3 Cholesterol and HDL-4 Cholesterol. Lastly, metabolic comparison between MGUS to MM patients primarily indicated alterations in lipoproteins levels: Total Cholesterol, HDL Cholesterol, HDL Free Cholesterol, Total Apolipoprotein-A1, HDL Apolipoprotein-A1, HDL-4 Apolipoprotein-A1 and HDL-4 Phospholipids. This study provides novel insights into the serum metabolic and lipoprotein subfraction changes in patients as they progress from a healthy state to MGUS to MM, which may allow for earlier clinical detection and treatment.

OBJECTIVE: Currently, we have a shortage of comprehensive information about newer antiepileptic drugs (AEDs) in the pediatric population. This might explain the discrepancies among pediatricians\' preferences in this regard. Therefore, it is crucial to study the multifaceted impacts of these drugs on children. The endpoints of our study were non-AED predictors of the requirement of combination therapy for seizure management, seizure-free period >6 months and >12 months, change in Quality of Life in Childhood Epilepsy Questionnaire - 55 (QOLCE-55), and incidence of adverse events.
METHODS: This prospective, observational study was conducted in KIMS, Bhubaneswar, India, from January 2021 to November 2022. Children of 2-12 years of age were treated with monotherapy of either newer antiepileptics, e.g., levetiracetam, topiramate, and oxcarbazepine or older antiepileptics, e.g., valproic acid, phenytoin, phenobarbital, and carbamazepine. Univariate and multivariate analyses were performed for the assessment of predictors. We used R software (version 4.1.1) for data analysis.
RESULTS: One hundred and ninety-eight (91.7%) of 216 enrolled participants completed this study. The mean age of the study population was 5.2 years and 117 (59%) of them were males. The univariate analysis showed that male gender, low birth weight, preterm birth, assisted vaginal delivery and site-specific epilepsy, and maternal history of epilepsy were significant predictors of combination therapy and reduced seizure-free period. There was a non-significant difference regarding the improvement of QOLCE-55 scores. None of the adverse events were serious.
CONCLUSIONS: Perinatal complications and maternal history of epilepsy contribute significantly toward the efficacy of antiepileptics. However, multivariate analysis did not yield statistically significant results.

Psychotic symptoms are prevalent in patients with bipolar disorder (BD). However, nearly all previous studies on differences in sociodemographic and clinical factors between patients with (BD P +) and without (BD P-) psychotic symptoms were conducted in Western populations, and limited information is known in China.
A total of 555 patients with BD from seven centers across China were recruited. A standardized procedure was used to collect patients\' sociodemographic and clinical characteristics. The patients were divided into BD P + or BD P- groups based on the presence of lifetime psychotic symptoms. Mann-Whitney U test or chi-square test was used to analyze differences in sociodemographic and clinical factors between patients with BD P + and BD P-. Multiple logistic regression analysis was conducted to explore factors that were independently correlated with psychotic symptoms in BD. All the above analyses were re-conducted after the patients were divided into BD I and BD II group according to their types of diagnosis.
A total of 35 patients refused to participate, and the remaining 520 patients were included in the analyses. Compared with patients with BD P-, those with BD P + were more likely to be diagnosed with BD I and mania/hypomania/mixed polarity in the first mood episode. Moreover, they were more likely to be misdiagnosed as schizophrenia than major depressive disorder, were hospitalized more often, used antidepressants less frequently, and used more antipsychotics and mood stabilizers. Multivariate analyses revealed that diagnosis of BD I, more frequent misdiagnosis as schizophrenia and other mental disorders, less frequent misdiagnosis as major depressive disorder, more frequent lifetime suicidal behavior, more frequent hospitalizations, less frequent use of antidepressants, more frequent use of antipsychotics and mood stabilizers were independently correlated with psychotic symptoms in BD. After dividing the patients into BD I and BD II groups, we observed notable differences in sociodemographic and clinical factors, as well as clinicodemographic correlates of psychotic features between the two groups.
Differences in clinical factors between patients with BD P + and BD P- showed cross-cultural consistency, but results on the clinicodemographic correlates of psychotic features were not. Notable differences between patients with BD I and BD II were found. Future work exploring the psychotic features of BD needs to take types of diagnosis and cultural differences into consideration.
This study was first registered on the website of the ClinicalTrials.gov ( https://clinicaltrials.gov/ ) on 18/01/2013. Its registration number is NCT01770704.

This study uses three distinct models to analyse a univariate time series of data: Holt\'s exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5 years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction.

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

(1) Background: Affective distress can be triggered by aggressive stimuli with an unfavorable role for the individual. Some of the factors that lead to the development and evolution of a mental disorder can be genetic. The aim of this study is to determine some correlations between the human leukocyte antigen (HLA) genes and the affective distress profile (PDA). (2) Methods: A psychological assessment and testing tool for anxiety was applied to 115 people. The low-resolution HLA alleles of class I (HLA-A, HLA-B, and HLA-C) and class II (HLA-DRB1 and HLA-DQB1) were identified by the PCR technique after DNA extraction from the blood. Depending on the PDA, the subjects were divided into two groups: a group with a low PDA and another one with a medium and high PDA. The IBM SPSS software was used to compare the frequency of HLA alleles between the two groups. (3) Results: The univariate analysis revealed a significant association of the HLA-A locus (A*01, A*30), HLA-B (B*08), and HLA-DRB1 (DRB1*11) with the low PDA group and of the HLA-A locus (A*32), HLA-B (B*52), and HLA-C (C*12) with the medium and high PDA group. (4) Conclusions: The present study highlighted potential associations between HLA alleles and anxiety disorders.

Hepatocellular carcinoma (HCC) remains an incurable malignancy despite the treatment methods being continually updated. Matrix metalloproteinases (MMPs) promote the progression of HCC; however, no consensus exists on which MMP plays the predominant role in HCCs. In the present study, we analyzed differentially expressed genes in HCCs, especially MMPs, compared with adjacent tissues using the Cancer Genome Atlas database. The KEGG enrichment pathway using differentially expressed genes included extracellular matrix-receptor interaction, which was correlated with MMPs. We found that among the MMP family, only MMP1, MMP3, MMP8, MMP9, MMP11, MMP12, MMP14, MMP15, MMP20, MMP21, and MMP24 significantly increased in HCCs compared with adjacent tissues. Crucially, survival and univariate analyses indicated that only MMPs 1, 9, 12, and 14 predict poor overall survival; however, multivariate Cox analysis and a nomogram demonstrated that only MMP1 is a poor prognostic biomarker for HCCs. In addition, we observed significant enrichment of uncharacterized cells but decreased macrophages in HCC tissues. Consistent with decreased macrophages in HCCs, MMP1 was negatively associated with macrophages but positively correlated with uncharacterized cells, indicating that the main producer of MMP1 is uncharacterized cells. Furthermore, MMP1 expression was negatively correlated with immune responses of HCCs. Taken together, our findings indicated that MMP1 is a poor and predominant prognostic biomarker for patients with HCC and that anti-MMP1 may be a novel therapy that is worth studying in depth.

The high performance and stability of wheat genotypes for yield, grain protein content (GPC), and other desirable traits are critical for varietal development and food and nutritional security. Likewise, the genotype by environment (G × E) interaction (GEI) should be thoroughly investigated and favorably utilized whenever genotype selection decisions are made. The present study was planned with the following two major objectives: 1) determination of GEI for some advanced wheat genotypes across four locations (Ludhiana, Ballowal, Patiala, and Bathinda) of Punjab, India; and 2) selection of the best genotypes with high GPC and yield in various environments. Different univariate [Eberhart and Ruessll\'s models; Perkins and Jinks\' models; Wrike\'s Ecovalence; and Francis and Kannenberg\'s models], multivariate (AMMI and GGE biplot), and correlation analyses were used to interpret the data from the multi-environmental trial (MET). Consequently, both the univariate and multivariate analyses provided almost similar results regarding the top-performing and stable genotypes. The analysis of variance revealed that variation due to environment, genotype, and GEI was highly significant at the 0.01 and 0.001 levels of significance for all studied traits. The days to flowering, plant height, spikelets per spike, grain per spike, days to maturity, and 1000-grain weight were specifically affected by the environment, whereas yield was mainly affected by the environment and GEI. Genotypes, on the other hand, had a greater impact on the GPC than environmental conditions. As a result, a multi-environmental investigation was necessary to identify the GEI for wheat genotype selection because the GEI was very significant for all of the evaluated traits. Yield, 1000-grain weight, spikelet per spike, and days to maturity were observed to have positive correlations, implying the feasibility of their simultaneous selection for yield enhancement. However, GPC was observed to have a negative correlation with yield. Patiala was found to be the most discriminating environment for both yield and GPC and also the most effective representative environment for GPC, whereas Ludhiana was found to be the most effective representative environment for yield. Eventually, two NILs (BWL7508, and BWL7511) were selected as the top across all environments for both yield and GPC.