Introduction: Ginger (Zingiber officinale Roce.) is a widely consumed food item and a prominent traditional Chinese medicinal herb. The intrinsic quality of ginger may differ due to variations in its origin and processing techniques. To evaluate the quality of ginger, a straightforward and efficient discriminatory approach has been devised, utilizing 6-gingerol, 8-gingerol, and 10-gingerol as benchmarks. Methods: In order to categorize ginger samples according to their cultivated origins with different longitude and latitude (Shandong, Anhui, and Yunnan provinces in China) and processing methods (liquid nitrogen pulverization, ultra-micro grinding, and mortar grinding), similarity analysis (SA), hierarchical cluster analysis (HCA), and principal component analysis (PCA) were employed. Furthermore, there was a quantitative determination of the significant marker compounds gingerols, which has considerable impact on maintaining quality control and distinguishing ginger products accurately. Moreover, discrimination analysis (DA) was utilized to further distinguish and classify samples with unknown membership degrees based on the eigenvalues, with the aim of achieving optimal discrimination between groups. Results: The findings obtained from the high-performance liquid chromatography (HPLC) data revealed that the levels of various gingerols present in all samples exhibited significant variations. The study confirmed that the quality of ginger was primarily influenced by its origin and processing method, with the former being the dominant factor. Notably, the sample obtained from Anhui province and subjected to liquid nitrogen pulverization demonstrated the highest content of gingerols. Conclusion: The results obtained from the analysis of SA, HCA, PCA, and DA were consistent and could be employed to evaluate the quality of ginger. As such, the combination of HPLC fingerprints and chemo metric techniques provided a dependable approach for comprehensively assessing the quality and processing of ginger.

The first 2 years of life are a critical window of opportunity for ensuring optimal child growth and development. In Ethiopia, the magnitude of the minimum acceptable diet ranges from 7 to 74⋅6 %. The evidence revealed the variation and unrelated data on the prevalence of minimum acceptable diet. Therefore, the present study aimed to assess the minimum acceptable diet and its associated factors among children aged 6-23 months in Lalibela town administration, northeast Ethiopia. A community-based cross-sectional study was conducted in Lalibela town administration, northeast Ethiopia among 387 mothers/caregivers with children aged 6-23 months from May 1 to 30, 2022. The data were entered by Epidata version 3.1 and analysed by SPSS version 25.0. A multivariable binary logistic regression model was fitted to identify factors associated with minimum acceptable diet. The degrees of association were assessed using an adjusted odds ratio with a 95 % confidence interval and P-value of 0⋅05. The magnitude of minimum acceptable diet in the study area was 16⋅7 % (95 % confidence interval: 12⋅8-20⋅6 %). Sex of child, getting infant and young child feeding counselling at antenatal care, infant feeding practice-related knowledge and childhood illness are the variables that were found to be an independent predictor of minimum acceptable diet. Health facilities should strengthen infant feeding counselling starting from antenatal care visits during pregnancy for the recommended minimum acceptable diet is crucial.

The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent \'imaging\', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.

UNASSIGNED: Although extensive experimental evidence on the process of liver regeneration exists, in humans, validation is largely missing. However, liver regeneration is critically affected by underlying liver disease. Within this project, we aimed to systematically assess early transcriptional changes during liver regeneration in humans and further assess how these processes differ in people with dysfunctional liver regeneration.
UNASSIGNED: Blood samples of 154 patients and intraoperative tissue samples of 46 patients undergoing liver resection were collected and classified with regard to dysfunctional postoperative liver regeneration. Of those, a matched cohort of 21 patients were used for RNA sequencing. Samples were assessed for circulating cytokines, gene expression dynamics, intrahepatic neutrophil accumulation, and spatial transcriptomics.
UNASSIGNED: Individuals with dysfunctional liver regeneration demonstrated an aggravated transcriptional inflammatory response with higher intracellular adhesion molecule-1 induction. Increased induction of this critical leukocyte adhesion molecule was associated with increased intrahepatic neutrophil accumulation and activation upon induction of liver regeneration in individuals with dysfunctional liver regeneration. Comparing baseline gene expression profiles in individuals with and without dysfunctional liver regeneration, we found that dual-specificity phosphatase 4 (DUSP4) expression, a known critical regulator of intracellular adhesion molecule-1 expression in endothelial cells, was markedly reduced in patients with dysfunctional liver regeneration. Mimicking clinical risk factors for dysfunctional liver regeneration, we found liver sinusoidal endothelial cells of two liver disease models to have significantly reduced baseline levels of DUSP4.
UNASSIGNED: Exploring the landscape of early transcriptional changes of human liver regeneration, we observed that people with dysfunctional regeneration experience overwhelming intrahepatic inflammation. Subclinical liver disease might account for DUSP4 reduction in liver sinusoidal endothelial cells, which ultimately primes the liver for an aggravated inflammatory response.
UNASSIGNED: Using a unique human biorepository, focused on liver regeneration (LR), we explored the landscape of circulating and tissue-level alterations associated with both functional and dysfunctional LR. In contrast to experimental animal models, people with dysfunctional LR demonstrated an aggravated transcriptional inflammatory response, higher intracellular adhesion molecule-1 (ICAM-1) induction, intrahepatic neutrophil accumulation and activation upon induction of LR. Although inflammatory responses appear rapidly after liver resection, people with dysfunctional LR have exaggerated inflammatory responses that appear to be related to decreased levels of LSEC DUSP4, challenging existing concepts of post-resectional LR.

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83-71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS\' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments.

Short-chain fatty acids (SCFAs) exhibit anticancer activity in cellular and animal models of colon cancer. Acetate, propionate, and butyrate are the three major SCFAs produced from dietary fiber by gut microbiota fermentation and have beneficial effects on human health. Most previous studies on the antitumor mechanisms of SCFAs have focused on specific metabolites or genes involved in antitumor pathways, such as reactive oxygen species (ROS) biosynthesis. In this study, we performed a systematic and unbiased analysis of the effects of acetate, propionate, and butyrate on ROS levels and metabolic and transcriptomic signatures at physiological concentrations in human colorectal adenocarcinoma cells. We observed significantly elevated levels of ROS in the treated cells. Furthermore, significantly regulated signatures were involved in overlapping pathways at metabolic and transcriptomic levels, including ROS response and metabolism, fatty acid transport and metabolism, glucose response and metabolism, mitochondrial transport and respiratory chain complex, one-carbon metabolism, amino acid transport and metabolism, and glutaminolysis, which are directly or indirectly linked to ROS production. Additionally, metabolic and transcriptomic regulation occurred in a SCFAs types-dependent manner, with an increasing degree from acetate to propionate and then to butyrate. This study provides a comprehensive analysis of how SCFAs induce ROS production and modulate metabolic and transcriptomic levels in colon cancer cells, which is vital for understanding the mechanisms of the effects of SCFAs on antitumor activity in colon cancer.

UNASSIGNED: Dysregulation of long noncoding RNAs (lncRNAs) has been reported to be associated with multiple tumors where they act as tumor suppressors or accelerators. The lncRNA CYTOR was identified as an oncogene involved in many cancers, such as gastric cancer, colorectal cancer, hepatocellular carcinoma, and renal cell carcinoma. However, the role of CYTOR in bladder cancer (BCa) has rarely been reported.
UNASSIGNED: Using cancer datasets from The Cancer Genome Atlas (TCGA) program, we analyzed the association between CYTOR expression and prognostic value, oncogenic pathways, antitumor immunity and immunotherapy response in BCa. The influence of CYTOR on the immune infiltration pattern in the urothelial carcinoma microenvironment was further verified in our dataset. Single-cell analysis revealed the role of CYTOR in the tumor microenvironment (TME) of BCa. Finally, we evaluated the expression of CYTOR in BCa in the Peking University First Hospital (PKU-BCa) dataset and its correlation with the malignant phenotype of BCa in vitro and in vivo.
UNASSIGNED: The results indicated that CYTOR was highly expressed in multiple cancer samples, including BCa, and increased CYTOR expression contributed to poor overall survival (OS). Additionally, elevated CYTOR expression was significantly correlated with clinicopathological features of BCa, such as female sex, advanced TNM stage, high histological grade and non-papillary subtype. Functional characterization revealed that CYTOR may be involved in immune-related pathways and the epithelial mesenchymal transformation (EMT) process. Moreover, CYTOR had a significant association with infiltrating immune cells, including M2 macrophages and regulatory T cells (Tregs). CYTOR facilitates the crosstalk between cancer-associated fibroblasts (CAFs) and macrophages, and mediates M2 polarization of macrophages. Correlation analysis revealed a positive correlation between CYTOR expression and programmed cell death-1 (PD-1)/programmed death ligand 1 (PD-L1)/expression and other targets for specific immunotherapy in BCa, which are recognized to predict the efficacy of immunotherapy.
UNASSIGNED: These results suggest that CYTOR serves as a potential biomarker for predicting survival outcome, TME cell infiltration characteristics and immunotherapy response in BCa.

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.