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Disease-related malnutrition is a prevalent issue among cancer patients, affecting approximately 40-80% of those undergoing treatment. This condition is associated with numerous adverse outcomes, including extended hospitalization, increased morbidity and mortality, delayed wound healing, compromised muscle function and reduced overall quality of life. Moreover, malnutrition significantly impedes patients' tolerance of various cancer therapies, such as surgery, chemotherapy, and radiotherapy, resulting in increased adverse effects, treatment delays, postoperative complications, and higher referral rates. At present, numerous countries and regions have developed objective assessment models to predict the risk of malnutrition in cancer patients. As advanced technologies like artificial intelligence emerge, new modeling techniques offer potential advantages in accuracy over traditional methods. This article aims to provide an exhaustive overview of recently developed models for predicting malnutrition risk in cancer patients, offering valuable guidance for healthcare professionals during clinical decision-making and serving as a reference for the development of more efficient risk prediction models in the future.

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OBJECTIVES: The study aimed to investigate the relationship between serum 25-hydroxyvitamin D (25(OH)D) concentrations and obstructive sleep apnoea (OSA) and to assess the confounding effect of body mass index (BMI) on this relationship. DESIGN: This was a cross-sectional analysis using data from the 2007-08 National Health and Nutrition Examination Survey (NHANES). SETTING: Data were sourced from NHANES, a continuous survey sponsored by the Centres for Disease Control and Prevention, covering residents from 15 urban areas in the United States of America(USA). PARTICIPANTS: The study included 4901 participants aged 16 years and older who had completed 25(OH)D data and responses to the OSA questionnaire. MAIN EXPOSURE MEASURE: Serum 25(OH)D concentrations were measured using liquid chromatography-tandem mass spectrometry. MAIN OUTCOME MEASURE: The primary outcome was the self-reported diagnosis of OSA from questionnaires. RESULTS: After adjusting for age, sex and race (model 1), a significant negative association was observed between 25(OH)D and OSA (ß=-3.21, 95% CI: -6.17 to -0.26). However, this association was no longer significant after further adjustment for BMI (model 2) (ß=1.47, 95% CI: -1.48, 4.42). In the fully adjusted model (model 3), there was no significant association between 25(OH)D and OSA (ß=0.92, 95% CI: -1.93, 3.76). Subgroup analyses stratified by sex, age, race or BMI also revealed no significant associations between 25(OH)D and OSA. CONCLUSIONS: The study found no significant association between 25(OH)D and OSA. The observed correlation between lower levels of 25(OH)D and OSA may be due to confounding factors, such as higher BMI in the OSA group. Therefore, improving obesity management in OSA patients may be necessary to prevent 25(OH)D insufficiency. This underscores the importance of comprehensive management of both OSA and obesity to promote optimal health outcomes.

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[This corrects the article DOI: 10.3389/fpubh.2024.1378041.].

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Although the relationship between vaginal microorganisms and fertility has been well established, only few studies have investigated vaginal microorganisms in women undergoing in vitro fertilization (IVF). Our aim was to study the differences in vaginal microbiota between infertile women with repeated implantation failure (RIF) and those who achieved clinical pregnancy in their first frozen embryo transfer cycle. We compared the vaginal microbiota of patients with a history of RIF (n = 37) with that of the control group (n = 43). Following DNA extraction, metagenomic sequencing was employed for the analysis of alpha and beta diversities, distinctions in bacterial species, and the functional annotation of microbial genes. Furthermore, disparities between the two groups were revealed. Alpha diversity analysis revealed that the Shannon index was higher in the RIF group (P < 0.05). There were differences in the beta diversity between groups (P = 0.16). At the bacterial family level, the relative abundance of Actinomycetaceae (P = 0.013) and Ruminococcaceae (P = 0.013) were significantly higher in the RIF group. At the genus level, the abundances of Actinomyces (P = 0.028) and Subdoligranulum (P = 0.013) were significantly higher in the RIF group. At the species level, the abundances of Prevotella timonensis (P = 0.028), Lactobacillus jensenii (P = 0.049), and Subdoligranulum (P = 0.013) were significantly higher in the RIF group. Significant differences in family, genus, species, alpha and beta diversity were observed in the vaginal microbiota between groups. Notably, among these findings, the Subdoligranulum genus emerged as the most prominent correlating factor.

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Background: Previous cohort studies conducted on large populations have suggested a potential association between obstructive sleep apnea (OSA) and an elevated risk of developing lung cancer. However, limited research has comprehensively investigated the correlation between the two conditions, and the causal effect remains unknown. Methods: A comprehensive and systematic search was conducted across various databases, including PubMed, Web of Science, Cochrane Library, and Embase, from their inception dates to November 1, 2023. To assess the relationship between OSA and lung cancer, a meta-analysis was performed. Additionally, a two-sample Mendelian randomization (MR) study was conducted using summary data. The datasets included 336,659 individuals from the FinnGen study for OSA and 27,209 individuals from the International Lung Cancer Consortium study, as well as 420,473 individuals from the UK Biobank study for lung cancer. The estimates from each study were aggregated using the inverse variance-weighted method. Results: Data from six population-based cohort studies, encompassing 6,589,725 individuals, indicated a significant increase in the risk of developing lung cancer among patients with OSA (HR 1.28, 95% CI 1.07-1.54). However, the MR analysis did not support a causal relationship between OSA and lung cancer (OR 1.001, 95% CI 0.929-1.100). This lack of association was consistent across specific subtypes of lung cancer, including non-small-cell lung cancer (OR 1.000, 95% CI 0.999-1.000, p = 0.974), lung adenocarcinoma (OR 0.996, 95% CI 0.906-1.094, p = 0.927), and squamous cell lung carcinoma (OR 1.034, 95% CI 0.937-1.140, p = 0.507). Conclusions: Our meta-analysis findings suggest an elevated risk of lung cancer among individuals with OSA. However, the MR analysis did not provide evidence supporting a causal relationship between OSA and lung cancer. Further investigation is required to uncover the underlying factors contributing to the observed association between OSA and lung cancer risk.

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Objectives: The Patient-Generated Subjective Global Assessment (PG-SGA) serves as a specialized nutritional assessment instrument designed for cancer patients. Despite its specificity, the complexity and time requirements of this tool, along with the necessity for administration by trained professionals, limit its practicality in clinical settings. Our objective is to identify a straightforward, efficient, and dependable nutritional assessment tool to promote broader adoption in clinical practice. Methods: This study encompassed a total of 450 patients diagnosed with cancer. Of these, 315 individuals constituted the training set, and the remaining 135 were allocated to the external validation set. The model variables were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Binary logistic regression outcomes facilitated the development of a nomogram, offering a visual depiction of the predicted probabilities. The predictive accuracy of the nomogram model was evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve. Results: The LASSO method detected four variables that were included in the final prediction model: age, serum albumin levels (ALB), body mass index (BMI), and activities of daily living (ADL). The area under the curve (AUC) for this prediction model was 0.905. Both the internal and external calibration curves for malnutrition showed that the predictive nomogram model was highly accurate. Conclusion: The study has developed a prediction model that demonstrates remarkable accuracy in forecasting malnutrition. Furthermore, it presents a streamlined nutritional assessment tool aimed at swiftly identifying cancer patients at nutritional risk, thereby facilitating oncologists in delivering targeted nutritional support to these individuals.

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Single-cell RNA sequencing technology (scRNA-seq) has been steadily developing since its inception in 2009. Unlike bulk RNA-seq, scRNA-seq identifies the heterogeneity of tissue cells and reveals gene expression changes in individual cells at the microscopic level. Here, we review the development of scRNA-seq, which has gone through iterations of reverse transcription, in vitro transcription, smart-seq, drop-seq, 10 × Genomics, and spatial single-cell transcriptome technologies. The technology of 10 × Genomics has been widely applied in medicine and biology, producing rich research results. Furthermore, this review presents a summary of the analytical process for single-cell transcriptome data and its integration with other omics analyses, including genomes, epigenomes, proteomes, and metabolomics. The single-cell transcriptome has a wide range of applications in biology and medicine. This review analyzes the applications of scRNA-seq in cancer, stem cell research, developmental biology, microbiology, and other fields. In essence, scRNA-seq provides a means of elucidating gene expression patterns in single cells, thereby offering a valuable tool for scientific research. Nevertheless, the current single-cell transcriptome technology is still imperfect, and this review identifies its shortcomings and anticipates future developments. The objective of this review is to facilitate a deeper comprehension of scRNA-seq technology and its applications in biological and medical research, as well as to identify avenues for its future development in alignment with practical needs.

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BACKGROUND: Malnutrition is prevalent among elderly cancer patients. This study aims to develop a predictive model for malnutrition in hospitalized elderly cancer patients. METHODS: Data from January 2022 to January 2023 on cancer patients aged 60+ were collected, involving 22 variables. Key variables were identified using the LASSO (Least Absolute Shrinkage and Selection Operator) method, and nine machine learning models were tested. SHAP was used to interpret the XGBoost model. Malnutrition prevalence was assessed. RESULTS: Among 450 participants, 46.4 % were malnourished. Key predictors identified were ADL (Activities of Daily Living), ALB (Albumin), BMI (Body Mass Index) and age. XGBoost had the highest AUC of 0.945, accuracy of 0.872, and sensitivity of 0.968. Higher ADL and age increased malnutrition risk, while lower ALB and BMI reduced it. CONCLUSIONS: The XGBoost model is highly effective in detecting malnutrition in elderly cancer patients, enabling early and rapid nutritional assessments.

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The development of multitarget opioid drugs has emerged as an attractive approach for innovative pain management with reduced side effects. In the present study, a novel hybrid peptide BNT12 containing the opioid and neurotensin (NT)-like fragments was synthesized and pharmacologically characterized. In acute radiant heat paw withdrawal test, intracerebroventricular (i.c.v.) administration of BNT12 produced potent antinociception in mice. The central antinociceptive activity of BNT12 was mainly mediated by µ-, δ-opioid receptor, neurotensin receptor type 1 (NTSR1) and 2 (NTSR2), supporting a multifunctional agonism of BNT12 in the functional assays. BNT12 also exhibited significant antinociceptive effects in spared nerve injury (SNI)-neuropathic pain, complete Freund's adjuvant (CFA)-induced inflammatory pain, acetic acid-induced visceral and formalin-induced pain after i.c.v. administration. Furthermore, BNT12 exhibited substantial reduction of acute antinociceptive tolerance, shifted the dose-response curve to the right by only 1.3-fold. It is noteworthy that BNT12 showed insignificant chronic antinociceptive tolerance at the supraspinal level. In addition, BNT12 exhibited reduced or no opioid-like side effects on conditioned place preference (CPP) response, naloxone-precipitated withdrawal response, acute hyperlocomotion, motor coordination, gastrointestinal transit, and cardiovascular responses. The present investigation demonstrated that the novel hybrid peptide BNT12 might serve as a promising analgesic candidate with limited opioid-like side effects.

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Introduction: The buffalo is an important domestic animal globally, providing milk, meat, and labor to more than 2 billion people in 67 countries. The rumen microorganisms of buffaloes play an indispensable role in enabling the healthy functionality and digestive function of buffalo organisms. Currently, there is a lack of clarity regarding the differences in the composition and function of rumen microorganisms among buffaloes at different growth stages. Methods: In this study, metagenomics sequencing technology was applied to examine the compositional and functional differences of rumen microorganisms in adult and breastfed buffaloes. Results: The results revealed that the rumen of adult buffaloes had significantly higher levels of the following dominant genera: Prevotella, UBA1711, RF16, Saccharofermentans, F23-D06, UBA1777, RUG472, and Methanobrevibacter_A. Interestingly, the dominant genera specific to the rumen of adult buffaloes showed a significant positive correlation (correlation>0.5, p-value<0.05) with both lignocellulose degradation-related carbohydrate-active enzymes (CAZymes) and immune signaling pathways activated by antigenic stimulation. The rumen of breastfed buffaloes had significantly higher levels of the following dominant genera: UBA629, CAG- 791, Selenomonas_C, Treponema_D, Succinivibrio, and RC9. Simultaneously, the rumen-dominant genera specific to breastfed buffaloes were significantly positively correlated (correlation>0.5, p-value<0.05) with CAZymes associated with lactose degradation, amino acid synthesis pathways, and antibiotic-producing pathways. Discussion: This indicates that rumen microorganisms in adult buffaloes are more engaged in lignocellulose degradation, whereas rumen microorganisms in breastfed buffaloes are more involved in lactose and amino acid degradation, as well as antibiotic production. In conclusion, these findings suggest a close relationship between differences in rumen microbes and the survival needs of buffaloes at different growth stages.

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BACKGROUND: Mycobacterium tuberculosis heat-resistant antigen (Mtb-HAg) is a peptide antigen released from the mycobacterial cytoplasm into the supernatant of Mycobacterium tuberculosis (Mtb) attenuated H37Ra strain after autoclaving at 121 °C for 20 min. Mtb-HAg can specifically induce γδ T-cell proliferation in vitro. However, the exact composition of Mtb-HAg and the protein antigens that are responsible for its function are currently unknown. METHODS: Mtb-HAg extracted from the Mtb H37Ra strain was subjected to LC‒MS mass spectrometry. Twelve of the identified protein fractions were recombinantly expressed in Escherichia coli by genetic engineering technology using pET-28a as a plasmid and purified by Ni-NTA agarose resin to stimulate peripheral blood mononuclear cells (PBMCs) from different healthy individuals. The proliferation of γδ T cells and major γδ T-cell subset types as well as the production of TNF-α and IFN-γ were determined by flow cytometry. Their proliferating γδ T cells were isolated and purified using MACS separation columns, and Mtb H37Ra-infected THP-1 was co-cultured with isolated and purified γδ T cells to quantify Mycobacterium viability by counting CFUs. RESULTS: In this study, Mtb-HAg from the attenuated Mtb H37Ra strain was analysed by LC‒MS mass spectrometry, and a total of 564 proteins were identified. Analysis of the identified protein fractions revealed that the major protein components included heat shock proteins and Mtb-specific antigenic proteins. Recombinant expression of 10 of these proteins in by Escherichia coli genetic engineering technology was used to successfully stimulate PBMCs from different healthy individuals, but 2 of the proteins, EsxJ and EsxA, were not expressed. Flow cytometry results showed that, compared with the IL-2 control, HspX, GroEL1, and GroES specifically induced γδ T-cell expansion, with Vγ2δ2 T cells as the main subset, and the secretion of the antimicrobial cytokines TNF-α and IFN-γ. In contrast, HtpG, DnaK, GroEL2, HbhA, Mpt63, EsxB, and EsxN were unable to promote γδ T-cell proliferation and the secretion of TNF-α and IFN-γ. None of the above recombinant proteins were able to induce the secretion of TNF-α and IFN-γ by αß T cells. In addition, TNF-α, IFN-γ-producing γδ T cells inhibit the growth of intracellular Mtb. CONCLUSION: Activated γδ T cells induced by Mtb-HAg components HspX, GroES, GroEL1 to produce TNF-α, IFN-γ modulate macrophages to inhibit intracellular Mtb growth. These data lay the foundation for subsequent studies on the mechanism by which Mtb-HAg induces γδ T-cell proliferation in vitro, as well as the development of preventive and therapeutic vaccines and rapid diagnostic reagents.

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BACKGROUND: PC (phytocyanin) is a class of copper-containing electron transfer proteins closely related to plant photosynthesis, abiotic stress responses growth and development in plants, and regulation of the expression of some flavonoids and phenylpropanoids, etc., however, compared with other plants, the PC gene family has not been systematically characterized in apple. RESULTS: A total of 59 MdPC gene members unevenly distributed across 12 chromosomes were identified at the genome-wide level. The proteins of the MdPC family were classified into four subfamilies based on differences in copper binding sites and glycosylation sites: Apple Early nodulin-like proteins (MdENODLs), Apple Uclacyanin-like proteins (MdUCLs), Apple Stellacyanin-like proteins (MdSCLs), and Apple Plantacyanin-like proteins (MdPLCLs). Some MdPC members with similar gene structures and conserved motifs belong to the same group or subfamily. The internal collinearity analysis revealed 14 collinearity gene pairs among members of the apple MdPC gene. Interspecific collinearity analysis showed that apple had 31 and 35 homologous gene pairs with strawberry and grape, respectively. Selection pressure analysis indicated that the MdPC gene was under purifying selection. Prediction of protein interactions showed that MdPC family members interacted strongly with the Nad3 protein. GO annotation results indicated that the MdPC gene also regulated the biosynthesis of phenylpropanoids. Chip data analysis showed that (MdSCL3, MdSCL7 and MdENODL27) were highly expressed in mature fruits and peels. Many cis-regulatory elements related to light response, phytohormones, abiotic stresses and flavonoid biosynthetic genes regulation were identified 2000 bp upstream of the promoter of the MdPC gene, and qRT-PCR results showed that gene members in Group IV (MdSCL1/3, MdENODL27) were up-regulated at all five stages of apple coloring, but the highest expression was observed at the DAF13 (day after fruit bag removal) stage. The gene members in Group II (MdUCL9, MdPLCL3) showed down-regulated or lower expression in the first four stages of apple coloring but up-regulated and highest expression in the DAF 21 stage. CONCLUSION: Herein, one objective of these findings is to provide valuable information for understanding the structure, molecular evolution, and expression pattern of the MdPC gene, another major objective in this study was designed to lay the groundwork for further research on the molecular mechanism of PC gene regulation of apple fruit coloration.

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Osteoarthritis (OA) is a degenerative disease with a high prevalence in the elderly population, but our understanding of its mechanisms remains incomplete. Analysis of serum exosomal small RNA sequencing data from clinical patients and gene expression data from OA patient serum and cartilage obtained from the GEO database revealed a common dysregulated miRNA, miR-199b-5p. In vitro cell experiments demonstrated that miR-199b-5p inhibits chondrocyte vitality and promotes extracellular matrix degradation. Conversely, inhibition of miR-199b-5p under inflammatory conditions exhibited protective effects against damage. Local viral injection of miR-199b-5p into mice induced a decrease in pain threshold and OA-like changes. In an OA model, inhibition of miR-199b-5p alleviated the pathological progression of OA. Furthermore, bioinformatics analysis and experimental validation identified Gcnt2 and Fzd6 as potential target genes of MiR-199b-5p. Thus, these results indicated that MiR-199b-5p/Gcnt2 and Fzd6 axis might be a novel therapeutic target for the treatment of OA.

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Accurate identification of single nucleotide variants (SNVs) in key driver genes holds a significant value for disease diagnosis and treatment. Fluorescent probes exhibit tremendous potential in specific, high-resolution, and rapid detection of SNVs. However, additional steps are required in most post-PCR assays to convert double-stranded DNA (dsDNA) products into single-stranded DNA (ssDNA), enabling them to possess hybridization activity to trigger subsequent reactions. This process not only prolongs the complexity of the experiment but also introduces the risk of losing target information. In this study, we proposed two strategies for enriching active double-stranded DNA, involving PCR based on obstructive groups and cleavable units. Building upon this, we explored the impact of modified units on the strand displacement reaction (SDR) and assessed their discriminatory efficacy for mutations. The results showed that detection of low variant allele frequencies (VAF) as low as 0.1% can be achieved. The proposed strategy allowed orthogonal identification of 45 clinical colorectal cancer tissue samples with 100% specificity, and the results were generally consistent with sequencing results. Compared to existing methods for enriching active targets, our approach offers a more diverse set of enrichment strategies, characterized by the advantage of being simple and fast and preserving original information to the maximum extent. The objective of this study is to offer an effective solution for the swift and facile acquisition of active double-stranded DNA. We anticipate that our work will facilitate the practical applications of SDR based on dsDNA.

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Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

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Background: Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets using machine learning (ML) method. Methods: The data used in this study are from the national health and nutrition examination survey 2003-2018. According to the demographic information and heavy metal exposure level of participants, a total of 22 variables were included. Lasso was used to screen out the key variables, and 9 commonly used ML models were selected to establish the associations with the 5-fold cross validation method. Finally, we choose the SHapley Additive exPlanations (SHAP) method to explain the prediction results of Adaboost model. Results: 11,667 eligible individuals were randomly divided into two groups to train and verify the prediction model. Through lasso, characteristic variables were selected from 24 variables as predictors. The AUC (area under curve) of the models selected in this study were all greater than 0.7, and AdaBoost was the best model. The AUC value of AdaBoost was 0.807, the accuracy was 0.720, and the sensitivity was 0.792. It is noteworthy that higher levels of cadmium, body mass index, cesium, being female, and increasing age were associated with an increased probability of MetS. Conversely, lower levels of cobalt and molybdenum were linked to a decrease in the estimated probability of MetS. Conclusion: Our study highlights the AdaBoost model proved to be highly effective, precise, and resilient in detecting a correlation between exposure to heavy metals and MetS. Through the use of interpretable methods, we identified cadmium, molybdenum, cobalt, cesium, uranium, and barium as prominent contributors within the predictive model.

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AIMS: This study aimed to investigate the relationship between ulcerative colitis (UC) and anxiety and explore its central mechanisms using colitis mice. METHODS: Anxiety-like behavior was assessed in mice induced by 3% dextran sodium sulfate (DSS) using the elevated plus maze and open-field test. The spatial transcriptome of the hippocampus was analyzed to assess the distribution of excitatory and inhibitory synapses, and Toll-like receptor 4 (TLR4) inhibitor TAK-242 (10 mg/kg) and AAV virus interference were used to examine the role of peripheral inflammation and central molecules such as Glutamate Receptor Metabotropic 1 (GRM1) in mediating anxiety behavior in colitis mice. RESULTS: DSS-induced colitis increased anxiety-like behaviors, which was reduced by TAK-242. Spatial transcriptome analysis of the hippocampus showed an excitatory-inhibitory imbalance mediated by glutamatergic synapses, and GRM1 in hippocampus was identified as a critical mediator of anxiety behavior in colitis mice via differential gene screening and AAV virus interference. CONCLUSION: Our work suggests that the hippocampus plays an important role in brain anxiety caused by peripheral inflammation, and over-excitation of hippocampal glutamate synapses by GRM1 activation induces anxiety-like behavior in colitis mice. These findings provide new insights into the central mechanisms underlying anxiety in UC and may contribute to the development of novel therapeutic strategies for UC-associated anxiety.

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