Impact of microbial-based biopreparations on soil quality, plant health, and fruit chemistry in raspberry cultivation.

Application of microbial-based biopreparations as a pre-harvest strategy offers a method to obtain sustainable agricultural practices and could be an important approach for advancing food science, promoting sustainability, and meeting global food market demands. The impact of a bacterial-fungal biopreparation mixture on soil-plant-microbe interactions, fruit chemical composition and yield of 7 raspberry clones was investigated by examining the structural and functional profiles of microbial communities within leaves, fruits, and soil. Biopreparation addition caused the enhancement of the microbiological utilization of specific compounds, such as d-mannitol, relevant in plant-pathogen interactions and overall plant health. The biopreparation treatment positively affected the nitrogen availability in soil (9-160%). The analysis of plant stress marker enzymes combined with the evaluation of fruit quality and chemical properties highlight changes inducted by the pre-harvest biopreparation application. Chemical analyses highlight biopreparations' role in soil and fruit quality improvement, promoting sustainable agriculture. This effect was dependent on tested clones, showing increase of soluble solid content in fruits, concentration of polyphenols or the sensory quality of the fruits. The results of the next-generation sequencing indicated increase in the effective number of bacterial species after biopreparation treatment. The network analysis showed stimulating effect of biopreparation on microbial communities by enhancing microbial interactions (increasing the number of network edges up to 260%) of and affecting the proportions of mutual relationships between both bacteria and fungi. These findings show the potential of microbial-based biopreparation in enhancing raspberry production whilst promoting sustainable practices and maintaining environmental homeostasis and giving inshght in holistic understanding of microbial-based approaches for advancing food science monitoring.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

A simple and efficient method for betalain quantification in RUBY-expressing plant samples.

The RUBY reporter system has demonstrated great potential as a visible marker to monitor gene expression in both transiently and stably transformed plant tissues. Ectopic expression of the RUBY reporter leads to bright red pigmentation in plant tissues that do not naturally accumulate betalain. Unlike traditional visual markers such as ß-glucuronidase (GUS), luciferase (LUC), and various fluorescent proteins, the RUBY reporter system does not require sample sacrifice or special equipment for visualizing the gene expression. However, a robust quantitative analysis method for betalain content has been lacking, limiting accurate comparative analyses. In this work, we present a simple and rapid protocol for quantitative evaluation of RUBY expression in transgenic plant tissues. Using this method, we demonstrate that differential RUBY expression can be quantified in transiently transformed leaf tissues, such as agroinfiltrated Nicotiana benthamiana leaves, and in stable transgenic maize tissues, including seeds, leaves, and roots. We found that grinding fresh tissues with a hand grinder and plastic pestle, without the use of liquid nitrogen, is an effective method for rapid betalain extraction. Betalain contents estimated by spectrophotometric and High-Performance Liquid Chromatography (HPLC) analyses were highly consistent, validating that our rapid betalain extraction and quantification method is suitable for comparative analysis. In addition, betalain content was strongly correlated with RUBY expression level in agroinfiltrated N. benthamiana leaves, suggesting that our method can be useful for monitoring transient transformation efficiency in plants. Using our rapid protocol, we quantified varying levels of betalain pigment in N. benthamiana leaves, ranging from 110 to 1066 mg/kg of tissue, and in maize samples, ranging from 15.3 to 1028.7 mg/kg of tissue. This method is expected to streamline comparative studies in plants, providing valuable insights into the effectiveness of various promoters, enhancers, or other regulatory elements used in transgenic constructs.

Ratios of organic mass to organic carbon in fine particulate matter at urban sites in China and Korea during winter and summer.

This study evaluates the composition and seasonal characteristics of fine particulate matter (PM2.5) during winter and summer through simultaneous measurements conducted at the Gwangju Institute of Science and Technology in South Korea and the Changping campus of Peking University in China. PM2.5 samples were concurrently collected at both sites, and chemical analyses were conducted to quantify various components, including carbonaceous materials, ionic species, and metals. Although the average PM2.5 concentrations were comparable between the two sites, there were distinct differences in the concentrations of major components. Organic indicator compounds were analyzed to discern the contributions of primary and secondary pollution sources. Changping displayed a mix of primary and secondary pollution, characterized by higher concentrations of primary organic carbon (POC) such as polycyclic aromatic hydrocarbons and hopanes, compared to Gwangju. In contrast, Gwangju demonstrated a higher prevalence of secondary organic carbon (SOC), particularly water-soluble organic carbon not related to biomass burning (WSOCnbb) and various polar organic compounds. The organic mass to organic carbon (OM/OC) ratios estimated using the mass balance method revealed significant differences, with Gwangju showing a higher ratio of 2.3 compared to 1.9 at Changping, indicating a greater influence of secondary pollutants at Gwangju. Additionally, both Changping and Gwangju exhibited higher OM/OC ratios in summer (Changping: 2.0, Gwangju: 2.5) compared to winter (Changping: 1.8, Gwangju: 2.2), indicating seasonal differences in organic mass contributions to PM2.5. These findings underscore the importance of accounting for spatial and seasonal variations in air pollution studies and suggest that updating commonly used OM/OC ratios could enhance the reliability of research outcomes.

Maximum standardized uptake value-to-tumor size ratio in fluorodeoxyglucose F18 positron emission tomography/computed tomography: a simple prognostic parameter for non-small cell lung cancer.

PURPOSE: By correcting the effect of tumor size on metabolic activity, the maximum standardized uptake value-to-tumor size (SUVmax:tumor size) ratio on fluorodeoxyglucose F18 positron emission tomography (18F-FDG PET)/computed tomography (CT) scans can be a prognostic parameter of non-small cell lung cancer (NSCLC). The current study evaluates the prognostic value of SUVmax:tumor size ratio on pretreatment 18F-FDG PET/CT scans in patients with NSCLC. Furthermore, the SUVmax:tumor size ratio is compared with other established PET parameters. METHODS: This study included 108 patients with NSCLC who underwent pretreatment 18F-FDG PET/CT scans and curative lung surgery. The associations between the SUVmax:tumor size ratio and other conventional PET parameters were investigated. The recurrence-free survival according to the SUVmax:tumor size ratio was also analyzed. In addition, the SUVmax:tumor size ratio was compared according to postoperative pathologic findings. RESULTS: In total, 72 (66.7%) of the 108 participants presented with adenocarcinoma (ADC). Nineteen (17.6%) patients experienced recurrence during a median follow-up period of 32.3 months. The median SUV max:tumor size ratio was 2.37 (1.23 for ADCs and 3.90 for other histologic types). The SUVmax:tumor size ratio was associated with SUVmax and mean SUV, as well as metabolic tumor volume and total lesion glycolysis. Patients with an SUVmax:tumor size ratio higher than the median had a worse recurrence outcome than those with an SUVmax:tumor size ratio lower than the median. Participants with ADC who presented with lymphovascular invasion had a higher SUVmax:tumor size ratio than those without. The presence of lymph node metastasis and advanced histologic grade were associated with a high SUVmax:tumor size ratio in patients with ADC. CONCLUSION: The SUVmax:tumor size ratio on pretreatment 18F-FDG PET/CT scans was associated with aggressive tumor behavior and poor outcome in NSCLCs, particularly ADC. CLINICAL SIGNIFICANCE: The SUVmax:tumor size ratio on pretreatment 18F-FDG PET/CT scans has a prognostic value in patients with NSCLCs, especially ADC.

Biochemical markers for prediction of the first half pregnancy losses: a review.

Objective: 26% of all pregnancies end in miscarriage, and up to 10% of clinically diagnosed pregnancies, and recurrent pregnancy loss is 5% among couples of childbearing ages. Although there are several known causes of pregnancy loss in the first half, including recurrent pregnancy loss, including parental chromosomal abnormalities, uterine malformations, endocrinological disorders, and immunological abnormalities, about half of the cases of pregnancy loss in its first half remain unexplained. Methods: The review includes observational controlled studies (case-control or cohort, longitudinal studies, reviews, meta-analyses), which include the study of biochemical factors for predicting pregnancy losses in the first half, in singlet pregnancy. The Newcastle-Ottawa Scale (NOS) was used to assess the research quality. Results: Finally, 27 studies were included in the review, which has 134904 examined patients. The results of the review include estimates of ß-human chorionic gonadotropin, progesterone, pregnancy-associated protein - A, angiogenic vascular factors, estradiol, α-fetoprotein, homocysteine and CA-125 as a predictors or markers of the first half pregnancy losses. Conclusion: It may be concluded that to date, research data indicate the unavailability of any reliable biochemical marker for predicting pregnancy losses in its first half and require either a combination of them or comparison with clinical evidence. A fairly new model shall be considered for the assessment of α-fetoprotein in vaginal blood, which may have great prospects in predicting spontaneous miscarriages.

Immunohistochemical Expression of Calretinin in Dentigerous Cyst Transforming Into Unicystic Ameloblastoma: A Case Report.

The term "unicystic ameloblastoma" describes cystic lesions that exhibit radiographic, clinical, or gross characteristics of a jaw cyst. However, histologic examination reveals a typical ameloblastomatous epithelium lining the cyst cavity, with or without luminal and/or mural tumor proliferation. Unicystic ameloblastoma is a less prevalent kind of ameloblastoma. Among the odontogenic cysts, neoplastic transformation is highest in odontogenic keratocysts (OKCs) and dentigerous cysts (DCs). Calretinin is considered a specific immunohistochemical marker for neoplastic ameloblastic epithelium and serves as a diagnostic tool for differentiating odontogenic cystic lesions from ameloblastic tumors. This paper illustrates a case of a DC transforming into unicystic ameloblastoma in a 22-year-old male patient using the immunohistochemical expression of calretinin.

Clinical and laboratory pattern of patients with systemic lupus erythematosus seropositive for rheumatoid factor.

Introduction: The aim of the study was to investigate the associations between the presence and level of rheumatoid factor (RF) in the blood serum and the clinical and laboratory characteristics of patients with systemic lupus erythematosus (SLE). Material and methods: This retrospective tricentric cross-sectional study analyzed a Ukrainian contingent of SLE patients. Medical records of 495 patients were evaluated. Rheumatoid factor serum concentration was tested in 206 of them (41.6%) using turbidimetry technique. Clinical manifestations, routine laboratory parameters, specific immunological tests, disease activity (SLEDAI-2K), and damage indices (SLICC/ACR DI) were evaluated. Results: Our study revealed that RF was elevated in 27.7% of patients. The RF-positive patients experienced a longer delay in SLE diagnosis (2.0 vs. 0.5 years, p = 0.046), less frequent kidney involvement (42.1% vs. 59.4%, p = 0.045) and fever (42.1% vs. 59.2%, p = 0.046), and more frequent lymphadenopathy (59.6% vs. 42.3%, p = 0.039) compared to RF-negative patients. Patients with RF positivity had higher levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and antinuclear antibody (ANA) titer, and were more frequently positive for antibodies to Ro/SSA and La/SSB. Rheumatoid factor concentration directly correlated with CRP (r = 0.318; p < 0.01) and ESR (r = 0.228; p = 0.04) levels. However, no associations were found between RF levels and SLEDAI-2K, joint involvement frequency, SLICC/ACR DI or drug therapy content. Univariate logistic regression analysis showed that RF positivity was independently associated with lymphadenopathy, presence of anti-Ro/SSA and anti-La/SSB antibodies, and negatively associated with kidney involvement. Conclusions: In RF-seropositive SLE patients (approximately 28%), the diagnosis is established later compared to RF-seronegative ones; kidney involvement and fever are less common, while lymphadenopathy develops more frequently. Rheumatoid factor seropositivity is associated with higher levels of ESR, CRP, ANA, and the presence of antibodies to Ro/SSA and La/SSB. According to the results of univariate logistic regression analysis, an independent association with RF positivity was confirmed only for kidney involvement, lymphadenopathy, and antibodies to Ro/SSA and La/SSB.

Metabolomics analysis of (Geo)propolis from Brazilian stingless bees by FIA and UHPLC-HRMS (Orbitrap).

Native stingless bees (Meliponini) from Brazil make (geo)propolis which is largely used in folk medicine, specially by indigenous and quilombos communities and beekeepers´ families but are progressively being recognized for their pharmacological activities. In this study, the ethanolic extracts of (geo)propolis (EEGs) from Melipona marginata, M. quadrifasciata, M. scutellaris, and Tetragonisca angustula were analysed by Flow injection analysis (FIA) and Ultra-high performance liquid chromatography (UHPLC) in a high resolution Orbitrap mass analyser (HRMS) to investigate and compare their chemical profile. Untargeted metabolomic approach based on UHPLC-HRMS experiments, and bioinformatic tools, allowed to annotate 59 compounds from diverse classes such as: flavonoids, phenolic compounds, sugars, terpenoids, and lipids. In addition, using multivariate tools and Flow injection- high resolution mass spectrometry (FIA-HRMS), it was possible to classify samples and identify marker ions related to the bee species or genus and to the geographical origin as a proof of concept.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

Assessing microvascular invasion in HBV-related hepatocellular carcinoma: an online interactive nomogram integrating inflammatory markers, radiomics, and convolutional neural networks.

Objective: The early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC. Materials and methods: 173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves. Results: Inflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively. Conclusion: NLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.

LINC00365 as a potential biomarker for total nephrectomy in advanced-stage renal cell carcinoma patients.

BACKGROUND: Renal cell carcinoma (RCC) is one of the most frequent urological cancers globally that has a good prognosis in the early tumor stages. However, there is a poor prognosis in metastatic RCC patients. Therefore, it is needed to evaluate the molecular biology of RCC progression to introduce the efficient diagnostic and therapeutic markers in these patients. Long non-coding RNAs (lncRNAs) have key roles in regulation of molecular mechanisms during RCC progression. In the present study, we assessed the levels of LINC00365 expressions in RCC patients to suggest that as a tumor marker in these patients. METHODS: Fifty fresh RCC tumor tissues and their normal margins were collected to assess the levels of LINC00365 expressions and probable correlations with clinicopathological features of RCC patients. RESULTS: There was significant LINC00365 up regulation in females compared with males (p=0.050). Among the RCC patients with total nephrectomy, there was a significant LINC00365 up regulation in advanced stage compared with primary stage tumors (p=0.035). RCC patients older than 60 years old who were undergone the total nephrectomy had also significant LINC00365 up regulation compared with RCC patients younger than 60 years old (p=0.039). CONCLUSIONS: given the significant increase in LINC00365 expression in advanced stage RCC tumors and patients over 60 years old who had total nephrectomy; it could serve as a useful diagnostic marker in screening programs for old high-risk individuals. It was also noticed that female RCC patients had elevated levels of LINC00365 expressions in their tumor samples, suggesting its potential use as a gender-specific diagnostic marker for high-risk females. Nevertheless, evaluating the levels of LINC00365 in serum samples of RCC patients is necessary to suggest that as a reliable diagnostic marker in clinical settings.

Intratumoral and fecal microbiota reveals microbial markers associated with gastric carcinogenesis.

Background: The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets. Methods: We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions. Results: The α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non-Helicobacter pylori (HP) commensals, which are crucial in both HP-negative and HP-positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712). Conclusions: This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified Lactobacillus, Streptococcus, Roseburia, Faecalibacterium and Phascolarctobacterium as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of Lactobacillus and Streptococcus as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

Caudal-Type Homeobox Transcription Factor 2 is a Favorable Prognostic Indicator in Stage II and III Gastric Cancer Following Curative Surgery.

Background: The study explored the prognostic value of caudal-type homeobox transcription factor 2 (CDX2) in stage II and III gastric cancer. Methods: This study evaluated the expression level of CDX2 in gastric cancer in a hospital cohort (n=197) using immunohistochemistry. According to a semiquantitative score used to determine CDX2 expression, the cases were divided into a low CDX2 group (116 cases) and a high CDX2 group (81 cases). The RNA-seq expression data from 291 patients with stage II and III gastric cancer from The Cancer Genome Atlas (TCGA) cohort were used to verify the immunohistochemistry results. Based on the median CDX2 expression value, the TCGA patients were divided into a low CDX2 group (145 cases) and a high CDX2 group (146 cases). The relationships among CDX2 expression and clinicopathological features were determined using the Chi-square test. Cox proportional hazards regression models were applied to estimate the independent prognostic factors. The probability of survival was determined using the Kaplan-Meier method and Log rank tests. Results: Based on the Cox multivariate analysis, CDX2 was the independent prognostic factor in the hospital and TCGA cohorts. In the hospital cohort, CDX2 expression was associated with an improved DFS (hazard ratio [HR] = 0.4076, 95% CI, 0.2675-0.6210, P = 0.0001) and OS (HR = 0.4183, 95% CI, 0.2744-0.6375, P = 0.0002). In the TCGA cohort, CDX2 expression also was associated with an improved DFS (HR = 0.5948, 95% CI, 0.4153-0.8521, P = 0.0054) and OS (HR = 0.5976, 95% CI, 0.4172-0.8561, P = 0.0058). Furthermore, the CDX2 expression level was correlated with an improved DFS (P = 0.0025) and OS (P = 0.0015) using the Kaplan-Meier Plotter database for gastric cancer. Conclusion: CDX2 is a potential prognostic biomarker for stage II and III gastric cancer. In addition, CDX2 positive cancer patients are more likely to have resectable tumors and exhibit better survival rates.

Decoding the Rarity: A Unique Case of Clear-Cell Variant of Oral Squamous Cell Carcinoma.

The clear-cell variant of oral squamous cell carcinoma is an extremely rare histological variant and an incompletely understood entity. Clear cell appearance in squamous cell carcinoma may be attributed to hydropic degeneration of neoplastic cells. We report a case of a 32-year-old male patient who presented with an ulceroproliferative growth in the left maxillary posterior region on the hard palate and gingiva, obliterating the buccal vestibule. Histopathologic examination revealed thick anastomosing strands of round to ovoid neoplastic cells with predominantly clear cytoplasm and marked cellular and nuclear pleomorphism infiltrating into the fibro-cellular connective tissue stroma. Special staining and immunohistochemistry (IHC) were performed to rule out the differentials of clear-cell variants of different sites such as salivary gland, odontogenic origin, and metastatic tumors. The clear cells were negative for periodic acid Schiff (PAS) and mucicarmine. The malignant clear cells showed positive reactions with IHC markers pan-cytokeratin and P63 and yielded negative results for S100 and CD10, confirming the diagnosis as a clear-cell variant of oral squamous cell carcinoma. We emphasize the importance of prompt and comprehensive diagnostic work-up to identify this rare, aggressive, and possibly fatal neoplasm.

DNAMarkMaker: streamlining ARMS and CAPS marker development from resequencing data with NGS short reads.

DNA markers serve as essential tools in breeding selection and genetic analysis. However, developing DNA markers can be time-consuming and labor-intensive due to the need to identify polymorphisms between cultivars/lines and to design suitable primers. To address these challenges, we have developed DNAMarkMaker, a tool designed to automate the process of primer design for Amplification Refractory Mutation System (ARMS) and Cleaved Amplified Polymorphic Sequences (CAPS) markers, utilizing resequencing data. One key feature of DNAMarkMaker is its user-friendly graphical user interface (GUI), ensuring its accessibility and ease of use, even for researchers not well-versed in bioinformatics. We confirmed DNAMarkMaker's applicability by developing DNA markers for rice, potato, and turnip-each representing distinct genome structures: homozygous diploid, heterozygous autotetraploid, and heterozygous diploid, respectively. DNAMarkMaker will contribute to the rapid and efficient development of DNA markers, accelerating breeding and genetic analysis in various crops.

Detection and validation of QTLs for green stem disorder of soybean (Glycine max (L.) Merr.).

In mechanically harvested soybean, green stem disorder (GSD) is an undesirable trait that causes green-stained seeds, which are graded lower in Japan. To obtain DNA markers for reduced GSD, we conducted a quantitative trait locus (QTL) analysis for 2 years using F4 and F5 lines from a cross between 'Suzuotome' (less GSD) and 'Fukuyutaka' (more GSD). We validated the effect of a detected QTL for GSD by first identifying F4 or F5 plants in which one or more markers in the QTL region were heterozygous. The F5 or F6 progeny of each plant was used to form a pair consisting of two groups in which the QTL region was homozygous for either the 'Suzuotome' or 'Fukuyutaka' allele in a similar genetic background, and the two groups within each pair were compared for GSD. Over 3 years of testing, the 'Suzuotome' allele of a QTL on chromosome 6 was found to reduce the level of GSD. This novel QTL was mapped to the region around DNA marker W06_0130, and was not closely linked to QTLs for important agronomic traits including yield components. Using this marker, the low level of GSD from 'Suzuotome' could be conferred to 'Fukuyutaka' or other high-GSD cultivars.

Polyploid QTL-seq revealed multiple QTLs controlling steamed tuber texture and starch gelatinization temperature in sweetpotato.

Sweetpotato (Ipomoea batatas) includes diverse cultivars with flesh textures ranging from dry to moist. Moist-fleshed cultivars often contain starch with a lower gelatinization temperature (GT). To elucidate the genetic determinants of flesh texture and starch GT, we conducted a QTL analysis using F1 progenies obtained from a cross between dry-fleshed and moist-fleshed cultivars, 'Benikomachi' (BK) and 'Amahazuki' (AH), by using an updated polyploid QTL-seq pipeline. Flesh texture was assessed based on the wet area ratio (WAR) observed on the cut surface of steamed tubers, as progenies with dry and moist flesh exhibited low and high WAR values, respectively, demonstrating a strong correlation. Three QTLs were found to regulate the WAR. Notably, two AH-derived alleles at 4.30 Mb on Itr_chr05 and 21.01 Mb on Itr_chr07, along with a BK-derived allele at 2.89 Mb on Itr_chr15, were associated with increased WAR. Starch GT, which displayed no correlation with either flesh texture or WAR, was distinctly influenced by two QTLs: a GT-increasing BK-derived allele at 1.74 Mb on Itr_chr05 and a GT-decreasing AH-derived allele at 30.16 Mb on Itr_chr12. Consequently, we developed DNA markers linked to WAR, offering a promising avenue for the targeted breeding of sweetpotato with the desired flesh textures.

MTAP as an emerging biomarker in thoracic malignancies.

S-methyl-5'-thioadenosine phosphorylase (MTAP) deficiency is an emerging biomarker in non-small cell lung cancer (NSCLC) and beyond. The MTAP gene is located in the chromosomal region 9p21.3, which shows one of the most common homozygous deletions across all human cancers (9p21 loss). Loss of 9p21 is found in the majority of pleural mesotheliomas, where it serves as an established diagnostic marker. Until recently, fluorescence in situ hybridization (FISH) was the gold standard for the detection of 9p21 losses, but loss of MTAP expression by immunohistochemistry (IHC) gains increasing importance as an easy to apply and cost-effective diagnostic surrogate marker. Besides, MTAP loss, which has been reported in 13% of NSCLC, is becoming an emerging predictive biomarker in two different scenarios in NSCLC and other cancer types: 1) MTAP loss seems to negatively predict the response to immune checkpoint inhibitor (ICI) treatment via silencing of the tumor microenvironment, and 2) MTAP loss serves as a predictive biomarker for novel targeted treatment strategies. MTAP deficiency leads to an impaired function of the protein arginine methyltransferase 5 (PRMT5) due to its partial inhibition by MTAP's accumulating substrate methylthioadenosine (MTA). This process leaves MTAP deficient tumor cells heavily dependent on the remaining function of PRMT5, making it a perfect target for synthetic lethality. Indeed, MTA-cooperative PRMT5-inhibitors are now tested in several clinical trials with promising early results in solid malignancies. With its emergence as a predictive biomarker, the implementation of MTAP IHC into diagnostic routine for NSCLC and other tumors is likely to take place soon. In this review article, we summarize the current literature on the role of MTAP in thoracic tumors and evaluate different testing methods, including IHC, FISH and next generation sequencing.

The Mechanisms and Quality Markers of Anti-rheumatoid Arthritis of Traditional Chinese Medicine.

BACKGROUND: RA is a recurrent autoimmune disease that has significant adverse effects on the physical and mental health of patients. Traditional Chinese medicine has shown significant advantages in the prevention and treatment of RA. Numerous clinical and experimental studies have confirmed that traditional Chinese medicine components have clear therapeutic effects and minimal adverse reactions in treating RA. The research on traditional Chinese medicine for the prevention and treatment of RA has become a hot topic in the field of autoimmune diseases. METHODS: The related references about the mechanisms and Q-markers of anti-RA of traditional Chinese medicine in this review were collected from Willy, SpringLink, Web of Science, Elsevier, PubMed, SciFinder, Scopus, ACS publications, Baidu Scholar, Google Scholar, and CNKI. RESULTS: The traditional Chinese medicine components such as terpenoids, flavonoids, and alkaloids have significant anti-RA effects, and their mechanisms are mainly to inhibit NF-κB signaling pathway, inhibit the proliferation of RA fibroblasts like synovial cells, and regulate Th1/Th2 cell balance, and so on. Predicting and studying the Q-markers of traditional Chinese medicine anti-RA by plant phylogeny and chemical componentss, traditional medicinal properties, pharmacokinetics, component measurability, correlation between composition and efficacy, and gut microbiota will provide scientific foundations for the research and further development of anti-RA traditional Chinese medicine. CONCLUSIONS: The active components of traditional Chinese medicine exhibited the characteristic of multiple mechanisms in the treatment of RA, such as terpenoids had anti-angiogenesis effects, flavonoids had anti-inflammatory and cartilage protective effects, and alkaloids had antiinflammatory and analgesic effects. The proposal of Q-markers for anti-RA provided new research ideas for promoting the development of new drugs for anti-RA and ensuring the safety and effectiveness of clinical medications.