The critical role of tumor microbiome in cancer immunotherapy.

In recent years, the microbiome has shown an integral role in cancer immunotherapy and has become a prominent and widely studied topic. A full understanding of the interactions between the tumor microbiome and various immunotherapies offers opportunities for immunotherapy of cancer. This review scrutinizes the composition of the tumor microbiome, the mechanism of microbial immune regulation, the influence of tumor microorganisms on tumor metastasis, and the interaction between tumor microorganisms and immunotherapy. In addition, this review also summarizes the challenges and opportunities of immunotherapy through tumor microbes, as well as the prospects and directions for future related research. In conclusion, the potential of microbial immunotherapy to enhance treatment outcomes for cancer patients should not be underestimated. Through this review, it is hoped that more research on tumor microbial immunotherapy will be done to better solve the treatment problems of cancer patients.

Association between duration of progesterone supplementation and clinical outcomes in artificial frozen-thawed embryo transfer cycles.

Objective: The administration of progesterone before transfer in hormone replacement treatment (HRT) is crucial for the clinical outcomes of frozen-thawed embryo transfer (FET), but the optimal duration of progesterone remains controversial. This study aimed to investigate the effect of the duration of progesterone administration on the clinical outcomes of FET cycles. Methods: This prospective cohort study included 353 artificial FET cycles conducted at a reproductive medicine center between April and October 2021. The FET cycles were stratified into four groups based on the duration of progesterone supplementation before the procedure and the embryonic development stage: group P3 (73 patients) received intramuscular progesterone for 3 days and group P4 (87 patients) for 4 days before Day 3 frozen embryo transfer, group P5 (70 patients) for 5 days and group P6 (123 patients) for 6 days before frozen blastocyst transfer. This trial was performed using one or two vitrified embryo(s) when the endometrial thickness reached 7 mm after estrogen supplementation in an artificial cycle. The primary outcome was clinical pregnancy, and secondary outcomes included biochemical pregnancy, implantation, early pregnancy loss, and live births. Results: There were no significant differences in the demographic and clinical characteristics between the groups. No significant difference was observed in the clinical pregnancy rates between groups: 23/73 (31.5%) in group P3 vs 28/87 (32.2%) in group P4 (P = 0.927). Compared to group P5 (41/70, 58.6%), the clinical pregnancy rate was not significantly different in group P6 (77/123, 62.6%, P = 0.753). There was no significant difference in the implantation rates between groups: 33/136 (24.3%) in group P3 vs 34/166 (20.5%) in group P4 (P = 0.431), and 62/133 (46.6%) in group P5 vs 107/231 (46.3%) in group P6 (P = 0.956). The duration of progesterone supplementation (mean: 3.5 ± 0.5 days; range:3-4 days) before Day 3 frozen embryo transfer did not impact clinical pregnancy (odds ratio [OR] 1.048; 95% confidence interval [CI], 0.518-2.119). The duration of progesterone administration (mean: 5.6 ± 0.5 days; range:5-6 days) before frozen blastocyst transfer may not affect clinical pregnancy (OR 1.339; 95% CI, 0.717-2.497). Conclusion: There may be no significant correlation between the duration of progesterone supplementation and pregnancy outcomes in artificial FET cycles, although the clinical pregnancy rate was higher when progesterone supplementation was extended for one day before FET.

Association between diabetes status and breast cancer in US adults: findings from the US National Health and Nutrition Examination Survey.

Objectives: The aim of this study was to investigate the association between diabetes status and the risk of breast cancer among adult Americans, exploring the impact of BMI, age, and race on this relationship. Methods: A cross-sectional analysis of 8,249 individuals from the National Health and Nutrition Examination Survey (NHANES) was conducted. Diabetes was categorized as type 2 diabetes and prediabetes, with both conditions diagnosed according to the ADA 2014 guidelines. The association between diabetes status and breast cancer risk was explored using multiple logistic regression analysis. Results: Patients with diabetes had higher odds of breast cancer (OR: 1.51; 95% CI 1.00 to 2.28), Using the two-piecewise linear regression model, it was observed that there is a threshold effect in the risk of breast cancer occurrence at the age of 52 years. Specifically, the risk of breast cancer is relatively low before the age of 52 but increases significantly after this age. Conclusions: This study identified a significant association between diabetes status and breast cancer risk among adult Americans. We also found a threshold effect in breast cancer occurrence at the age of 52. Age was significantly associated with breast cancer risk in both Non-Hispanic White and Non-Hispanic Black individuals. These findings underscore the importance of diabetes management, maintaining a healthy BMI, and age-related risk considerations in reducing breast cancer risk.

Assisted reproductive technology and interactions between serum basal FSH/LH and ovarian sensitivity index.

Objectives: This study aimed to investigate whether the FSH (follicle-stimulating hormone)/LH (Luteinizing hormone) ratio correlates with ovarian response in a cross-sectional retrospective study of a population with normal levels of anti-Müllerian hormone (AMH). Methods: This was a retrospective cross-sectional study with data obtained from medical records from March 2019 to December 2019 at the reproductive center in the Affiliated Hospital of Southwest Medical University. The Spearmans correlation test evaluated correlations between Ovarian sensitivity index (OSI) and other parameters. The relationship between basal FSH/LH and ovarian response was analyzed using smoothed curve fitting to find the threshold or saturation point for the population with mean AMH level (1.1

Independent value of PMOI on hCG day in predicting pregnancy outcomes in IVF/ICSI cycles.

Objectives: The aim of this study was to determine whether, on the day of human chorionic gonadotropin (hCG) injection, the progesterone to number of mature oocytes index (PMOI) can be used alone or together with other parameters in a fresh embryo transfer in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycle to predict pregnancy outcome. Methods: This was a retrospective cohort study of all couples who underwent a clinical pregnancy and received a fresh IVE/ICSI cycle at a single large reproductive medical center between June 2019 and March 2022. The study involved a total of 1239 cycles. To analyze risk factors associated with pregnancy outcomes on the day of HCG injection, univariate and multivariate logistic regression analyses were used. The area under the curve (AUC) was determined, and PMOI and other factors were compared using receiver operating characteristic (ROC) curves. Results: The clinical pregnancy rate was significantly higher in group A (60.76%) than in the other groups (Group B: 52.92% and Group C:47.88%, respectively, p =0.0306). Univariate and multivariate logistic regression revealed that PMOI levels were significantly correlated with the probability of pregnancy outcome, independent of other risk factors. More importantly, PMOI levels independently predict the occurrence of pregnancy outcome, comparable to the model combining age. The optimal serum PMOI cutoff value for pregnancy outcome was 0.063 ug/L. Conclusion: Our results suggest that PMOI levels have an independent predictive value for pregnancy outcome in fresh IVF/ICSI cycles.

Association of ABO blood groups with ovarian reserve: a retrospective cohort study in Chinese Han women younger than 40 years with infertility.

BACKGROUND: Ovarian reserve reflects both the quantity and quality of oocytes available for procreation and is affected by many known and unknown factors. ABO blood type is related to several infertility processes, but it is unclear whether and how ABO blood type affects ovarian reserve. OBJECTIVE: The purpose of the study was to explore the correlation between ABO blood types and ovarian reserve in infertile Chinese Han women under 40 years of age undergoing the in vitro fertilization (IVF)/ intracytoplasmic sperm injection (ICSI)-embryo transfer (IVF/ICSI-ET) treatment. METHODS: Women aged < 40 years who underwent IVF/ICSI-ET at our institution and had a documented ABO blood type were eligible for this study. In this study, patients were divided into two groups according to the diminished ovarian reserve (DOR) group (AMH < 1.1 ng/mL, AFC < 6) and the non-diminished ovarian reserve (non-DOR) group (AMH ≥ 1.1 ng/mL, AFC ≥ 6). The relationship between ovarian reserve and ABO blood group was determined by correlation analysis. RESULTS: In this retrospective cohort study, clinical data were collected from 1690 Chinese Han women treated with IVF/ ICSI-ET in hospital records between April 2019 and March 2020 in the affiliated hospital of Southwest Medical University, located in Luzhou, China. The differences in age, duration of infertility, BMI, FSH, FSH / LH, and p (DOR vs non-DOR) for each parameter (DOR vs non-DOR) were statistically significant, and the differences in LH and E2 were not statistically significant. ABO blood groups were most prevalent in the DOR group with O (143, 34.8%) and A (122, 29.7%) and in the non-DOR group with A (428, 33.5%) and O (419, 32.8%). ABO blood groups were most prevalent in the DOR group with O (n = 57, 30.5%) and A (n = 54, 28.9%) and in the non-DOR group with A (n = 335, 34.0%) and O (n = 323, 32.8%) were the most frequent in the non-DOR group. CONCLUSIONS: In this retrospective cohort study, we confirmed the lack of a significant association between ABO blood type and ovarian reserve. Further studies are needed to clarify whether there is any prognostic correlation between ABO blood group and ovarian reserve in women undergoing IVF/ICSI-ET.

Predictive value of anti-Mullerian hormone for pregnancy outcomes following assisted reproductive techniques (ART) in Southwest China.

BACKGROUND: Anti-Müllerian hormone (AMH) is secreted by granulosa cells in preantral follicles and small antral follicles. There is limited information about whether serum AMH levels are related to pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET). The aim of this study was to provide a theoretical basis for improving pregnancy outcomes. METHODS: A retrospective cohort study was conducted on infertile women who were treated at the Reproductive Centre of the Affiliated Hospital of Southwest Medical University between September 2018 and September 2019. The sample included 518 participants from Southwest China. The participants were divided into 2 groups according to their AMH level. Their data were retrieved from the medical records: days and dosage of gonadotropin (Gn) (one bottle equals 75 IU), the number of oocytes obtained, the number of oocytes in metaphase II (MII) and the number of high-quality embryos. The pregnancy outcomes were followed up and divided into two groups according to whether they were pregnant or not, with statistical analysis of the parameters related to the in vitro fertilization process performed separately. RESULTS: Compared to a lower AMH level (AMH ≤ 1.1), a higher AMH level (AMH > 1.1) resulted in less total Gn (bottle) (P = 0.00 < 0.05) and a lower starting Gn (IU) (P = 0.00 < 0.05), while the number of oocytes obtained,MII,cleavages and high-quality embryos were higher (P = 0.00 < 0.05). The participants' pregnancy outcomes (ectopic pregnancy, miscarriage, singleton, twin, multiple births) were found to not be predictable by AMH through ROC curves (P = 0.980, 0.093, 0.447, 0.146, 0.526, and 0.868 > 0.05). For participants in the pregnancy group, although AMH was lower in the nonpregnant participants(P = 0.868 > 0.05), the difference was not statistically significant, and the correlation coefficients between the two groups suggested no differences in the IVF process, except for the starting Gn (IU) (P = 0.038 < 0.05). CONCLUSION: AMH has clinical application value in predicting ovarian reserve function, providing guidance and suggestions for the specific formulation of ovulation promotion programs with assisted reproductive technology, but it cannot effectively predict the outcome of clinical pregnancy.

Comparison of the predictive capability of antral follicle count vs. the anti-Müllerian hormone for ovarian response in infertile women.

Objectives: The aim of this study was to compare the predictive capability of antral follicle count (AFC) and the anti-Müllerian hormone (AMH) on ovarian response in infertile women and to identify potential factors influencing retrieved oocytes. Methods: A total of 2585 infertile women who underwent in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles had been enrolled in this study. Spearman correlation was used to investigate the correlation between retrieved oocytes and AFC. Multiple linear regression analysis was used to study the parameters affecting the number of retrieved oocytes. Results: Spearman correlation and multiple linear regression analysis revealed that the oocyte retrieval number was positively correlated with AFC (r = 0.651, p < 0.001) and AMH (r = 0.566, p < 0.001) and negatively correlated with age (r = -0.425, p < 0.001) and regimen selection (r = -0.233 p < 0.001). There was no significant correlation between retrieved oocytes and BMI (p = 0.913). ROC analysis revealed that AFC was a better predictor of adverse effects than AMH, BMI, and age (AUC: 0.916 VS 0.791, 0.575, 0.752). Meanwhile, AFC and AMH were comparable in predicting high response (AUC = 0.731 and AUC = 0.733, respectively). Conclusions: This study showed that retrieved oocytes were positively correlated with serum AMH and AFC and negatively correlated with age and BMI. AFC had an ideal predictive performance in ovarian response prediction. The mechanism of the effect of AFC on ovarian response during controlled ovarian hyperstimulation (COH) needs to be further investigated.

Computational ghost imaging based on a conditional generation countermeasure network under a low sampling rate.

In this paper, an end-to-end depth neural network based on a conditional generative adversarial network for computational ghost imaging (CGANCGI) is proposed to restore clear object images with high quality at a sub-Nyquist sampling rate. The 2D light signal collected by a CMOS camera and the gray image of the original measured object are used as the input of the network model; then, the CGANCGI network is trained, and the measured object image is recovered directly from the 2D light signal. Experiments have verified that the proposed method only needs 1/10 of traditional deep learning samples to achieve fast image restoration with high-quality, and its peak signal-to-noise ratio and structural similarity are, respectively, four to six times and five to seven times higher than those of the original image, which prove that our method has practical application prospects in ghost imaging under low sampling rates.

Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma.

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package 'edgeR'. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

High-speed computational ghost imaging based on an auto-encoder network under low sampling rate.

Computational ghost imaging is difficult to apply under low sampling rate. We propose high-speed computational ghost imaging based on an auto-encoder network to reconstruct images with high quality under low sampling rate. The auto-encoder convolutional neural network is designed, and the object images can be reconstructed accurately without labeled images. Experimental results show that our method can greatly improve the peak signal-to-noise ratio and structural similarity of the test samples, which are up to 18 and 0.7, respectively, under low sampling rate. Our method only needs 1/10 of traditional deep learning samples to achieve fast and high-quality image reconstruction, and the network also has a certain generalization to the gray-scale images.

Specular highlight removal based on an iterative light field method.

In this paper, a novel, to the best of our knowledge, iterative approach for highlight removal is proposed using lenselet-based plenoptic cameras without multiple exposures. An unsupervised k-means clustering approach that relates unsaturated pixels to chromatic dispersion based on the intrinsic decomposition and dichromatic reflection model is proposed to recover unsaturated highlights. Meanwhile, an adaptive direction method along with a Gaussian probability distribution model is designed to recover the saturated highlights. Finally, a method that combines the specular residual ratio with information entropy is built to quantitatively evaluate the quality of highlight removal. Generally, our method not only fully removes specular highlights, but also has low spatial complexity of image acquisition, more stability, and outstanding restoration for complex scenes.

Combining Dual-Light Emissions to Achieve Efficient Broadband Yellowish-Green Luminescence in One-Dimensional Hybrid Lead Halides.

In recent years, low-dimensional lead halides have emerged as some of most attractive photoelectric materials due to their intrinsic broadband emissions with a potential application in white-light emitting diodes. To achieve the desired performance, tremendous research has emphasized the modulation of inorganic components as optical centers; however, less work has paid attention to the direct contribution of the organic components. Herein, we successfully assembled two new hybrid lead halides of [H2BPP]Pb2X6 (X = Br, 1, and Cl, 2) containing one-dimensional double [Pb2X6]2- chains using optically active 1,3-bis(4-pyridyl)-propane (BPP) as an organic cation. Under UV-light excitation, compounds 1 and 2 exhibit broadband yellowish-green emissions, which were verified by promising photoluminescence quantum efficiencies (PLQEs) of 8.10% and 4.84%, respectively. The broadband light emissions are derived from the combination of dual higher-energy blue and lower-energy yellow light spectra, which can be attributed to the individual contributions of the organic and inorganic components, respectively, according to the time-resolved and temperature-dependent emission spectra as well as theoretical calculations. This work proves the great contribution of organic components to the photophysical properties and provides a new design strategy to realize broadband light emission by rationally combining the dual-emitting properties of different assembly blocks.

Tandem Lewis Pair Polymerization and Organocatalytic Ring-Opening Polymerization for Synthesizing Block and Brush Copolymers.

Lewis pair polymerization is a powerful method for preparing soluble polymers bearing pendant active vinyl groups by directly polymerizing dissymmetric divinyl polar monomers. Herein, we present a strategy for synthesizing block and brush copolymers via tandem Lewis pair polymerization of methacrylates, "thiol-ene" click reaction and organocatalytic ring-opening polymerization of lactide.

The prediction of palmitoylation site locations using a multiple feature extraction method.

As an extremely important and ubiquitous post-translational lipid modification, palmitoylation plays a significant role in a variety of biological and physiological processes. Unlike other lipid modifications, protein palmitoylation and depalmitoylation are highly dynamic and can regulate both protein function and localization. The dynamic nature of palmitoylation is poorly understood because of the limitations in current assay methods. The in vivo or in vitro experimental identification of palmitoylation sites is both time consuming and expensive. Due to the large volume of protein sequences generated in the post-genomic era, it is extraordinarily important in both basic research and drug discovery to rapidly identify the attributes of a new protein's palmitoylation sites. In this work, a new computational method, WAP-Palm, combining multiple feature extraction, has been developed to predict the palmitoylation sites of proteins. The performance of the WAP-Palm model is measured herein and was found to have a sensitivity of 81.53%, a specificity of 90.45%, an accuracy of 85.99% and a Matthews correlation coefficient of 72.26% in 10-fold cross-validation test. The results obtained from both the cross-validation and independent tests suggest that the WAP-Palm model might facilitate the identification and annotation of protein palmitoylation locations. The online service is available at http://bioinfo.ncu.edu.cn/WAP-Palm.aspx.

Position-specific analysis and prediction for protein lysine acetylation based on multiple features.

Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.

Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform.

In vivo, some proteins exist as monomers and others as oligomers. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural components of various biological functions, including cooperative effects, allosteric mechanism and ion-channel gating. Therefore, with the avalanche of protein sequences generated in the post-genomic era, it is very important for both basic research and the pharmaceutical industry to acquire the possible knowledge about quaternary structural attributes of their proteins of interest. In view of this, a high throughput method (DWT_DT), a 2-layer approach by fusing discrete wavelet transform (DWT) and decision-tree algorithm (DT) with physicochemical features, has been developed to predict protein quaternary structures. The 1st layer is to assign a query protein to one of the 10 main quaternary structural attributes. The 2nd layer is to evaluate whether the protein in question is composed of homo- or hetero-oligomers. The overall accuracy by jackknife test for the 1st layer identification was 89.60%. The overall accuracy of the 2nd layer varies from 88.23 to 100%. The results suggest that this newly developed protocol (DWT_DT) is very promising in predicting quaternary structures with complicated composition.

A method to distinguish between lysine acetylation and lysine methylation from protein sequences.

Lysine acetylation and methylation are two major post-translational modifications of lysine residues. They play vital roles in both biological and pathological processes. Specific lysine residues in H3 histone protein tails appear to be targeted for either acetylation or methylation. Hence it is very challenging to distinguish between acetylated and methylated lysine residues using computational methods. This work presents a method that incorporates protein sequence information, secondary structure and amino acid properties to differentiate acetyl-lysine from methyl-lysine. We apply an encoding scheme based on grouped weight and position weight amino acid composition to extract sequence information and physicochemical properties around lysine sites. The proposed method achieves an accuracy of 93.3% using a jackknife test. Feature analysis demonstrates that the prediction model with multiple features can take full advantage of the supplementary information from different features to improve classification performance and prediction robustness. Analysis of the characteristics of lysine residues which can be either methylated or acetylated shows that they are more similar to methyl-lysine than to acetyl-lysine.

PredSulSite: prediction of protein tyrosine sulfation sites with multiple features and analysis.

Tyrosine sulfation is a ubiquitous posttranslational modification that regulates extracellular protein-protein interactions, intracellular protein transportation modulation, and protein proteolytic process. However, identifying tyrosine sulfation sites remains a challenge due to the lability of sulfation sequences. In this study, we developed a method called PredSulSite that incorporates protein secondary structure, physicochemical properties of amino acids, and residue sequence order information based on support vector machine to predict sulfotyrosine sites. Three types of encoding algorithms-secondary structure, grouped weight, and autocorrelation function-were applied to mine features from tyrosine sulfation proteins. The prediction model with multiple features achieved an accuracy of 92.89% in 10-fold cross-validation. Feature analysis showed that the coil structure, acidic amino acids, and residue interactions around the tyrosine sulfation sites all contributed to the sulfation site determination. The detailed feature analysis in this work can help us to understand the sulfation mechanism and provide guidance for the related experimental validation. PredSulSite is available as a community resource at http://www.bioinfo.ncu.edu.cn/inquiries_PredSulSite.aspx.