Development and Validation of an Interpretable Machine Learning Prediction Model for Total Pathological Complete Response after Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer: Multicenter Retrospective Analysis.

Objective: This study aims to develop an interpretable machine learning (ML) model to accurately predict the probability of achieving total pathological complete response (tpCR) in patients with locally advanced breast cancer (LABC) following neoadjuvant chemotherapy (NAC). Methods: This multi-center retrospective study included pre-NAC clinical pathology data from 698 LABC patients. Post-operative pathological outcomes divided patients into tpCR and non-tpCR groups. Data from 586 patients at Shanghai Ruijin Hospital were randomly assigned to a training set (80%) and a test set (20%). In comparison, data from our hospital's remaining 112 patients were used for external validation. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Predictive models were constructed using six ML algorithms: decision trees, K-nearest neighbors (KNN), support vector machine, light gradient boosting machine, and extreme gradient boosting. Model efficacy was assessed through various metrics, including receiver operating characteristic (ROC) curves, precision-recall (PR) curves, confusion matrices, calibration plots, and decision curve analysis (DCA). The best-performing model was selected by comparing the performance of different algorithms. Moreover, variable relevance was ranked using the SHapley Additive exPlanations (SHAP) technique to improve the interpretability of the model and solve the "black box" problem. Results: A total of 191 patients (32.59%) achieved tpCR following NAC. Through LASSO regression analysis, five variables were identified as predictive factors for model construction, including tumor size, Ki-67, molecular subtype, targeted therapy, and chemotherapy regimen. The KNN model outperformed the other five classifier algorithms, achieving area under the curve (AUC) values of 0.847 (95% CI: 0.809-0.883) in the training set, 0.763 (95% CI: 0.670-0.856) in the test set, and 0.665 (95% CI: 0.555-0.776) in the external validation set. DCA demonstrated that the KNN model yielded the highest net advantage through a wide range of threshold probabilities in both the training and test sets. Furthermore, the analysis of the KNN model utilizing SHAP technology demonstrated that targeted therapy is the most crucial factor in predicting tpCR. Conclusion: An ML prediction model using clinical and pathological data collected before NAC was developed and verified. This model accurately predicted the probability of achieving a tpCR in patients with LABC after receiving NAC. SHAP technology enhanced the interpretability of the model and assisted in clinical decision-making and therapy optimization.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status.

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.

QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize.

Introduction: Plant height (PH) and ear height (EH) are key plant architectural traits in maize, which will affect the photosynthetic efficiency, high plant density tolerance, suitability for mechanical harvesting. Methods: QTL mapping were conducted for PH and EH using a recombinant inbred line (RIL) population and two corresponding immortalized backcross (IB) populations obtained from crosses between the RIL population and the two parental lines. Results: A total of 17 and 15 QTL were detected in the RIL and IB populations, respectively. Two QTL, qPH1-1 (qEH1-1) and qPH1-2 (qEH1-4) in the RIL, were simultaneously identified for PH and EH. Combing reported genome-wide association and cloned PH-related genes, co-expression network analyses were constructed, then five candidate genes with high confidence in major QTL were identified including Zm00001d011117 and Zm00001d011108, whose homologs have been confirmed to play a role in determining PH in maize and soybean. Discussion: QTL mapping used a immortalized backcross population is a new strategy. These identified genes in this study can provide new insights for improving the plant architecture in maize.

Predictive value of stromal tumor-infiltrating lymphocytes in patients with breast cancer treated with neoadjuvant chemotherapy: A meta-analysis.

BACKGROUND: The predictive value of tumor-infiltrating lymphocytes (TILs) in response to neoadjuvant chemotherapy (NAC) for breast cancer (BC) has received increasing attention. Here, a meta-analysis was conducted to evaluate the correlation between the expression of stromal TILs and pathological complete response (pCR) after NAC in BC patients. METHODS: The PubMed, Embase, Cochrane Library, and Web of Science databases were searched online by using a combination of keywords and free words to screen literature on the expression of stromal TILs and pCR after NAC in patients with BC. The data were extracted and evaluated for quality. Relative risk (RR) was used to evaluate the relationship between the expression of stromal TILs before NAC and pCR in BC patients. Meta-analysis was performed with Review Manager 5.3 and STATA 14.0 software. RESULTS: Eleven studies involving 6039 BC patients were included in the meta-analysis. The results showed a generally high expression of stromal TILs in BC patients, and the pCR rate after NAC in BC patients with a high expression of stromal TILs was significantly higher than that in BC patients with a low expression of stromal TILs [RR = 1.83, 95% confidence interval (CI): 1.69-1.97]. Subgroup analysis based on the molecular subtypes of BC showed that the pCR rate was significantly higher in patients with a high expression of stromal TILs in hormone receptor (HR)-positive BC [RR = 3.23, 95% CI: 2.43-4.30], human epidermal growth factor receptor 2 (HER-2)-positive BC [RR = 1.41, 95% CI: 1.25-1.60], and triple-negative BC [RR = 1.70, 95% CI: 1.53-1.90] than in those with a low expression of stromal TILs. Subgroup analysis based on expression threshold showed that the pCR rate was higher in patients with a high expression of stromal TILs than in patients with a low expression of stromal TILs at different expression thresholds (10% [RR = 1.99, 95% CI: 1.55-2.55], 20%/30% [RR = 1.57, 95% CI: 1.37-1.81], 50%/60% [RR = 1.91, 95% CI: 1.73-2.11]. CONCLUSION: TILs can be used as a predictor of pCR after NAC in patients with BC, and the appropriate high expression threshold of stromal TILs should be selected as the predictive value according to the molecular subtype of BC.

Causal Effects of COVID-19 on the Risk of Thrombosis: A Two-Sample Mendel Randomization Study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) and thrombosis are linked, but the biomolecular mechanism is unclear. We aimed to investigate the causal relationship between COVID-19 and thrombotic biomarkers. METHODS: We used two-sample Mendelian randomization (MR) to assess the effect of COVID-19 on 20 thrombotic biomarkers. We estimated causality using inverse variance weighting with multiplicative random effect, and performed sensitivity analysis using weighted median, MR-Egger regression and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) methods. All the results were examined by false discovery rate (FDR) with the Benjamin and Hochberg method for this correction to minimize false positives. We used R language for the analysis. RESULTS: All COVID-19 classes showed lower levels of tissue factor pathway inhibitor (TFPI) and interleukin-1 receptor type 1 (IL-1R1). COVID-19 significantly reduced TFPI (odds ratio [OR] = 0.639, 95% confidence interval [CI]: 0.435-0.938) and IL-1R1 (OR = 0.603, 95% CI = 0.417-0.872), nearly doubling the odds. We also found that COVID-19 lowered multiple coagulation factor deficiency protein 2 and increased C-C motif chemokine 3. Hospitalized COVID-19 cases had less plasminogen activator, tissue type (tPA) and P-selectin glycoprotein ligand 1 (PSGL-1), while severe cases had higher mean platelet volume (MPV) and lower platelet count. These changes in TFPI, tPA, IL-1R1, MPV, and platelet count suggested a higher risk of thrombosis. Decreased PSGL-1 indicated a lower risk of thrombosis. CONCLUSION: TFPI, IL-1R, and seven other indicators provide causal clues of the pathogenesis of COVID-19 and thrombosis. This study demonstrated that COVID-19 causally influences thrombosis at the biomolecular level.

Real-world data on the first-line immune checkpoint inhibitors or in combination with chemotherapy in older patients (aged ≥ 75 years) with advanced non-small cell lung cancer.

Purpose: The purpose of this study is to investigate the efficacy and safety of immune checkpoint inhibitors (ICIs) or plus with chemotherapy in older patients. Methods: We enrolled 110 older patients with non-small cell lung cancer (NSCLC ≥75 years) who received either chemotherapy alone (chemo), ICI plus chemotherapy (ICI + chemo), or ICI alone and ICI plus other therapies, which included anti-angiogenesis drugs or other novel ICI (ICIs). Patient characteristics, treatment response, survival, and toxicity were evaluated. Results: In total population, the ICIs group has the highest disease control rate (DCR 75%). There were no significant differences in progression-free survival (PFS) and overall survival (OS) among older patients between ICI + chemo and ICIs groups (PFS: 5.3 months vs. 5.5 months, p = 0.70, OS: 10.7 months vs. 20.3 months, p = 0.995). Meanwhile, we observed ICIs had a longer PFS and OS than chemo group (PFS: 3.9 months vs. 5.5 months, p = 0.01, OS: 10.9 months vs. 20.3 months, p = 0.05). Subgroup analysis showed that patients with programmed death ligand-1 (PD-L1) ≥ 1% had a distinct longer trend toward OS in ICIs group compared to ICI + chemo group (22.4 months vs. 10.7 months, p = 0.605), even though there was no significant difference. In terms of safety, ICIs was more tolerable and had a lower discontinuation rate than ICI + chemo group. Conclusion: In the real world, ICI + chemo is more likely to be discontinued due to adverse effects and does not significantly improve patient survival compared with ICIs treatment in total population and subgroup. Therefore, ICI alone or ICIs plus other therapies, such as anti-angiogenesis drugs or other novel ICI (ICIs) could be recommended for older cases with PD-L1 positive NSCLC.

Bioinformatics analysis proposes a possible role for long noncoding RNA MIR17HG in retinoblastoma.

BACKGROUND: Retinoblastoma (RB) is the most common prevalent intraocular malignancy among infants and children, particularly in underdeveloped countries. With advancements in genomics and transcriptomics, noncoding RNAs have been increasingly utilized to investigate the molecular pathology of diverse diseases. AIMS: This study aims to establish the competing endogenous RNAs network associated with RB, analyse the function of mRNAs and lncRNAs, and finds the relevant regulatory network. METHODS AND RESULTS: This study establishes a network of competing endogenous RNAs by Spearman correlation analysis and prediction based on RB patients and healthy children. Enrichment analyzes based on Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes are conducted to analyze the potential biological functions of lncRNA and mRNA networks. Weighted gene co-expression network analysis (WGCNA) is employed to identify gene cluster modules exhibiting the strongest correlation with RB. The results indicate a significant correlation between the lncRNA MIR17HG (R = .73, p = .02) and the RB phenotype. ceRNA networks reveal downstream miRNAs (hsa-mir-425-5p and hsa-mir455-5p) and mRNAs (MDM2, IPO11, and ITGA1) associated with MIR17Hg. As an inhibitor of the p53 signaling pathway, MDM2 can suppress the development of RB. CONCLUSION: In conclusion, lncRNAs play a role in RB, and the MIR17HG/hsa-mir-425-5p/MDM2 pathway may contribute to RB development by inhibiting the p53 signaling pathway.

Nivolumab combined docetaxel versus nivolumab in patients with previously treated nonsmall cell lung cancer: a phase 2 study.

The current standard second-line treatment is immune checkpoint inhibitors monotherapy for nonsmall cell lung cancer (NSCLC) patients. The objective of this phase 2 study was to evaluate the efficacy and safety of nivolumab plus docetaxel compared with nivolumab monotherapy for second-line therapy in immunotherapy-naive patients with advanced NSCLC. Progression-free survival (PFS) was the primary endpoint of this phase 2 study. Patients were randomized to receive nivolumab plus docetaxel or nivolumab monotherapy. From July 2019 to June 2022, a total of 22 patients were recruited, with significantly longer median PFS observed in the nivolumab plus docetaxel group (4.0 months) compared to the nivolumab group (2.0 months), P  = 0.0019. The study was closed in June 2022 due to slow recruitment. The objective response rate was 10.0% [95% confidence interval (CI), 0-28.6] in the nivolumab group and 25% (95% CI, 0.5-49.5) in the nivolumab + docetaxel group ( P  = 0.346). Disease control was significantly higher in the nivolumab plus docetaxel arm (40.0% versus 83.3%, P  = 0.035). There was also an improvement in overall survival (OS) in the nivolumab + docetaxel arm, but this was not statistically significant (10.0 months versus 7.2 months, P  = 0.129). The addition of docetaxel to nivolumab was well-tolerated, with adverse events more common in the combination group. Despite the small sample size, the results suggest that the addition of docetaxel to nivolumab may be a promising treatment option for NSCLC patients progressing on platinum-based chemotherapy, with trends towards improved OS observed.

Development and validation of a prognostic nomogram for breast cancer patients who underwent chemoradiotherapy and surgery: a retrospective cohort study based on the SEER database and two Chinese cohorts.

There is no strong evidence indicating the optimal treatment for breast cancer (BC) and no specific prognostic model. The aim of this study was to establish nomograms to predict the overall survival (OS) of BC patients receiving chemoradiotherapy and surgery, thereby quantifying survival benefits and improving patient management. A total of 1877 patients with primary nonmetastatic BC who received chemoradiotherapy and surgery from 2010 to 2019 were identified from the Surveillance, Epidemiology and End Results (SEER) database as the training cohort, 804 as the internal validation cohort, and 796 patients from the First Affiliated Hospital of Zhengzhou University (n=324) and Jiaxing Maternal and Child Health Hospital (n=472) as the external validation cohort. Least absolute shrinkage and selection operator (LASSO), univariate, and multivariate Cox regression analyses were performed in the training cohort to determine independent prognostic factors for BC, and a nomogram was constructed to predict 3-year, 5-year, and 8-year OS. The final model incorporated 7 factors that significantly affect OS: race, location, positive regional nodes, T stage, N stage, subtype, and grade. The calibration curves showed good consistency between the predicted survival and actual outcomes. Time-dependent receiver operating characteristic (ROC) curves and the time-dependent area under the curve (AUC) confirmed that the accuracy and clinical usefulness of the constructed nomograms were favorable. Decision curve analysis (DCA) and net reclassification improvement (NRI) also demonstrated that this nomogram was more suitable for clinical use than the 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system and the previous prediction model. In the training cohort and the internal validation cohort, the concordance indices (C-index) of the nomogram for predicting OS (0.723 and 0.649, respectively) were greater than those of the 7th AJCC TNM staging system and the previous prediction model. In addition, based on Kaplan-Meier (K-M) survival curves, the survival differences among different risk stratifications were statistically significant, indicating that our risk model was accurate. In this study, we determined independent prognostic factors for OS in patients with primary nonmetastatic BC treated with chemoradiotherapy and surgery. A new and accurate nomogram for predicting 3-, 5-, and 8-year OS in this patient population was developed and validated for potential clinical applicability.

ASF-Transformer: neutralizing the impact of atmospheric turbulence on optical imaging through alternating learning in the spatial and frequency domains.

Atmospheric turbulence, a pervasive and complex physical phenomenon, challenges optical imaging across various applications. This paper presents the Alternating Spatial-Frequency (ASF)-Transformer, a learning-based method for neutralizing the impact of atmospheric turbulence on optical imaging. Drawing inspiration from split-step propagation and correlated imaging principles, we propose the Alternating Learning in Spatial and Frequency domains (LASF) mechanism. This mechanism utilizes two specially designed transformer blocks that alternate between the spatial and Fourier domains. Assisted by the proposed patch FFT loss, our model can enhance the recovery of intricate textures without the need for generative adversarial networks (GANs). Evaluated across diverse test mediums, our model demonstrated state-of-the-art performance in comparison to recent methods. The ASF-Transformer diverges from mainstream GAN-based solutions, offering a new strategy to combat image degradation introduced by atmospheric turbulence. Additionally, this work provides insights into neural network architecture by integrating principles from optical theory, paving the way for innovative neural network designs in the future.

Calculation of carbon emission efficiency in China and analysis of influencing factors.

Carbon emissions have risen in line with China's economic expansion. The key to sustainable development is finding a way to strike a balance between economic expansion and environmental protection, so improving carbon emission efficiency is vital. This paper uses provincial data from 2010 to 2020 to account for total carbon emissions using the emission factor method and obtains carbon emission efficiency data on this basis. A dynamic spatial Durbin model is then used to empirically test the possible influencing factors. The results show that, firstly, the growth rate of total carbon emissions is generally in line with the growth rate of GDP, indicating that there is no 'decoupling' in the economic system. Second, regional carbon emissions and carbon emission efficiency are not necessarily related. Thirdly, there is a clear spatial effect on carbon emission efficiency. The eastern region has the highest carbon emission efficiency, the western region has the lowest, and the northeastern and central regions have little difference in carbon emission efficiency. Further spatial and temporal migration analysis reveals that five provinces have made the migration between 2010 and 2020. Fourthly, in the short term, the direct and indirect effects of the factors affecting carbon emission efficiency are insignificant, but in the long term, most of the factors have significant direct and indirect effects on carbon emission efficiency. Finally, based on the above research findings, this paper makes policy recommendations.

Characterization of the Isocitrate Dehydrogenase Gene Family and Their Response to Drought Stress in Maize.

Isocitrate dehydrogenase (IDH) is a key rate-limiting enzyme in the tricarboxylic acid cycle and acts in glutamine synthesis. IDH also participates in plant growth and development and in response to abiotic stresses. We identified 11 maize IDH genes (ZmIDH) and classified these genes into ZmNAD-IDH and ZmNADP-IDH groups based on their different coenzymes (NAD+ or NADP+). The ZmNAD-IDH group was further divided into two subgroups according to their catalytic and non-catalytic subunits, as in Arabidopsis. The ZmIDHs significantly differed in physicochemical properties, gene structure, conserved motifs, and protein tertiary structure. Promoter prediction analysis revealed that the promoters of these ZmIDHs contain cis-acting elements associated with light response, abscisic acid, phytohormones, and abiotic stresses. ZmIDH is predicted to interact with proteins involved in development and stress resistance. Expression analysis of public data revealed that most ZmIDHs are specifically expressed in anthers. Different types of ZmIDHs responded to abiotic stresses with different expression patterns, but all exhibited responses to abiotic stresses to some extent. In addition, analysis of the public sequence from transcription data in an association panel suggested that natural variation in ZmIDH1.4 will be associated with drought tolerance in maize. These results suggested that ZmIDHs respond differently and/or redundantly to abiotic stresses during plant growth and development, and this analysis provides a foundation to understand how ZmIDHs respond to drought stress in maize.

Macrophage/microglia polarization for the treatment of diabetic retinopathy.

Macrophages/microglia are immune system defense and homeostatic cells that develop from bone marrow progenitor cells. According to the different phenotypes and immune responses of macrophages (Th1 and Th2), the two primary categories of polarized macrophages/microglia are those conventionally activated (M1) and alternatively activated (M2). Macrophage/microglial polarization is a key regulating factor in the development of inflammatory disorders, cancers, metabolic disturbances, and neural degeneration. Macrophage/microglial polarization is involved in inflammation, oxidative stress, pathological angiogenesis, and tissue healing processes in ocular diseases, particularly in diabetic retinopathy (DR). The functional phenotypes of macrophages/microglia affect disease progression and prognosis, and thus regulate the polarization or functional phenotype of microglia at different DR stages, which may offer new concepts for individualized therapy of DR. This review summarizes the involvement of macrophage/microglia polarization in physiological situations and in the pathological process of DR, and discusses the promising role of polarization in personalized treatment of DR.

A Sedentary Lifestyle Changes the Composition and Predicted Functions of the Gut Bacterial and Fungal Microbiota of Subjects from the Same Company.

A sedentary lifestyle affects the diversity and composition of the gut microbiota, but previous studies have mainly focused on bacteria instead of fungi. Here, we compared both the fecal bacterial and fungal microbiota compositions and functions in sedentary persons and controls. Subjects from the China Railway Corporation, including 99 inspectors and 88 officials, were enrolled in our study. Fecal microbiota communities were analyzed using 16S rRNA gene sequencing for bacteria and ITS sequencing for fungi. We found that the diversity of the gut microbiota of the sedentary group was significantly lower than that of the control group (P < 0.05). The sedentary group had a higher abundance of Firmicutes, a lower abundance of Actinobacteria and Proteobacteria and a higher abundance of Ascomycota, and a lower abundance of Basidiomycota. Furthermore, functional prediction analysis of the fungal microbiota revealed more L-tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde, more phospholipid remodeling (phosphatidylethanolamine, yeast), and more L-tyrosine degradation I, as well as less pentose phosphate pathway (non-oxidative branch), less adenosine nucleotide biosynthesis and less L-valine biosynthesis in the sedentary group (P < 0.05). Thus, a sedentary lifestyle changes the composition and function of the gut microbiota. It may change the pentose phosphate pathway (non-oxidative branch), nucleic acid and amino acid biosynthesis and phospholipid metabolism in fungi.

Hyperglycemia-regulated tRNA-derived fragment tRF-3001a propels neurovascular dysfunction in diabetic mice.

Neurovascular dysfunction is a preclinical manifestation of diabetic complications, including diabetic retinopathy (DR). Herein, we report that a transfer RNA-derived RNA fragment, tRF-3001a, is significantly upregulated under diabetic conditions. tRF-3001a downregulation inhibits Müller cell activation, suppresses endothelial angiogenic effects, and protects against high-glucose-induced retinal ganglion cell injury in vitro. Furthermore, tRF-3001a downregulation alleviates retinal vascular dysfunction, inhibits retinal reactive gliosis, facilitates retinal ganglion cell survival, and preserves visual function and visually guided behaviors in STZ-induced diabetic mice and db/db diabetic mice. Mechanistically, tRF-3001a regulates neurovascular dysfunction in a microRNA-like mechanism by targeting GSK3B. Clinically, tRF-3001a is upregulated in aqueous humor (AH) samples of DR patients. tRF-3001a downregulation inhibits DR-induced human retinal vascular endothelial cell and Müller cell dysfunction in vitro and DR-induced retinal neurovascular dysfunction in C57BL/6J mice. Thus, targeting tRF-3001a-mediated signaling is a promising strategy for the concurrent treatment of vasculopathy and neuropathy in diabetes mellitus.

Prognostic relevance of tumor­infiltrating lymphocytes in residual tumor tissue from patients with triple­negative breast cancer following neoadjuvant chemotherapy: A systematic review and meta­analysis.

Further adjuvant chemotherapy treatment can provide benefits to certain patients with triple-negative breast cancer (TNBC) that fail to achieve pathological complete response (pCR) after the administration of a neoadjuvant chemotherapy (NAC) regimen. However, biomarkers suitable for identifying patients likely to experience poor prognostic outcomes after undergoing additional adjuvant chemotherapy are currently lacking. Accordingly, the present meta-analysis was conducted to explore the relationship between tumor-infiltrating lymphocytes (TILs) or TIL subtypes (CD4+ or CD8+) in residual tumor (RT) tissue following NAC and TNBC patient prognosis. Relevant studies published through March 2023 were identified in Pubmed, The Cochrane Library, Embase and Web of Science databases. After excluding irrelevant studies, data were extracted from the remaining reports, while study quality was analyzed with the Newcastle-Ottawa Scale. Subsequent analyses were performed with Stata 14.0 and Review Manager 5.3. In total, seven relevant studies incorporating 1,202 patients were identified, all of which were retrospective cohort studies. Pooled analyses demonstrated that those patients exhibiting higher levels of RT TIL infiltration following NAC exhibited significantly improved recurrence-free, metastasis-free and event-free survival (RFS/MFS/EFS) compared with patients with lower RT TIL infiltration levels, together with an improved distant recurrence-free interval (DRFI) [hazard ratio (HR)=0.52; 95% confidence interval (CI)=0.39-0.69; P<0.00001]. In addition, patients exhibiting high RT TIL infiltration exhibited improved overall survival (OS) and breast cancer-specific survival (BCSS; HR=0.49; 95% CI=0.38-0.65; P<0.00001). Additional subgroup analyses revealed that patients with higher TIL infiltration levels or TIL subtype (CD4+ or CD8+) infiltration exhibited improved RFS/MFS/EFS/DRFI as compared with patients with lower levels of overall TIL or TIL subtype (CD4+ or CD8+) infiltration in RT tissue (HR=0.35, 95% CI=0.20-0.59, P<0.0001; HR=0.49, 95% CI=0.33-0.71, P=0.0002). Consistently, the OS/BCSS of patients exhibiting high levels of overall TIL or TIL subtype (CD4+ or CD8+) infiltration was increased compared with patients with lower levels of such infiltration (HR=0.33, 95% CI=0.19-0.59, P=0.0002; HR=0.55, 95% CI=0.41-0.76, P=0.0002). These data thus demonstrate that levels of overall TIL infiltration or infiltration by CD4+ or CD8+ TILs in RT following NAC can be used as a biomarker to reliably predict prognostic outcomes in patients with TNBC, in addition to highlighting possible targets that may guide the further immunotherapeutic management of these patients.

[FARSB stratifies prognosis and cold tumor microenvironment across different cancer types: an integrated single cell and bulk RNA sequencing analysis].

OBJECTIVE: Immunotherapy has brought significant clinical benefits to a subset of patients, but has thus far been disappointing in the treatment of immunologically "cold" tumors. Existing biomarkers that can precisely identify these populations are insufficient. In this context, a potential cold tumor microenvironment (TME) marker FARSB was investigated to reveal its impact on TME and patients' response to immunotherapy across pan-cancer. METHODS: The expression levels and mutational landscape of FARSB in pan-cancer were investigated. Kaplan-Meier and univariate Cox regression analyses were applied to analyze the prognostic significance of FARSB. Pathways affected by FARSB were investigated by gene set enrichment and variation analysis. The relationship between FARSB expression and immune infiltration was examined using the TIMER2 and R packages. Single-cell RNA sequencing (scRNA-seq) data of several cancer types from GSE72056, GSE131907, GSE132465, GSE125449 and PMID32561858 were analyzed to validate the impact of FARSB on the TME. The predictive effect of FARSB on immunotherapy efficacy was explored in 3 immune checkpoint inhibitors (ICIs)- treated cohorts (PMID32472114, GSE176307, and Riaz2017). RESULTS: FARSB expression was significantly higher in 25 tumor tissues than in normal tissues and was associated with poor prognosis in almost all tumor types. FARSB expression exhibited a strong association with several DNA damage repair pathways and was significantly associated with TP53 mutation in lung adenocarcinoma (P < 0.0001, OR=2.25). FARSB characterized a typical immune desert TME and correlated with impaired expression of chemokines and chemokines receptors. Large-scale scRNA-seq analysis confirmed the immunosuppressive role of FARSB and revealed that FARSB potentially shapes the cold TME by impeding intercellular interactions. In 3 ICI-treated cohorts, FARSB demonstrated predictive value for immunotherapy. CONCLUSION: This study provides a pan-cancer landscape of the FARSB gene by integrated single-cell and bulk DNA sequencing analysis and elucidates its biological function to promote DNA damage repair and construct the immune desert TME, suggesting the potential value of FARSB as a novel marker for stratifying patients with poor immunotherapeutic benefits and "cold" TME.

Aneuploid subtypes of circulating tumor cells and circulating tumor-derived endothelial cells predict the overall survival of advanced lung cancer.

Objective: This study aimed to detect circulating tumor cells (CTCs) and circulating tumor-derived endothelial cells (CTECs) in patients with advanced lung cancer, for describing the distribution characteristics of CTC and CTEC subtypes, exploring the correlation between CTC/CTEC subtypes and novel prognostic biomarkers. Methods: A total of 52 patients with advanced lung cancer were enrolled in this study. Using the subtraction enrichment-immunofluorescence in situ hybridization (SE-iFISH) system, CTCs and CTECs derived from these patients were identified. Results: Based on cell size, there were 49.3% small and 50.7% large CTCs, and 23.0% small and 77.0% large CTECs. Triploidy, tetraploidy, and multiploidy varied in the small and large CTCs/CTECs. Besides these three aneuploid subtypes, monoploidy was found in the small and large CTECs. Triploid and multiploid small CTCs and tetraploid large CTCs were associated with shorter overall survival (OS) in patients with advanced lung cancer. However, none of the CTECs subtypes showed a significant correlation with patient prognosis. In addition, we found strong positive correlations (P<0.0001) in the four groups including triploid small cell size CTCs and multiploid small cell size CTECs, and multiploid small cell size CTCs and monoploid small cell size CTECs. Furthermore, combined detection of the specific subtypes, including triploid small CTC and monoploid small CTEC, triploid small CTC and triploid small CTEC, and multiploid small CTC and monoploid small CTEC, were associated with poor prognosis in advanced lung cancer. Conclusions: Aneuploid small CTCs are associated with the outcome of patients with advanced lung cancer. In particular, the combined detection of triploid small CTCs and monoploid small CTECs, triploid small CTCs and triploid small CTECs, and multiploid small CTCs and monoploid small CTECs has clinical significance for predicting prognosis in patients with advanced lung cancer.

Advances in artificial intelligence models and algorithms in the field of optometry.

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis.

BACKGROUND: Lung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments. METHODS: The expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes. RESULTS: A total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis. CONCLUSION: A novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.