Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL). METHODS: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model. RESULTS: A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type. CONCLUSION: Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

METTL3-mediated m6A mRNA contributes to the resistance of carbon-ion radiotherapy in non-small-cell lung cancer.

Lung cancer is one of the leading causes of death among cancer patients worldwide. Carbon-ion radiotherapy is a radical nonsurgical treatment with high local control rates and no serious adverse events. N6-methyladenosine (m6A) modification is one of the most common chemical modifications in eukaryotic messenger RNA (mRNA) and has important effects on the stability, splicing, and translation of mRNAs. Recently, the regulatory role of m6A in tumorigenesis has been recognized more and more. However, the dysregulation of m6A and its role in carbon-ion radiotherapy of non-small-cell lung cancer (NSCLC) remains unclear. In this study, we found that the level of methyltransferase-like 3 (METTL3) and its mediated m6A modification were elevated in NSCLC cells with carbon-ion radiotherapy. Knockdown of METTL3 in NSCLC cells impaired proliferation, migration, and invasion in vitro and in vivo. Moreover, we found that METTL3-mediated m6A modification of mRNA inhibited the decay of H2A histone family member X (H2AX) mRNA and enhanced its expression, which led to enhanced DNA damage repair and cell survival.

Genetic influence of meningioma on cisplatin resistance: a Mendelian randomization analysis.

BACKGROUND: Although various aspects of cisplatin resistance have been studied, the impact of genetic variations still needs to be explored. AIM: This study aimed to investigate the impact of cisplatin on meningiomas using a two-sample Mendelian randomization (MR) approach, employing genetic variants associated with cisplatin use as instrumental variables. METHOD: We conducted a two-sample MR analysis using genome-wide association study (GWAS) data. Instrumental variables were derived from single-nucleotide polymorphisms (SNPs) associated with meningioma to estimate the causal relationship with cisplatin resistance. Sensitivity analyses were performed to confirm the findings. RESULTS: Genetic predisposition to meningioma significantly increased the risk of cisplatin resistance (odds ratio (OR): 1.63; 95% confidence interval (CI) 1.44-1.85, P < 0.05). Sensitivity analyses supported the causal link. CONCLUSION: This MR study suggests that genetic predisposition to meningioma increases susceptibility to cisplatin resistance. Further research is needed to uncover the mechanisms behind these causal effects.

Causal impact of statins on susceptibility to osteoarthritis: insights from a two-sample Mendelian randomization analysis.

BACKGROUND: Osteoarthritis is a widely prevalent cause of pain and disability among older adults. It is an incurable condition, and most treatments are aimed at alleviating symptoms. AIM: This study aimed to investigate the impact of statins on osteoarthritis by using a two-sample Mendelian randomization approach, using genetic variants associated with statin use as instrumental variables. METHOD: Information on single nucleotide polymorphisms associated with statin medication was obtained from the FinnGen study, and data on osteoarthritis were sourced from the UK Biobank. The inverse variance weighted method was used as the primary analytical approach for the Mendelian randomization analysis. Sensitivity analyses were conducted to evaluate horizontal pleiotropy and heterogeneity. To examine the genetic relationship between statins and osteoarthritis, linkage disequilibrium score regression-based estimates were used. RESULTS: Mendelian randomization analysis indicated a positive effect of statin use on the treatment of osteoarthritis (odds ratio 0.951, 95% confidence interval 0.914-0.99, p < 0.05). This conclusion was supported by various Mendelian randomization methods. Sensitivity analyses revealed no significant directional pleiotropy or influential single nucleotide polymorphisms that could compromise the overall causal inference. Linkage disequilibrium score regression-based estimates suggested a modest genetic correlation between statin use and osteoarthritis (Rg = 0.098, Se = 0.034, p < 0.05), thus reinforcing the robustness of the Mendelian randomization analysis. CONCLUSION: Statins reduce the risk of osteoarthritis, aligning with the results of observational studies. Further research is essential to validate these results and explore the underlying mechanisms in detail.

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.

BACKGROUND: There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important. PURPOSE: This study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients. METHODS: We introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB). Six models were trained to make individualized treatment recommendations for PCa patients. Inverse probability treatment weighting (IPTW) was used to avoid treatment selection bias. RESULTS: 35,236 patients were included. Patients whose actual treatment was consistent with SNB recommendations had better survival outcomes than those who were inconsistent (multivariate hazard ratio (HR): 0.76, 95% confidence interval (CI), 0.64-0.92; IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.95; risk difference (RD): 3.80, 95% CI, 2.48-5.11; IPTW-adjusted RD: 2.17, 95% CI, 0.92-3.35; the difference in restricted mean survival time (dRMST): 3.81, 95% CI, 2.66-4.85; IPTW-adjusted dRMST: 3.23, 95% CI, 2.06-4.45). Keeping other covariates unchanged, patients with 1 ng/mL increase in PSA levels received RP caused 1.77 months increase in the time to 90% mortality, and the similar results could be found in age, Gleason score, tumor size, TNM stages, and metastasis status. CONCLUSIONS: Our highly interpretable and reliable DL model (SNB) may identify patients with PCa who could benefit from RP, outperforming other models and clinical guidelines. Additionally, the DL-based treatment guidelines obtained can provide priori evidence for subsequent studies.

Optimizing adjuvant treatment options for patients with glioblastoma.

Background: This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method. Methods: We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). Results: The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT. Conclusion: Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

Individualized survival prediction and surgery recommendation for patients with glioblastoma.

Background: There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients. Aim: This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection. Methods: We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation. Results: The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group. Conclusion: The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.

Background: The role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level. Objective: The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required. Methods: We introduced the deep survival regression with mixture effects (DSME), a semi-parametric DL model integrating three causal inference methods. Six models were trained to make individualized treatment recommendations. Patients who received treatments in line with the DL models' recommendations were compared with those who underwent treatments divergent from the recommendations. Inverse probability weighting (IPW) was used to minimize bias. The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference. Results: In total, 5269 female patients with MBC were included. DSME was an independent protective factor, outperforming other models in recommending surgery (IPW-adjusted hazard ratio [HR] = 0.39, 95% confidence interval [CI]: 0.19-0.78) and type of surgery (IPW-adjusted HR = 0.66, 95% CI: 0.48-0.93). DSME was superior to other models and traditional guidelines, suggesting a higher proportion of patients benefiting from surgery, especially breast-conserving surgery. The debiased effect of patient characteristics, including age, tumor size, metastatic sites, lymph node status, and breast cancer subtypes, on surgery decision was also quantified. Conclusions: Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed. This method can facilitate the development of efficient, reliable treatment recommendation systems and provide quantifiable evidence for decision-making.

In Situ Redox Synthesis of Highly Stable Au/Electroactive Polyimide Composite and Its Application on 4-Nitrophenol Reduction.

In this study, we developed a series of Au/electroactive polyimide (Au/EPI-5) composite for the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP) using NaBH4 as a reducing agent at room temperature. The electroactive polyimide (EPI-5) synthesis was performed by chemical imidization of its 4,4'-(4.4'-isopropylidene-diphenoxy) bis (phthalic anhydride) (BSAA) and amino-capped aniline pentamer (ACAP). In addition, prepare different concentrations of Au ions through the in-situ redox reaction of EPI-5 to obtain Au nanoparticles (AuNPs) and anchored on the surface of EPI-5 to form series of Au/EPI-5 composite. Using SEM and HR-TEM confirm the particle size (23-113 nm) of the reduced AuNPs increases with the increase of the concentration. Based on CV studies, the redox capability of as-prepared electroactive materials was found to show an increase trend: 1Au/EPI-5 < 3Au/EPI-5 < 5Au/EPI-5. The series of Au/EPI-5 composites showed good stability and catalytic activity for the reaction of 4-NP to 4-AP. Especially, the 5Au/EPI-5 composite shows the highest catalytic activity when applied for the reduction of 4-NP to 4-AP within 17 min. The rate constant and kinetic activity energy were calculated to be 1.1 × 10-3 s-1 and 38.9 kJ/mol, respectively. Following a reusability test repeated 10 times, the 5Au/EPI-5 composite maintained a conversion rate higher than 95%. Finally, this study elaborates the mechanism of the catalytic reduction of 4-NP to 4-AP.

Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.

BACKGROUND: Due to the heterogeneity of low-grade gliomas (LGGs), the lack of randomized control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is currently controversial. AIM: To determine the best choice between subtotal resection (STR) and gross-total resection (GTR) for individual patients and to identify features that are potentially relevant to treatment heterogeneity. METHODS: Patients were enrolled from the SEER database. We used a novel DL approach to make treatment recommendations for patients with LGG. We also made causal inference of the average treatment effect (ATE) of GTR compared with STR. RESULTS: The patients were divided into the Consis. and In-consis. groups based on whether their actual treatment and model recommendations were consistent. Better brain cancer-specific survival (BCSS) outcomes in the Consis. group was observed. Overall, we also identified two subgroups that showed strong heterogeneity in response to GTR. By interpreting the models, we identified numerous variables that may be related to treatment heterogeneity. CONCLUSIONS: This is the first study to infer the individual treatment effect, make treatment recommendation, and guide surgical options through deep learning approach in LGG research. Through causal inference, we found that heterogeneous responses to STR and GTR exist in patients with LGG. Visualization of the model yielded several factors that contribute to treatment heterogeneity, which are worthy of further discussion.

Highly efficient debromination of 4,4'-dibrominated diphenyl ether by organic palygorskite-supported Pd/Fe nanoparticles.

Organic palygorskite (OP)-supported Pd/Fe nanoparticles composite (OP-Pd/Fe) was prepared by stepwise reduction method. The removal capacity of 4,4'-dibrominated diphenyl ether (BDE15) by OP-Pd/Fe was compared with other various materials. For better understanding the possible mechanism, the synthesized and reacted OP-Pd/Fe materials were characterized by TEM, SEM, XRD, and XPS, respectively. The effects of major influencing parameters on the degradation of BDE15 were also studied. Benefit from the synergistic effect of the carrier and bimetallic nanoparticles, BDE15 could be completely debrominated into diphenyl ether (DE) under suitable conditions. A two-stage adsorption/debromination removal mechanism was proposed. The degradation of BDE15 with OP-Pd/Fe was mainly stepwise debromination reaction, and hydrogen transfer mode was assumed as the dominated debromination mechanism. The removal process fitted well to the pseudo first-order kinetic equation. The observed rate constants increased with increasing Pd loading and OP-Pd/Fe dosage while decreased with increasing initial BDE15 concentration, the tetrahydrofuran/water ratio, and the initial pH of the solution. The work provides a new approach for the treatment of PBDEs pollution.

Constructing Hierarchically Porous N-Doped Carbons Derived from Poly(ionic liquids) with the Multifunctional Fe-Based Template for CO2 Adsorption.

Nitrogen-doped hierarchical porous carbons with a rich pore structure were prepared via direct carbonization of the poly(ionic liquid) (PIL)/potassium ferricyanide compound. Thereinto, the bisvinylimidazolium-based PIL was a desirable carbon source, and potassium ferricyanide as a multifunctional Fe-based template, could not only serve as the pore-forming agent, including metallic components (Fe and Fe3C), potassium ions (etching carbon framework during carbonization), and gas generated during the pyrolysis process, but also introduce the N atoms to porous carbons, which were in favor of CO2 capture. Moreover, the hierarchically porous carbon NDPC-1-800 (NDPC, nitrogen-doped porous carbon) had taken advantage of the highest specific surface area, exhibiting an excellent CO2 adsorption capacity and selectivity compared with NDC-800 (NDC, nitrogen-doped carbon) directly carbonized from the pure PIL. Furthermore, its hierarchical porous architectures played an important part in the process of CO2 capture, which was described briefly as follows: the synergistic effect of mesopores and micropores could accelerate the CO2 molecules' transportation and storage. Meanwhile, the appropriate microporous size distribution of NDPC-1-800 was conducive to enhancing CO2/N2 selectivity. This study was intended to open up a new pathway for designing N-doped porous carbons combining both PILs and the multifunctional Fe-based template potassium ferricyanide with wonderful gas adsorption and separation performance.

Investigation on Protic Ionic Liquids as Physical Solvents for Absorption of NO at Low Pressures.

Nitric oxide (NO) absorption in ionic liquids (ILs) is an interesting issue, but little attention has been focused on the removal of NO at low partial pressures. Herein, a series of protic ionic liquids (PILs) based on polyamines as the cation and hydroxybenzenes as the anion were prepared for capturing low-concentration NO (0-0.6 bar). Triethylenetetramine phenolate ([TETAH][PhO]) showed an excellent absorption performance, with low viscosity, fast absorption rate, and high absorption capacity. The experimental solubility data were fitted by the Krichevsky-Kasarnovsky (K-K) equation, and the absorption enthalpy (ΔH) of NO in [TETAH][PhO] was thus calculated to be -43.60 kJ/mol. Density functional theory calculations were further performed to better understand the interaction of [TETAH][PhO] with NO on the molecular level, and the results suggest that the weak interaction of NO with the PIL was induced by the presence of H protons. It is believed that this work may provide a new method for the efficient and reversible absorption of low-concentration NO.

Plasma C-MYC level manifesting as an indicator in progression of breast cancer.

Aim: To investigate whether plasma C-MYC level could be an indicator in clinical progression of breast cancer. Materials & methods: Plasma level of C-MYC expression was detected by quantitative real time PCR and the level of c-myc protein in breast cancer tissues was detected by immunohistochemistry. The expression level of C-MYC mRNA in supernatant of cancer cells culture was measured compared with the nonbreast cancer cells. Results: Plasma C-MYC level was significantly higher in patients with breast cancer than that in the controls, which associated with clinical stages, lymph node status, etc. Receiver operating characteristic curve analysis showed the sensitivity and specificity of plasma C-MYC level for diagnosis of breast cancer were 63.6 and 81.8%, respectively. The expression of c-myc protein in breast cancer tissues was associated with plasma C-MYC level, even C-MYC level in supernatant of cancer cells was elevated. Conclusion: Plasma C-MYC level might be a potential indicator in progression of breast cancer.

MUS81 Participates in the Progression of Serous Ovarian Cancer Associated With Dysfunctional DNA Repair System.

Objective: Methyl methanesulfonate ultraviolet sensitive gene clone 81 (MUS81) is a structure-specific endonuclease that plays a pivotal role in the DNA repair system of cancer cells. In this study, we aim to elucidate the potential association between the dysfunction of MUS81 and the progression of Serous Ovarian Cancer (SOC). Methods: To investigate the association between MUS81 and prognosis of SOC, immunohistochemistry technology and qPCR were used to analyze the level of MUS81 expression, and transcriptional profile analysis and protein interaction screening chip were used to explore the MUS81 related signal pathways. Random amplified polymorphic DNA (RAPD) analysis, immunofluorescence and comet assays were further performed to evaluate genomic instability and DNA damage status of transduced SOC cells. Experiments both in vitro and in vivo were conducted to verify the impact of MUS81 silencing on chemotherapeutic drug sensitivity of SOC. Results: The overexpression of MUS81 in SOC tissues was related to poor clinical outcomes. The transcriptional chip data showed that MUS81 was involved in multiple pathways associated with DNA repair. Deficiency of MUS81 intensified the genome instability of SOC cells, promoted the emergence of DSBs and restrained the formation of RAD51 foci in SOC cells with exposure to UV. Furthermore, downregulation of MUS81 enhanced the sensitivity to Camptothecin and Olaparib in SOC cell lines and xenograft model. Conclusions: MUS81 is involved in the progression of SOC and inhibition of MUS81 could augment the susceptibility to chemotherapeutic agents. MUS81 might represent a novel molecular target for SOC chemotherapy.

Probing deep into the binding mechanisms of folic acid with α-amylase, pepsin and trypsin: An experimental and computational study.

The inhibitions of folic acid (FA) towards three digestive enzymes, including α-amylase, pepsin and trypsin, were examined. The results showed that FA was able to reduce the enzymatic activity of α-amylase, pepsin, and trypsin by the formation of FA-enzyme complexes. The fluorescence spectral data indicated that the binding of FA with α-amylase, pepsin and trypsin resulted in strong fluorescence quenching of Tyr and Trp residues by hydrophobic interactions, hydrogen bonding and electrostatic interactions. To identify the precise binding sites of FA on α-amylase, pepsin and trypsin, the molecular modeling studies were also performed in this work. These investigations may constitute meaningful work for further advances in the mechanisms behind the interactions between FA and digestive enzymes.

Distribution of Lewis and Secretor polymorphisms and corresponding CA19-9 antigen expression in a Chinese population.

The Lewis (FUT3) and Secretor (FUT2) genes, corresponding to secretion of Lewis ABO (H) histo-blood group antigen CA19-9, are highly polymorphic with differences between populations. In this study, the FUT3 and FUT2 genes in 316 Chinese participants were sequenced to detect polymorphisms, and the associated CA19-9 antigen secretion was also measured. In total, 14 genotypes of FUT3 and 10 genotypes of FUT2 were verified. Le/Le, Le/le59,508 and Le/le59 were the main genotypes of FUT3 with frequencies of 53.2%, 10.7% and 3.5%, respectively. Se/Se, Se/se385 and se385/se385 were the main genotypes of FUT2, with frequencies of 21.4%, 18.6% and 16.2%, respectively. The alleles le1067 and le508 were found extensively in the Chinese population, and the frequency of allele se385 was shown to be higher than previously reported. Phenotype analysis revealed that 9.8% of individuals were the Lewis-negative type and 22.5% were the secretor-negative type. Combined phenotypes showed that 3.2% of participants were of 'double-negative' phenotype (le, se) and 19.3% were of single dominant non-secretor phenotype (Le, se). Serum Lewis antigen CA19-9 levels were significantly different between subgroups and consistent with the defined phenotype. Our study revealed the unique distribution of Lewis and Secretor polymorphisms in a large Chinese population, and decoded the combined genotypes of the two CA19-9-related genes.