Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.

Purpose: The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data. Methods: 2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software. Results: Male, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed. Conclusions: Gender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.

Progesterone receptor impairs immune respond and down-regulates sensitivity to anti-LAG3 in breast cancer.

BACKGROUND: Progesterone receptor (PR) serves as a crucial prognostic and predictive marker in breast cancer. Nonetheless, the interplay between PR and the tumor immune microenvironment remains inadequately understood. This investigation employs bioinformatics analyses, mouse models, and clinical specimens to elucidate the impact of PR on immune microenvironment and identify potential targets for immunotherapy, furnishing valuable guidance for clinical practice. METHODS: Analysis of immune infiltration score by Xcell between PR-positive and PR-negative breast cancer tumors. Construction of overexpression mouse progesterone receptor (mPgr) EMT-6 cell was to explore the tumor immune microenvironment. Furthermore, anti- Lymphocyte-activation gene 3 (LAG3) therapy aimed to investigate whether PR could influence the effectiveness of immune treatments. RESULTS: Overexpression mPgr inhibited tumor growth in vitro, but promoted tumor growth in Balb/c mouse. Flow cytometry showed that the proportion and cytotoxicity of CD8+T cells in tumor of overexpressing mPgr group were significantly reduced. The significant reduction in overexpressing mPgr group was found in the proportions of LAG3+CD8+ T cells and LAG3+ Treg T cells. Anti-LAG3 treatment resulted in reduced tumor growth in EV group mouse rather than in overexpressing mPgr group. Patents derived tumor fragment (PDTF) also showed higher anti-tumor ability of CD3+T cell in patents' tumor with PR <20% after anti-human LAG3 treatment in vitro. CONCLUSIONS: The mPgr promotes tumor growth by downregulating the infiltration and function of cytotoxic cell. LAG3 may be a target of ER-positive breast cancer immunotherapy. The high expression of PR hinders the sensitivity to anti-LAG3 treatment.

Characterizing the shear response of polymer-grafted nanoparticles.

Grafting polymer chains to the surface of nanoparticles overcomes the challenge of nanoparticle dispersion within nanocomposites and establishes high-volume fractions that are found to enable enhanced material mechanical properties. This study utilizes coarse-grained molecular dynamics simulations to quantify how the shear modulus of polymer-grafted nanoparticle (PGN) systems in their glassy state depends on parameters such as strain rate, nanoparticle size, grafting density, and chain length. The results are interpreted through further analysis of the dynamics of chain conformations and volume fraction arguments. The volume fraction of nanoparticles is found to be the most influential variable in deciding the shear modulus of PGN systems. A simple rule of mixture is utilized to express the monotonic dependence of shear modulus on the volume fraction of nanoparticles. Due to the reinforcing effect of nanoparticles, shortening the grafted chains results in a higher shear modulus in PGNs, which is not seen in linear systems. These results offer timely insight into calibrating molecular design parameters for achieving the desired mechanical properties in PGNs.

Development of a Cation Exchange SPE-HILIC-MS/MS Method for the Determination of Ningnanmycin Residues in Tea and Chrysanthemum.

Ningnanmycin is a widely used antibiotic in agricultural production that effectively controls fungal and viral diseases in tea trees and chrysanthemums. The polarity characteristic of ningnanmycin has posed limitations on the development of robust detection methods, thereby hindering effective monitoring and control measures. By combining cation exchange solid phase extraction (SPE) with hydrophilic interaction chromatography tandem mass spectrometry (HILIC-MS/MS), we have effectively tackled the issue pertaining to the separation and retention of ningnanmycin. The average recoveries of ningnanmycin in green tea, black tea, and chrysanthemum were 77.3-82.0%, 80.1-81.5%, and 74.0-80.0%, respectively. The intraday and interday relative standard deviations (RSDs) were below and equal to 7.7%. Good linearity was observed in the concentration range of 1-1000 µg/L (R2 > 0.998). The limits of detection (LODs) ranged from 1.1 µg/kg to 7.1 µg/kg, and the limits of quantification (LOQs) ranged from 3.6 µg/kg to 23.7 µg/kg for ningnanmycin. These results indicate the good accuracy, repeatability, reproducibility, and sensitivity of the method. It is suitable for detecting ningnanmycin in tea and chrysanthemum.

Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation.

In the realm of multiscale molecular simulations, structure-based coarse-graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems, such as polymers. This involves optimizing CG interactions by matching static correlation functions of the corresponding degrees of freedom in all-atom (AA) models. Here, we present a versatile method, namely, differentiable coarse-graining (DiffCG), which combines multiobjective optimization and differentiable simulation. The DiffCG approach is capable of constructing robust CG models by iteratively optimizing the effective potentials to simultaneously match multiple target properties. We demonstrate our approach by concurrently optimizing bonded and nonbonded potentials of a CG model of polystyrene (PS) melts. The resulting CG-PS model effectively reproduces both the structural characteristics, such as the equilibrium probability distribution of microscopic degrees of freedom and the thermodynamic pressure of the AA counterpart. More importantly, leveraging the multiobjective optimization capability, we develop a precise and efficient CG model for PS melts that is transferable across a wide range of temperatures, i.e., from 400 to 600 K. It is achieved via optimizing a pairwise potential with nonlinear temperature dependence in the CG model to simultaneously match target data from AA-MD simulations at multiple thermodynamic states. The temperature transferable CG-PS model demonstrates its ability to accurately predict the radial distribution functions and density at different temperatures, including those that are not included in the target thermodynamic states. Our work opens up a promising route for developing accurate and transferable CG models of complex soft-matter systems through multiobjective optimization with differentiable simulation.

Cediranib ameliorates portal hypertensive syndrome via inhibition of VEGFR-2 signaling in cirrhotic rats.

Portal hypertension (PHT) is a syndrome caused by systemic and portal hemodynamic disturbances with the progression of cirrhosis. However, the exact mechanisms regulating angiogenesis-related responses in PHT remain unclear. Cediranib is a potent inhibitor of vascular endothelial growth factor receptor (VEGFR) tyrosine kinases, exhibiting a greater affinity for VEGFR-2. Liver cirrhosis was induced by common bile duct ligation (BDL) in Sprague-Dawley rats. Sham-operated rats were controls. BDL and sham rats were randomly allocated to receive Cediranib or vehicle after BDL. On the 28th day, portal hypertension related parameters were surveyed. Cediranib treatment could significantly reduce the portal pressure (PP) in BDL rats, while it did not affect the mean arterial pressure (MAP) in sham groups and BDL groups. Cediranib treatment could significantly affect the stroke volume (SV), cardiac output (CO), cardiac index (CI), systemic vascular resistance (SVR), superior mesenteric artery (SMA) flow and SMA resistance in BDL groups and BDL with Cediranib groups. Cediranib treatment could improve the mesenteric vascular remodeling and contractility. Cediranib treatment significantly reduced mesenteric vascular density. And phospho-VEGFR-2 was significantly downregulated by Cediranib. On the other hand, phospho-endothelial Nitric Oxide Synthases (phospho-eNOS) expressions were upregulated. Cediranib not only improved splanchnic hemodynamics, extrahepatic vascular remodeling and vasodilation, but also alleviated intrahepatic fibrosis and collagen deposition significantly. Cediranib treatment could reduce intrahepatic angiogenesis between BDL-vehicle and BDL-Cediranib rats. In conclusion, Cediranib could improve extrahepatic hyperdynamic circulation by inhibiting extrahepatic angiogenesis through inhibition of the VEGFR-2 signaling pathway, portal collateral circulation formation, as well as eNOS-mediated vasodilatation and vascular remodeling, and at the same time, Cediranib improved intrahepatic fibrogenesis and angiogenesis, which together alleviate cirrhotic PHT syndrome.

Construction and validation of nomograms to reduce completion thyroidectomy by predicting lymph node metastasis in low-risk papillary thyroid carcinoma.

CONTEXT: More than 5 central lymph nodes metastases (CLNM) or lateral lymph node metastasis (LLNM) indicates a higher risk of recurrence in low-risk papillary thyroid carcinoma (PTC) and may lead to completion thyroidectomy (CTx) in patients initially undergoing lobectomy. OBJECTIVE: To screen potentially high-risk patients from low-risk patients by using preoperative and intraoperative clinicopathological features to predict lymph node status. METHODS: A retrospective analysis of 8301 PTC patients in Wuhan Union Hospital database (2009-2021) was performed according to the 2015 American Thyroid Association (ATA) and 2021 National Comprehensive Cancer Network (NCCN) guidelines, respectively. Logistic regression and best subsets regression were used to identify risk factors. Nomograms were established and externally validated using the Differentiated Thyroid Cancer in China cohort. RESULTS: More than 5 CLNM or LLNM was detected in 1648 (19.9%) patients. Two predictive models containing age, gender, maximum tumor size, free thyroxine (FT4) and palpable node (all p < 0.05) were established. The nomogram based on NCCN criteria showed better discriminative power and consistency with a specificity of 0.706 and a sensitivity of 0.725, and external validation indicated that 76% of potentially high-risk patients could achieve preoperative conversion of surgical strategy. CONCLUSIONS: Models based on large cohorts with good predictive performance were constructed and validated. Preoperative low-risk (T1-2N0M0) patients with age younger than 40 years, male gender, large tumor size, low FT4 and palpable nodes may be at high risk of LLNM or more than 5 CLNM, and they should receive more aggressive initial therapy to reduce CTx.

Near-infrared-excitable acetylcholinesterase-activated fluorescent probe for sensitive and anti-interference detection of pesticides in colored food.

The development of a common and anti-interference acetylcholinesterase (AChE) inhibition assay for plant-originated food samples has been of great challenge because of the prevalent and strong signal interferences from natural pigments. Plant pigments normally exhibit non-negligible absorbance in the UV-visible region. As a result, the signals of a typical near-infrared (NIR) fluorescent probe could be disturbed through primary inner filter effect if it is excited by UV-visible light during plant sample analysis. In this work, an NIR-excitable AChE-activated fluorescent probe was biomimetically designed and synthesized. And the NIR-excitation strategy was utilized for the anti-interference detection of organophosphate and carbamate pesticides in colored samples with this probe. Sensitive and rapid response to AChE and pesticides was achieved due to the high affinity of the biomimetic recognition unit in the probe. The limits of detection for four representative pesticides including dichlorvos, carbofuran, chlorpyrifos and methamidophos reached 0.0186 µg/L, 2.20 µg/L, 12.3 µg/L and 13.6 µg/L, respectively. Most importantly, fluorescent response to pesticide contents could be accurately measured in the coexistence of different plant pigments by this probe, and the measured results showed completely irrelevance to the plant pigments and their colors. Taking advantage of such probe, the new developed AChE inhibition assay showed good sensitivity and anti-interference ability in the detection of organophosphate and carbamate pesticides in real samples.

Association between COVID-19 and chronic liver disease: Mechanism, diagnosis, damage, and treatment.

As the outbreak evolves, our understanding of the consequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the resulting coronavirus disease 2019 (COVID-19) on the liver has grown. In this review, we discussed the hepatotropic nature of SARS-CoV-2 and described the distribution of receptors for SARS-CoV-2 (e.g., angiotensin-converting enzyme 2) in the vascular endothelium and cholangiocytes of the liver. Also, we proposed mechanisms for possible viral entry that mediate liver injury, such as liver fibrosis. Due to SARS-CoV-2-induced liver damage, many COVID-19 patients develop liver dysfunction, mainly characterized by moderately elevated serum aminotransferase levels. Patients with chronic liver disease (CLD), such as cirrhosis, hepatocellular carcinoma, nonalcoholic fatty liver disease, and viral hepatitis, are also sensitive to SARS-CoV-2 infection. We discussed the longer disease duration and higher mortality following SARS-CoV-2 infection in CLD patients. Correspondingly, relevant risk factors and possible mechanisms were proposed, including cirrhosis-related immune dysfunction and liver deco-mpensation. Finally, we discussed the potential hepatotoxicity of COVID-19-related vaccines and drugs, which influence the treatment of CLD patients with SARS-CoV-2 infection. In addition, we suggested that COVID-19 vaccines in terms of immunogenicity, duration of protection, and long-term safety for CLD patients need to be further researched. The diagnosis and treatment for liver injury caused by COVID-19 were also analyzed in this review.

Learning pair potentials using differentiable simulations.

Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim). DiffSim defines a loss function based on structural observables, such as the radial distribution function, through molecular dynamics (MD) simulations. The interaction potentials are then learned directly by stochastic gradient descent, using backpropagation to calculate the gradient of the structural loss metric with respect to the interaction potential through the MD simulation. This gradient-based method is flexible and can be configured to simulate and optimize multiple systems simultaneously. For example, it is possible to simultaneously learn potentials for different temperatures or for different compositions. We demonstrate the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions. We find that DiffSim can be used to probe a wider functional space of pair potentials compared with traditional methods like iterative Boltzmann inversion. We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.

Combination of VMD Mapping MFCC and LSTM: A New Acoustic Fault Diagnosis Method of Diesel Engine.

Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals.

Diagnosis and Treatment of Inguinal Hernias after Surgical Treatment of Prostate Cancer, Current State of the Problem.

(1) Purpose: To compare and evaluate the immediate and long-term results of the use of various hernioplasties for the treatment of inguinal hernias after surgical treatment of prostate cancer; to determine the possibility of performing transabdominal preperitoneal (TAPP) hernioplasty and total extraperitoneal (eTEP) hernioplasty in patients with inguinal hernia during surgical treatment of prostate cancer. (2) Method: This study is a clinical analytical prospective study, without the use of randomization. The study included 220 patients with inguinal hernia, who were randomly divided into two groups (group A (n = 100) and group B (n = 120)). Patients in group A received eTEP, and those in group B received TAPP. The end points of the study were the results associated with the operation itself and the prognosis of the disease in the two groups. (3) Results: Group A: five patients had a scrotal hematoma, in 10 cases nosocomial pneumonia or infectious complications from the postoperative wound. The overall rate of early postoperative complications was 15%. In group B, the following postoperative complications were reported: one case of intestinal injury, six cases of acute urinary retention, eight cases of scrotal hematoma and 12 cases of nosocomial pneumonia or infectious complications from the postoperative wound were admitted. The overall incidence of early postoperative complications was 22.5%. There was no statistically significant difference in the incidence of postoperative complications between the two groups (χ2 (3) = 2.54, p > 0.05). (4) Conclusion: During the analysis of the obtained results, no statistically significant difference was found in the duration of hospitalization, the volume of blood loss, the severity of pain syndrome, postoperative complication incidence and recurrence incidence (p > 0.05); however, the comparison groups differed in the duration of the operation: the operation time in group A was much longer compared to group B (p < 0.05).

Construction of a miRNA-Based Nomogram Model to Predict the Prognosis of Endometrial Cancer.

Objective: To investigate the differential expression of microRNA (miRNA) in patients with endometrial cancer and its relationship with prognosis and survival. Method: We used The Cancer Genome Atlas (TCGA) database to analyze differentially expressed miRNAs in endometrial cancer tissues and adjacent normal tissues. In addition, we successfully screened out key microRNAs to build nomogram models for predicting prognosis and we performed survival analysis on the key miRNAs as well. Result: We identified 187 differentially expressed miRNAs, which includes 134 up-regulated miRNAs and 53 down-regulated miRNAs. Further univariate Cox regression analysis screened out 47 significantly differentially expressed miRNAs and selected 12 miRNAs from which the prognostic nomogram model for ECA patients by LASSO analysis was constructed. Survival analysis showed that high expression of hsa-mir-138-2, hsa-mir-548f-1, hsa-mir-934, hsa-mir-940, and hsa-mir-4758 as well as low-expression of hsa-mir-146a, hsa-mir-3170, hsa-mir-3614, hsa-mir-3616, and hsa-mir-4687 are associated with poor prognosis in EC patients. However, significant correlations between the expressions levels of has-mir-876 and hsa-mir-1269a and patients' prognosis are not found. Conclusion: Our study found that 12 significantly differentially expressed miRNAs might promote the proliferation, invasion, and metastasis of cancer cells by regulating the expression of upstream target genes, thereby affecting the prognosis of patients with endometrial cancer.

sGRP78 enhances selective autophagy of monomeric TLR4 to regulate myeloid cell death.

Soluble glucose regulated protein 78 (sGRP78) has long been suggested as a mediator resolution of inflammation. We previously reported that sGRP78 induced the rapid endocytosis of TLR4 with defective TLR4 signaling. To elucidate the underlying mechanisms, in this study, we investigated how sGRP78 influenced the behavior and trafficking of TLR4 in myeloid cells. It was found that sGRP78 promoted LPS endocytosis with monomeric TLR4. This internalized monomeric TLR4 formed complexes with p62-LC3, and was degraded in autolysosomes. Furthermore, the sGRP78-enhanced autophagy-dependent TLR4 degradation caused apoptosis and ferroptosis in myeloid cells, contributing to the sGRP78-mediated resolution of inflammation. These reports establish innovative mechanisms for endotoxin clearance and immune regulation by TLR4 degradation, linking innate immunity with multiple ancient processes, including autophagy, apoptosis, and ferroptosis, together through a shared resolution-associated molecular pattern (RAMP)-sGRP78.

Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis.

Background: The long non-coding RNA (lncRNA)-mRNA regulation network plays an important role in the development of diffuse large B-cell lymphoma (DLBCL). This study uses bioinformatics to find an innovative regulation axis in DLBCL that will provide a positive reference for defining the mechanism of disease progression. Methods: Batch Cox regression was used to screen prognosis-related lncRNAs, and a random forest model was used to identify hub lncRNA. The clinical value of the lncRNA was evaluated and Spearman correlation analysis was used to predict the candidate target genes. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were used to define the biological function of the lncRNA. A batch Cox regression model, expression validation, and Spearman correlation analysis were used to select the best downstream target genes. The expression and prognostic value validation of this gene was conducted using public data. Gene Set Enrichment Analysis (GSEA) was performed to explore potential mechanisms for this gene in DLBCL. Results: LINC00654 was identified as the hub lncRNA and 1443 mRNAs were selected as downstream target genes of the lncRNA. The target genes were enriched in the regulation of GTPase and Notch signaling pathways. After validation, the ninein-like (NINL) gene was selected as the potential target of LINC00654 and the LINC00654-NINL axis was constructed. Patients with better responses to therapy were shown to have high NINL gene expression (p-value = 0.036). NINL also had high expression in the DB cell line and low expression in the OCILY3 cell line. Survival analysis showed that high NINL expression was a risk factor for overall survival (OS) and disease-specific survival (DSS) within older patients and those with advanced-stage cancer. GSEA results showed that NINL may be involved in neutrophil-mediated immunity and NF-κB signaling. Conclusion: This study identified a novel LncRNA00654-NINL regulatory axis in DLBCL, which could provide a favorable reference for exploring the possible mechanisms of disease progression.

CD20+CD22+ADAM28+ B Cells in Tertiary Lymphoid Structures Promote Immunotherapy Response.

Background: As the indication for immunotherapy is rapidly expanding, it is crucial to accurately identify patients who are likely to respond. Infiltration of B cells into many tumor types correlates with a good response to immune checkpoint inhibitor (ICI) therapy. However, B cells' roles in the anti-tumor response are far from clear. Methods: Based on single-cell transcriptomic data for ICI-treated patients, we identified a B-cell cluster [BIR (ICI-Responsive B) cells] and described the phenotype, cell-cell communication, biological processes, gene signature, and prognosis value of BIR cells through bioinformatic analysis, tissue immunofluorescence, and animal experiments. Surgery samples from 12 non-small cell lung carcinoma (NSCLC) patients with adjuvant checkpoint blockade were evaluated as external validation. Results: BIR cells were identified as a subset of CD20+CD22+ADAM28+ B cells with a memory phenotype. Bioinformatic analysis revealed that BIR cells had enhanced cell viability and epigenetic regulation, and that ALOX5AP, MIF, and PTPRC/CD45 expressed by myeloid cells may be critical coordinators of diverse biological processes of BIR cells. Immunofluorescence confirmed the presence of BIR cells in tertiary lymphoid structures (TLSs) in skin SCC, RCC, CRC, and breast cancer. BIR-associated gene signatures correlate with positive outcomes in patients with melanoma, glioblastoma, NSCLC, HNSCC, or RCC treated with ICI therapy, and BIR-cell density predicted NSCLC patients' response to checkpoint immunotherapy. In line with this, melanoma-bearing mice depleted of BIR cells were resistant to ICIs. Conclusions: CD20+CD22+ADAM28+ BIR cells were present in cancer-associated TLS and promoted the response to ICI therapy.

Slip-Spring Hybrid Particle-Field Molecular Dynamics for Coarse-Graining Branched Polymer Melts: Polystyrene Melts as an Example.

The topology of chains significantly modifies the dynamical properties of polymer melts. Here, we extend a recently developed efficient simulation method, namely the slip-spring hybrid particle-field (SS-hPF) model, to study the structural and dynamical properties of branched polymer melts over large spatial-temporal scales. In the coarse-grained SS-hPF simulation of polymers, the bonded potentials are derived by iterative Boltzmann inversion from the underlying fine-grained model. The nonbonded potentials are computed from a density functional field instead of pairwise interactions used in standard molecular dynamics simulations, which increases the computational efficiency by a factor of 10-20. The entangled dynamics is lost due to the soft-core nature of density functional field interactions. It is recovered by a multichain slip-spring model that is rigorously parametrized from existing experimental or simulation data. To quantitatively predict the relaxation and diffusion of branched polymers, which are dominated by arm retraction rather than chain reptation, the slip-spring algorithm is augmented to improve the polymer dynamics near the branch point. Multiple dynamical observables, e.g., diffusion coefficients, arm relaxations, and tube survival probabilities, are characterized in an example coarse-grained model of symmetric and asymmetric star-shaped polystyrene melts. Consistent dynamical behaviors are identified and compared with theoretical predictions. With a single rescaling factor, the prediction of diffusion coefficients agrees well with the available experimental measurements. In this work, an efficient approach is provided to build chemistry-specific coarse-grained models for predicting the dynamics of branched polymers.

How Ethanolic Disinfectants Disintegrate Coronavirus Model Membranes: A Dissipative Particle Dynamics Simulation Study.

We have developed dissipative particle dynamics models for pure dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and dimyristoylphosphatidylcholine (DMPC) as well as their binary and ternary mixed membranes, as coronavirus model membranes. The stabilities of pure and mixed membranes, surrounded by aqueous solutions containing up to 70 mol % ethanol (alcoholic disinfectants), have been investigated at room temperature. We found that aqueous solutions containing 5-10 mol % ethanol already have a significant weakening effect on the pure and mixed membranes. The magnitude of the effect depends on the membrane composition and the ethanol concentration. Ethanol permeabilizes the membrane, causing its lateral swelling and thickness shrinking and reducing the orientational order of the hydrocarbon tail of the bilayer. The free energy barrier for the permeation of ethanol in the bilayers is considerably reduced by the ethanol uptake. The rupture-critical ethanol concentrations causing the membrane failure are 20.7, 27.5, and 31.7 mol % in the aqueous phase surrounding pure DMPC, DOPC, and DPPC membranes, respectively. Characterizing the failure of lipid membranes by a machine-learning neural network framework, we found that all mixed binary and/or ternary membranes disrupt when immersed in an aqueous solution containing a rupture-critical ethanol concentration, ranging from 20.7 to 31.7 mol %, depending on the composition of the membrane; the DPPC-rich membranes are more intact, while the DMPC-rich membranes are least intact. Due to the tight packing of long, saturated hydrocarbon tails in DPPC, increasing the DPPC content of the mixed membrane increases its stability against the disinfectant. At high DPPC concentrations, where the DOPC and DMPC molecules are confined between the DPPC lipids, the ordered hydrocarbon tails of DPPC also induce order in the DOPC and DMPC molecules and, hence, stabilize the membrane more. Our simulations on pure and mixed membranes of a diversity of compositions reveal that a maximum ethanol concentration of 32 mol % (55 wt %) in the alcohol-based disinfectants is enough to disintegrate any membrane composed of these three lipids.

Influence of progesterone receptor on metastasis and prognosis in breast cancer patients with negative HER-2.

BACKGROUND: In de novo metastatic breast cancer patients, the site of metastasis and prognosis are related to the molecular subtype of breast cancer. There are few relevant reports to explore the clinicopathological and prognostic characteristics of different single positive hormone receptor subtypes [estrogen receptor (ER)+/progesterone receptor (PR)- and ER-/PR+] of metastatic breast cancer. METHODS: Using the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2015.We analyzed the metastatic patterns and prognosis of human epidermal growth factor receptor 2 (HER-2)-negative breast cancer patients. Cox analysis was used to analyze the influence of ER+/PR- and ER-/PR+ on the prognosis of patients in different subgroups and the risk factors affecting the prognosis of patients with single hormone receptor positivity. RESULTS: We included 206,187 breast cancer patients, including 7,726 stage IV patients. The loss of ER was a protective factor against bone metastasis (P<0.001) and a risk factor for visceral metastasis (P<0.001). The ER-/PR+ subtype had a similar proportion of de novo metastatic breast cancer, and similar clinicopathological characteristics, prognosis with triple negative breast cancer (TNBC). Single PR positivity was an independent risk factor for cancer specific survival (CSS) in multi-visceral metastasis subgroup comparing to TNBC. Meanwhile, no significant difference in overall survival (OS) or breast cancer specific survival (BCSS) between ER-/PR+ and ER-/PR- patients in all breast cancer patients or in stage IV breast cancer patients. Age [hazard ratio (HR) =2.16], grade (HR =2.36), T stage (T4: HR =3.24), lymph node metastasis (>10: HR =4.33), distant metastasis (HR =4.99), and no chemotherapy or an unknown (HR =1.65) were high-risk factors but surgery (HR <0.5) were protective factors for CSS in ER-/PR+ patients. CONCLUSIONS: ER-/PR+ subtype had a high proportion of stage IV patients. Meanwhile, such subtype breast cancer had similar clinicopathological characteristics, metastatic models (prefers to visceral metastasis), similar even worse prognosis compared with TNBC.

TSH-TSHR axis promotes tumor immune evasion.

BACKGROUND: Hormones are identified as key biological variables in tumor immunity. However, previous researches mainly focused on the immune effect of steroid hormones, while the roles that thyroid-stimulating hormone (TSH) played in the antitumor response were far from clear. METHODS: The source of TSH was determined using single-cell transcriptomic, histologic, quantitative PCR, and ELISA analysis. The influence of TSH on tumor proliferation, invasion, and immune evasion was evaluated in multiple cell lines of thyroid cancer, glioma, and breast cancer. Then transcriptomic sequencing and cellular experiments were used to identify signaling pathways. TSH receptor (TSHR) inhibitor was injected into homograft mouse tumor models with or without anti-programmed cell death protein-1 antibody. RESULTS: Monocyte-derived dendritic cells (moDCs) highly expressed TSHα and TSHß2 and were the primary source of TSH in the tumor microenvironment. TSH released by moDCs promoted proliferation and invasion of tumors with high TSHR expressions, such as thyroid cancers and glioma. TSH also induced tumor programmed death-ligand 1 (PD-L1) expression through the TSHR-AC-PKA-JNK-c-JUN pathway. TSHR inhibitors reversed tumor immune evasion by inhibiting PD-L1 expression in tumor and myeloid cells and enhancing Teff activation. CONCLUSIONS: TSH-TSHR axis promotes tumor evasion in thyroid cancers and glioma. TSH suppression therapy is an effective therapeutic strategy for combination in immune checkpoint blockades.