An ultrasound-activated nanoplatform remodels tumor microenvironment through diverse cell death induction for improved immunotherapy.

Although nanomaterial-based nanomedicine provides many powerful tools to treat cancer, most focus on the "immunosilent" apoptosis process. In contrast, ferroptosis and immunogenic cell death, two non-apoptotic forms of programmed cell death (PCD), have been shown to enhance or alter the activity of the immune system. Therefore, there is a need to design and develop nanoplatforms that can induce multiple modes of cell death other than apoptosis to stimulate antitumor immunity and remodel the immunosuppressive tumor microenvironment for cancer therapy. In this study, a new type of multifunctional nanocomposite mainly consisting of HMME, Fe3+ and Tannic acid, denoted HFT NPs, was designed and synthesized to induce multiple modes of cell death and prime the tumor microenvironment (TME). The HFT NPs consolidate two functions into one nano-system: HMME as a sonosensitizer for the generation of reactive oxygen species (ROS) 1O2 upon ultrasound irradiation, and Fe3+ as a GSH scavenger for the induction of ferroptosis and the production of ROS ·OH through inorganic catalytic reactions. The administration of HFT NPs and subsequent ultrasound treatment caused cell death through the consumption of GSH, the generation of ROS, ultimately inducing apoptosis, ferroptosis, and immunogenic cell death (ICD). More importantly, the combination of HFT NPs and ultrasound irradiation could reshape the TME and recruit more T cell infiltration, and its combination with immune checkpoint blockade anti-PD-1 antibody could eradicate tumors with low immunogenicity and a cold TME. This new nano-system integrates sonodynamic and chemodynamic properties to achieve outstanding therapeutic outcomes when combined with immunotherapy. Collectively, this study demonstrates that it is possible to potentiate cancer immunotherapy through the rational and innovative design of relatively simple materials.

Frailty and transplant-free survival of patients with liver cirrhosis: A meta-analysis.

BACKGROUND: Frailty is a common condition among patients with liver cirrhosis. Nonetheless, its role in predicting liver transplant-free survival (TFS) remains unclear. AIM: This systematic review and meta-analysis were conducted to elucidate the relationship between frailty and TFS in patients with cirrhosis. METHODS: Cohort studies addressing the objective of this meta-analysis were extracted from PubMed, Embase, and Web of Science databases. Between-study heterogeneity was assessed with the Cochrane Q test, and the I^2 statistic was estimated. Random-effect models, considering potential heterogeneity, were employed to combine the results. RESULTS: The meta-analysis encompassed 17 cohort studies involving 6273 patients with cirrhosis, of whom 1983 (31.6%) were classified as frail at baseline. The follow-up periods in the included studies ranged from 3 to 29 months, with an average duration of 11.5 months. The analysis revealed that frailty was significantly associated with a poor TFS (risk ratio [RR]: 2.07, 95% confidence interval: 1.72 to 2.50, p<0.001; I2 = 51%). Sensitivity analyses that sequentially omitted one dataset consistently supported these findings (RR: 1.95 to 2.17, p<0.05 in all cases). Subgroup analyses based on variables such as study design, mean age of patients, baseline Model for End-Stage Liver Disease score, tool used for frailty evaluation, follow-up duration, and study quality score also yielded congruent results. CONCLUSIONS: The evidence suggests that frailty may be an independent risk factor for poor TFS in patients with liver cirrhosis, thus emphasizing the importance of early identification and management of frailty in this population.

Prohibitin 2 deficiency in photoreceptors leads to progressive retinal degeneration and facilitated Müller glia engulfing microglia debris.

Müller glia and microglia are capable of phagocytosing fragments of retinal cells in response to retinal injury or degeneration. However, the direct evidence for their mutual interactions between Müller glia and microglia in the progression of retinal degeneration (RD) remains largely unclear. This study aims to construct a progressive RD mouse model and investigate the activated pattern of Müller glia and the interplay between Müller glia and microglia in the early stage or progression of RD. A Prohibitin 2 (Phb2) photoreceptor-specific knockout (RKO) mouse model was generated by crossing Phb2flox/flox mice with Rhodopsin-Cre mice. Optical Coherence Tomography (OCT), histological staining, and Electroretinography (ERG) assessed retinal structure and function, and RKO mice exhibited progressive RD from six weeks of age. In detail, six-week-old RKO mice showed no significant retinal impairment, but severe vision dysfunction and retina thinning were shown in ten-week-old RKO mice. Furthermore, RKO mice were sensitive to Light Damage (LD) and showed severe RD at an early age after light exposure. Bulk retina RNA-seq analysis from six-week-old control (Ctrl) and RKO mice showed reactive retinal glia in RKO mice. The activated pattern of Müller glia and the interplay between Müller glia and microglia was visualized by immunohistology and 3D reconstruction. In six-week-old RKO mice or light-exposed Ctrl mice, Müller glia were initially activated at the edge of the retina. Moreover, in ten-week-old RKO mice or light-exposed six-week-old RKO mice with severe photoreceptor degeneration, abundant Müller glia were activated across the whole retinas. With the progression of RD, phagocytosis of microglia debris by activated Müller glia were remarkably increased. Altogether, our study establishes a Phb2 photoreceptor-specific knockout mouse model, which is a novel mouse model of RD and can well demonstrate the phenotype of progressive RD. We also report that Müller glia in the peripheral retina is more sensitive to the early damage of photoreceptors. Our study provides more direct evidence for Müller glia engulfing microglia debris in the progression of RD due to photoreceptor Phb2 deficiency.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

Diesel exhaust particulate matter induces GC-1 spg cells oxidative stress by KEAP1-NRF2 pathway and inhibition of ATP5α1 S-sulfhydration.

Diesel exhaust particle (DEP) exposure induces a variety of toxicological effects through oxidative stress and inflammation responses. This research investigated the mechanisms underlying DEP-induced GC-1spg cells oxidative stress by examining ROS accumulation, antioxidant defense systems activation, mitochondrial dysfunction, and the Nrf2/Keap1/HO-1 pathway response. Subsequently, we further evaluated the ATP levels, ATP5α synthase activity and ATP5α synthase S-sulfhydrated modification in DEP-exposed GC-1 spg cells. The results showed that DEP exposure significantly inhibited cell proliferation and viability, increased intracellular ROS production, decreased MMP, down-regulated antioxidant capacity, activated the Nrf2/Keap1/HO-1 pathway. However, DEP-induced oxidative stress was partially alleviated by GSH and exogenous H2S. In addition, DEP exposure induced ATP depletion and ATP5α synthase inactivity in GC-1 spg cells, accompanied by ATP5α synthase S-sulfhydrated modification. In conclusion, our research showed that DEP may incapacitate mitochondria through oxidative stress injury, leading to GC-1 spg cells oxidative stress. This process may be associated with the reduction of ATP5α1 S-sulfhydrated modification. It provides a new perspective for the research of the mechanism related to male reproductive toxicity due to air pollution.

Effect of orthostatic hypotension on long-term prognosis of elderly patients with stable coronary artery disease: a retrospective cohort study.

Background: The long-term prognosis of patients with stable coronary artery disease (CAD) combined with orthostatic hypotension (OH) has rarely been reported. This research was designed to examine whether OH increases the risk of all-cause mortality and cardiovascular death among patients with stable CAD. Methods: We retrospectively analyzed retired military personnel over 65 years of age who were hospitalized at the General Hospital of Southern Theater Command of the Chinese People's Liberation Army between March and July 2010. A total of 924 patients with stable CAD were included, among whom 263 had OH. The risk of all-cause mortality and cardiovascular death in OH and non-OH groups were analyzed with the Cox proportional hazards models, and restricted cubic spline plots were utilized for subgroup analyses. Furthermore, competing risk models were applied for sensitivity analyses. Results: The median age of the patients was 82.00 (80.00-85.00) years. Over 159 months of follow-up, the loss to follow-up rate was 2.27%, and all-cause mortality was observed in 574 (63.57%) patients, including 184 with OH. Moreover, cardiovascular death occurred in 127 patients (13.73%), with 58 cases associated with OH. Although the relationship between OH and all-cause mortality was non-significant [body mass index (BMI) < 25 group, adjusted hazard ratio (HR) = 1.10 with a 95% confidence interval (CI): 0.82-1.40; BMI ≥ 25 group, adjusted HR = 1.30, 95% CI: 0.98-1.70], it was independently related to a growing risk of cardiovascular death (adjusted HR = 1.80, 95% CI: 1.20-2.60). This finding was further validated by using a competing risk model (subdistribution HR = 1.74, 95% CI: 1.22-2.49). Moreover, age, low-density lipoprotein cholesterol, and frequency of hospital admissions were identified as risk factors of cardiovascular death among patients with OH (P < 0.05). Conclusion: Our study, based on retired military personnel with stable CAD, found that OH led to a significantly higher risk of cardiovascular death, but it was not noticeably associated with all-cause mortality on long-term prognosis.

Effect of S-Ketamine on Postoperative Nausea and Vomiting in Patients Undergoing Video-Assisted Thoracic Surgery: A Randomized Controlled Trial.

Purpose: Postoperative nausea and vomiting (PONV) frequently occur in patients after surgery. In this study, the authors investigated whether perioperative S-ketamine infusion could decrease the incidence of PONV in patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy. Patients and Methods: This prospective, randomized, double-blinded, controlled study was conducted a total of 420 patients from September 2021 to May 2023 at Xuzhou Central Hospital in China, who underwent elective VATS lobectomy under general anesthesia with tracheal intubation. The patients were randomly assigned to either the S-ketamine group or the control group. The S-ketamine group received a bolus injection of 0.5 mg/kg S-ketamine and an intraoperative continuous infusion of S-ketamine at a rate of 0.25 mg/kg/h. The control group received an equivalent volume of saline. All patients were equipped with patient-controlled intravenous analgesia (PCIA), with a continuous infusion rate of 0.03 mg/kg/h S-ketamine in the S-ketamine group or 0.03 µg/kg/h sufentanil in the control group. The primary outcome was the incidence of PONV. Secondary outcomes included perioperative opioid consumption, hemodynamics, postoperative pain, and adverse events. Results: The incidence of PONV in the S-ketamine group (9.7%) was significantly lower than in the control group (30.5%). Analysis of perioperative opioid usage revealed that remifentanil usage was 40.0% lower in the S-ketamine group compared to the control group (1414.8 µg vs 2358.2 µg), while sufentanil consumption was 75.2% lower (33.1 µg vs 133.6 µg). The S-ketamine group demonstrated better maintenance of hemodynamic stability. Additionally, the visual analogue scale (VAS) scores on postoperative day 1 (POD-1) and postoperative day 3 (POD-3) were significantly lower in the S-ketamine group. Finally, no statistically significant difference in other postoperative adverse reactions was observed between the two groups. Conclusion: The results of this trial indicate that perioperative S-ketamine infusion can effectively reduce the incidence of PONV in patients undergoing VATS lobectomy.

Modulation of lipid profile by secretory phospholipase A2 group IIA: Verification with a transgenic mouse model.

We previously demonstrated a positive relation of secretory phospholipase A2 group IIA (sPLA2-IIA) with circulating high-density lipoprotein cholesterol (HDL-C) in patients with coronary artery disease, and sPLA2-IIA increased cholesterol efflux in THP-1 cells through peroxisome proliferator-activated receptor-γ (PPAR-γ)/liver X receptor α/ATP-binding cassette transporter A1 (ABCA1) signaling pathway. The aim of the present study was to examine the role of sPLA2-IIA over-expression on lipid profile in a transgenic mouse model. Fifteen apoE-/- and C57BL/7 female mice received bone marrow transplantation from transgenic SPLA2-IIA mice, and treated with specific PPAR-γ inhibitor GW9662. High fat diet was given after one week of bone marrow transplantation, and animals were sacrificed after twelve weeks. Immunohistochemical staining showed over-expression of sPLA2-IIA protein in the lung and spleen. The circulating level of HDL-C, but not that of low-density lipoprotein cholesterol (LDL-C), total cholesterol, or total triglyceride, was increased by sPLA2-IIA over-expression, and was subsequently reversed by GW9662 treatment. Over-expression of sPLA2-IIA resulted in augmented expression of cholesterol transporter ABCA1 at mRNA level in the aortas, and at protein level in macrophages, co-localized with macrophage specific antigen CD68. GW9662 exerted potent inhibitory effects on sPLA2-IIA-induced ABCA1 expression. Conclusively, we demonstrated the effects of sPLA2-IIA on circulating HDL-C level and the expression of ABCA1, possibly through regulation of PPAR-γ signaling in transgenic mouse model, that is in concert with the conditions in patients with coronary artery disease.

Enhanced Photo-excitation and Angular-Momentum Imprint of Gray Excitons in WSe2 Monolayers by Spin-Orbit-Coupled Vector Vortex Beams.

A light beam can be spatially structured in the complex amplitude to possess orbital angular momentum (OAM), which introduces an extra degree of freedom alongside the intrinsic spin angular momentum (SAM) associated with circular polarization. Furthermore, superimposing two such twisted light (TL) beams with distinct SAM and OAM produces a vector vortex beam (VVB) in nonseparable states where not only complex amplitude but also polarization is spatially structured and entangled with each other. In addition to the nonseparability, the SAM and OAM in a VVB are intrinsically coupled by the optical spin-orbit interaction and constitute the profound spin-orbit physics in photonics. In this work, we present a comprehensive theoretical investigation, implemented on the first-principles base, of the intriguing light-matter interaction between VVBs and WSe2 monolayers (WSe2-MLs), one of the best-known and promising two-dimensional (2D) materials in optoelectronics dictated by excitons, encompassing bright exciton (BX) as well as various dark excitons (DXs). One of the key findings of our study is that a substantial enhancement of the photoexcitation of gray excitons (GXs), a type of spin-forbidden DX, in a WSe2-ML can be achieved through the utilization of a 3D-structured TL with the optical spin-orbit interaction. Moreover, we show that a spin-orbit-coupled VVB surprisingly allows for the imprinting of the carried optical information onto GXs in 2D materials, which is robust against the decoherence mechanisms in the materials. This suggests a promising method for deciphering the transferred angular momentum from structured light to excitons.

Unraveling the Ligand-Binding Sites of CYP3A4 by Molecular Dynamics Simulations with Solvent Probes.

Cytochrome P450 3A4 (CYP3A4) is one of the most important drug-metabolizing enzymes in the human body and is well known for its complicated, atypical kinetic characteristics. The existence of multiple ligand-binding sites in CYP3A4 has been widely recognized as being capable of interfering with the active pocket through allosteric effects. The identification of ligand-binding sites other than the canonical active site above the heme is especially important for understanding the atypical kinetic characteristics of CYP3A4 and the intriguing association between the ligand and the receptor. In this study, we first employed mixed-solvent molecular dynamics (MixMD) simulations coupled with the online computational predictive tools to explore potential ligand-binding sites in CYP3A4. The MixMD approach demonstrates better performance in dealing with the receptor flexibility compared with other computational tools. From the sites identified by MixMD, we then picked out multiple sites for further exploration using ensemble docking and conventional molecular dynamics (cMD) simulations. Our results indicate that three extra sites are suitable for ligand binding in CYP3A4, including one experimentally confirmed site and two novel sites.

Predicting slight freezing of gait in Parkinson's disease with anticipatory postural adjustments and limits of stability.

INTRODUCTION: Gait initiation (GI) includes automatic and voluntary movements. However, research on their impact on the first step in patients with Parkinson's disease (PD) and their relationship to freezing of gait (FOG) is lacking. We examined the effects of automatic movements (anticipatory postural adjustments [APAs]) and voluntary movements (limits of stability [LOS]) on the first step (first-step duration and first-step range of motion), along with their early recognition and prediction of slight FOG. METHODS: Twenty-three patients with PD and slight freezing (PD + FOG) and 25 non-freezing patients with PD (PD-FOG) were tested while off medications and compared with 24 healthy controls (HC). All participants completed a 7-m Stand and Walk Test (7 m SAW) and wore inertial sensors to quantify the APAs and first step. LOS was quantified by dynamic posturography in different directions using a pressure platform. We compared differences among all three groups, analysed correlations, and evaluated their predictive value for slight FOG. RESULTS: In PD + FOG, APAs and LOS were worse than those in the PD-FOG and HC groups (p < 0.001), and the first step was worse than that in HC (p < 0.001). APAs were correlated mainly with the first-step duration. APAs and LOS were correlated with the first-step range of motion. APAs have been recognized as independent predictors of FOG, and their combination with LOS enhances predictive sensitivity. CONCLUSION: APAs and LOS in patients with PD directly affect the first step during GI. In addition, the combination of APAs and LOS helped predict slight FOG.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

FluPMT: Prediction of Predominant Strains of Influenza A Viruses Via Multi-task Learning.

Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vaccine strain is generally based on the assessment of the current predominant strains. Nevertheless, the process of vaccine production and distribution is very time-consuming, leaving a window for the emergence of new variants that could decrease vaccine effectiveness, so predictions of influenza A virus evolution can inform vaccine evaluation and selection. Hence, we present FluPMT, a novel sequence prediction model that applies an encoder-decoder architecture to predict the hemagglutinin (HA) protein sequence of the upcoming season's predominant strain by capturing the patterns of evolution of influenza A viruses. Specifically, we employ time series to model the evolution of influenza A viruses, and utilize attention mechanisms to explore dependencies among residues of sequences. Additionally, antigenic distance prediction based on graph network representation learning is incorporated into the sequence prediction as an auxiliary task through a multi-task learning framework. Experimental results on two influenza datasets highlight the exceptional predictive performance of FluPMT, offering valuable insights into virus evolutionary dynamics, as well as vaccine evaluation and production.

Effects of Shixiao Huoxue Decoction on pain, tumor necrosis factor-α, and interleukin-8 in patients with adenomyosis.

OBJECTIVE: To explore the effect of Shixiao Huoxue Decoction on pain and tumor necrosis factor (TNF)-α and interleukin (IL)-8 levels in patients with adenomyosis. METHODS: A total of 65 patients with adenomyosis admitted to South District of Guang'anmen Hospital from January 2020 to December 2021 were divided into two groups according to the treatment methods. The control group was treated with pregnatrienone, and the study group was treated with additional Shixiaohuoxue decoction. The incidence of complications, treatment efficacy, levels of inflammatory factors, Traditional Chinese Medicine symptom score, dysmenorrhea score, menstrual volume score, dysmenorrhea symptom score, changes in uterine volume, level of insulin-like growth factor 1 (IGF-1), and changes in the level of carbohydrate antigen (CA125) were observed before and after treatment in both two groups. Univariate Logistic analysis showed that uterine volume, IGF-1, CA125, serum IL-8 and TNF-α were correlated with the short-term efficacy of Meixiaohuoxue Decoction in the treatment of uterine adenomyosis (P<0.05). RESULTS: The levels of IL-8 and TNF-α in the study group were significantly lower than those in the control group after treatment (P<0.05). The scores of dyspareunia and non-menstrual pelvic pain in the study group were significantly lower than those in the control group (P<0.05). The overall response rate in the study group (93.75%) was significantly higher than that in the control group (66.66%) (P<0.05). The scores of Traditional Chinese Medicine symptoms, dysmenorrhea, menstrual volume, and dysmenorrhea symptoms in the study group were significantly lower than those in the control group after treatment (P<0.05). The IGF-1 and CA125 levels in the study group were significantly lower than those in the control group after treatment (P<0.05). However, no significant difference in uterine volume was found between the two groups after treatment (P>0.05). CONCLUSION: Xiaoxiao Huoxue Decoction demonstrated a better treatment efficacy in patients with adenomyosis through improving dysmenorrhea and Traditional Chinese Medicine symptoms, as well as reducing the levels of body inflammatory factors, non-menstrual pelvic pain, and dyspareunia, thus contributing to early recovery of patients. Therefore, Xiaoxiao Huoxue Decoction is worthy of promotion in clinical treatment of adenomyosis.

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.

Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC)  of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition.

Manipulating the processing window of directed self-assembly in contact hole shrinking with binary block copolymer/homopolymer blending.

Directed self-assembly (DSA) lithography has demonstrated significant potential in fabricating integrated circuits. However, DSA encounters limited processing windows due to the requirement for precise matching between the period of block copolymers (BCPs) and graphoepitaxy templates. We propose a binary BCP/homopolymer blending strategy to manipulate the self-assembly behavior and the processing window of graphoepitaxy DSA in contact hole shrinking. By carefully tailoring the blending rates of poly(methyl methacrylate) (PMMA) with different molecular weights in cylindrical polystyrene-b-poly(methyl methacrylate) (PS-b-PMMA), we manipulate the period and morphology of BCP/homopolymer self-assembly. Specifically, we employ BCP/homopolymer blending to fine-tune the critical dimension (CD) of contact holes with PS-affined topographical templates. Subsequent pattern transferring is achieved by selectively etching defect-free shrinkable cylinders as hard masks. Furthermore, self-consistent field theory (SCFT) simulation was employed to explore the self-assembly of BCP/homopolymer blending in confined cylindrical space and the results were in good consistency with the experimental results.

Ba3SnGa10-xInxO20 (0 ≤ x ≤ 2): site-selective doping, band structure engineering and photocatalytic overall water splitting.

Developing new photocatalysts and deciphering the structure-property relationship are always the central topics in photocatalysis. In this study, a new photocatalyst Ba3SnGa10O20 containing two d10 metal cations was prepared by a high temperature solid state reaction, and its crystal structure was investigated by Rietveld refinements of monochromatic X-ray powder diffraction data for the first time. There are 2 Ba, 4 metal cations and 6 O independent atoms in a unit cell. Sn4+ and Ga3+ co-occupy the octahedral cavities named M1 and M2 sites, and the other two metal sites are fully occupied by Ga3+. Rational In3+-to-Ga3+ substitution was performed to reduce the potential of the conduction band minimum and enhance the light absorption ability, which was indeed confirmed using UV-vis diffuse reflectance spectra and Mott-Schottky plots for Ba3SnGa10-xInxO20 (0 ≤ x ≤ 2). Interestingly, In3+ exhibits site selective doping at M1 and M2 sites exclusively. With the light absorption ability enhanced, the photocatalytic overall water splitting activity was also improved, i.e. the photocatalytic H2 generation rate was 1.7(1) µmol h-1 for Ba3SnGa10O20, and the optimal catalyst Ba3SnGa8.5In1.5O20 loaded with 1.0 wt% Pd exhibited the H2 generation rate of 27.5(4) µmol h-1 and the apparent quantum yield at 254 nm was estimated to be 2.28% in pure water.

Modeling and analysis of the effect of optimal virus control on the spread of HFMD.

A within-host and between-host hand, foot and mouth disease (HFMD) mathematical model is established and the affect of optimal control in its within-host part on HFMD transmission is studied. Through define two basic reproduction numbers, by using the fast-slow system analysis method of time scale, the global stabilities of the between-host (slow) system and within-host (fast) system are researched, respectively. An optimal control problem with drug-treatment control on coupled within-host and between-host HFMD model is formulated and analysed theoretically. Finally, the purposed optimal control measures are applied to the actual HFMD epidemic analysis in Zhejiang Province, China from April 1, 2021 to June 30, 2021. The numerical results show that the drug control strategies can reduce the virus load per capita and can effectively prevent large-scale outbreaks of HFMD.

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.

MOTIVATION: Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another. RESULTS: Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification. AVAILABILITY AND IMPLEMENTATION: The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.