Atomic layer deposition of Al-doped ZnO nanomembrane with in situ monitoring.

Due to shortcomings such as poor homogeneity of Al doping, precisely controlling the thickness, inability to conformally deposit on high aspect ratio devices and high pinhole rate, the applications of Al-doped ZnO (AZO) nanomembrane in integrated optoelectronic devices are remarkably influenced. Here, we report in situ monitoring during the atomic layer deposition (ALD) of AZO nanomembrane by using an integrated spectroscopic ellipsometer. AZO nanomembranes with different compositions were deposited with real-time and precise atomic level monitoring of the deposition process. We specifically investigate the half reaction and thickness evolution during the ALD processes and the influence of the chamber temperature is also disclosed. Structural characterizations demonstrate that the obtained AZO nanomembranes without any post-treatment are uniform, dense and pinhole-free. The transmittances of the nanomembranes in visible range are > 94%, and the optimal conductivity can reach up to 1210 S/cm. The output of current research may pave the way for AZO nanomembrane becoming promising in integrated optoelectronic devices.

Unraveling dynamic immunological landscapes in intracerebral hemorrhage: insights from single-cell and spatial transcriptomic profiling.

Intracerebral hemorrhage (ICH) poses a formidable challenge in stroke management, with limited therapeutic options, particularly in the realm of immune-targeted interventions. Clinical trials targeting immune responses post-ICH have encountered setbacks, potentially attributable to the substantial cellular heterogeneity and intricate intercellular networks within the brain. Here, we present a pioneering investigation utilizing single-cell RNA sequencing and spatial transcriptome profiling at hyperacute (1 h), acute (24 h), and subacute (7 days) intervals post-ICH, aimed at unraveling the dynamic immunological landscape and spatial distributions within the cerebral tissue. Our comprehensive analysis revealed distinct cell differentiation patterns among myeloid and lymphocyte populations, along with delineated spatial distributions across various brain regions. Notably, we identified a subset of lymphocytes characterized by the expression of Spp1 and Lyz2, termed macrophage-associated lymphocytes, which exhibited close interactions with myeloid cells. Specifically, we observed prominent interactions between Lgmn+Macro-T cells and microglia through the spp1-cd44 pathway during the acute phase post-ICH in the choroid plexus. These findings represent a significant advancement in our understanding of immune cell dynamics at single-cell resolution across distinct post-ICH time points, thereby laying the groundwork for exploring critical temporal windows and informing the development of targeted therapeutic strategies.

Identification and Characterization of Hibiscus mutabilis Varieties Resistant to Bemisia tabaci and Their Resistance Mechanisms.

Hibiscus mutabilis, the city flower of Chengdu, is culturally significant and has nutritional and medicinal benefits. However, frequent infestations of Bemisia tabaci have caused economic losses. This study aimed to identify insect-resistant H. mutabilis varieties. Over two years, varieties like Jinqiusong, Zuiyun, and Zuifurong showed moderate to high resistance based on reproductive indices. Assessments of antixenosis and developmental impacts revealed that adult B. tabaci exhibited low selectivity toward these resistant varieties, indicating a strong repellent effect. Gas chromatography-mass spectrometry analysis identified volatile organic compounds, such as alcohols, alkanes, and terpenes. Notably, 2-ethylhexanol and 6-methylheptanol exhibited repellent properties. Using nontargeted metabolomics, this study compared the metabolite profiles of the insect-resistant variety Jinqiusong (JQS), moderately resistant Bairihuacai (BRHC), and highly susceptible Chongbanbai (CBB) post B. tabaci infestation. Fifteen key metabolites were linked to resistance, emphasizing the phenylpropanoid biosynthesis pathway as crucial in defense. These findings offer a theoretical foundation for breeding insect-resistant H. mutabilis varieties and developing eco-friendly strategies against B. tabaci infestations.

Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma.

Glaucoma, a leading cause of irreversible blindness, poses a significant global health burden. Early detection is crucial for effective management and prevention of vision loss. This study presents a collection of novel structural biomarkers in glaucoma diagnosis. By employing advanced imaging techniques and data analysis algorithms, we now can recognize indicators of glaucomatous progression. Many research studies have revealed a correlation between the structural changes in the eye or brain, particularly in the optic nerve head and retinal nerve fiber layer, and the progression of glaucoma. These biomarkers demonstrate value in distinguishing glaucomatous eyes from healthy ones, even in the early stages of the disease. By facilitating timely detection and monitoring, they hold the potential to mitigate vision impairment and improve patient outcomes. This study marks an advancement in the field of glaucoma, offering a promising avenue for enhancing the diagnosis and possible management.

A Comparison between 2-Octyl Cyanoacrylate and Conventional Suturing for the Closure of Epiblepharon Incision Wounds in Children: A Retrospective Case-Control Study.

Background: Tissue adhesive has been widely used in ophthalmic surgery for various procedures, proving both effective and safe. However, no studies have compared the surgical efficacy of the tissue adhesive 2-octyl cyanoacrylate (SurgiSeal®) to that of traditional suture closure in Asian children undergoing surgery for lower lid epiblepharon. Methods: This is a single-center retrospective case-control study. Surgical correction for epiblepharon was performed on 22 patients from November 2019 to May 2023. A total of 20 patients who were followed up for at least 1 month were included for analysis. After standardized epiblepharon surgery, group A underwent wound closure with a subcuticular suture and 2-octyl cyanoacrylate, and group B underwent closure with a 6-O fast-absorbing surgical gut suture. Patients were followed up at 1, 4, and 12 weeks post-surgery. Results: A total of 10 patients (20 eyes) underwent skin closure with tissue adhesives (group A), and 10 patients (18 eyes) underwent wound closure using conventional suture material (group B). No significant differences in the sex ratio, mean age at operation, pre- and postoperative best-corrected visual acuity (BCVA), or average surgical time were observed between groups. Both groups exhibited improved postoperative BCVA, with symptom relief and a significant decrease in the severity of keratopathy after surgery. Neither recurrence nor complications were reported during follow-up. The aesthetic results were similar between groups, while caregivers of children in the tissue adhesive group expressed high satisfaction regarding the ease of postoperative care. Conclusions: Successful closure of lower lid epiblepharon surgery wounds in children can be performed using 2-octyl cyanoacrylate (SurgiSeal®). This method is simple, safe, and effective when compared to conventional sutures.

Highly Accurate and Explainable Predictions of Small-Molecule Antioxidants for Eight In Vitro Assays Simultaneously through an Alternating Multitask Learning Strategy.

Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.

Ion Exchange-mediated 3D Cross-linked ZIF-L Superstructure for Flexible Electrochemical Energy Storage.

Metal-organic frameworks (MOFs) are considered as a promising candidate for advancing energy storage owing to their intrinsic multi-channel architecture, high theoretical capacity, and precise adjustability. However, the low conductivity and poor structural stability lead to unsatisfactory rate and cycling performance, greatly hindering their practical application. Herein, we propose a sea urchin-like Co-ZIF-L superstructure using molecular template to induce self-assembly followed by ion exchange method, which shows improved conductivity, successive channels, and high stability. The ion exchange can gradually etch the superstructure, leading to the reconstruction of Co-ZIF-L with three-dimensional (3D) cross-linked ultrathin porous nanosheets. Moreover, the precise control of Co to Ni ratios can construct effective micro-electric field and synergistically enhance the rapid transfer of electrons and electrolyte ions, improving the conductivity and stability of CoNi-ZIF-L. The Co6.53Ni-ZIF-L electrode exhibits a high specific capacity (602 F g-1 at 1 A g-1) and long cycling stability (95.3% retention after 4,000 cycles at 5 A g-1). The Co6.53Ni-ZIF-L//AC asymmetric flexible supercapacitor employing gel electrolyte also exhibits excellent cycling stability (93.3% retention after 4000 cycles at 5 A g-1). This discovery provides valuable insights for electrode material selection and energy storage efficiency improvement.

The Application of Rho Kinase Inhibitors in the Management of Glaucoma.

Glaucoma is a chronic neurodegenerative disease that poses a significant threat of irreversible blindness worldwide. Current treatments for glaucoma focus on reducing intraocular pressure (IOP), which is the only modifiable risk factor. Traditional anti-glaucomatous agents, including carbonic anhydrase inhibitors, beta-blockers, alpha-2 agonists, and prostaglandin analogs, work by either improving uveoscleral outflow or reducing aqueous humor production. Rho kinase (ROCK) inhibitors represent a novel class of anti-glaucomatous drugs that have emerged from bench to bedside in the past decade, offering multifunctional characteristics. Unlike conventional medications, ROCK inhibitors directly target the trabecular meshwork outflow pathway. This review aims to discuss the mechanism of ROCK inhibitors in reducing IOP, providing neuroprotection, and preventing fibrosis. We also highlight recent studies and clinical trials evaluating the efficacy and safety of ROCK inhibitors, compare them with other clinical anti-glaucomatous medications, and outline future prospects for ROCK inhibitors in glaucoma treatment.

Thiosalicylic-Acid-Mediated Coordination Structure of Nickel Center via Thermodynamic Modulation for Aqueous Ni-Zn Batteries.

Uniquely functional nanocomplexes with rich coordination environments are critical in energy storage. However, the construction of structurally versatile nanocomplexes remains challenging. In this study, a nickel-based complex with structural variations is designed via thermodynamic modulation using a dual-ligand synthesis strategy. A nickel-based nanomaterial (NiSA-SSA-160) with a large specific surface area is synthesized around the competing coordination of the host and guest molecules that differ in terms of the chemical properties of the O and S elements. Concurrently, the coordination environment of NiSA-SSA-160 is investigated via X-ray absorption fine structure spectroscopy. The thiol functional groups synergistically induced an electron-rich Ni structure, thus increasing the electron density of the central atom. The electrochemical performance of an assembled NiSA-SSA-160//Zn@CC battery is shown to improve significantly, with a maximum energy density of 0.54 mWh cm-2 and a peak power density of 49.49 mW cm-2. This study provides a new perspective regarding coordination transformations and offers an idea for the design of functionally rich nanomaterials.

Using Smartphone Chatbot for Postoperative Care after Intravitreal Injection during COVID-19 Period: A Retrospective Cohort Study.

BACKGROUND: During COVID-19 pandemic period, it was difficult for the patients regular and scheduled follow-up in outpatient department, especially when lock-down. However, early detection of patients with initial infection or other serious conditions after ocular surgeries, such as intravitreous injection (IVI) for age-related macular degeneration (AMD). OBJECTIVE: We accessed a postoperative care chatbot system (PCCS) in smartphone for patients to self-report postoperative symptoms/signs with an instant bidirectional feedback system. METHODS: During the COVID-19 level 3 epidemic alert in July 2021 in Taiwan, the PCCS alerted the patient to report and grade six ocular symptoms/signs associated with ocular inflammation or retinal detachment. Patients used the PCCS for 7 days postoperatively to assess their symptoms/signs per day after receiving an alert. The data automatically collected using a cloud computer system judged the grade and sent messages to medical staff for further medical assistance. User's satisfaction questionnaire was collected on the 7th day. RESULTS: One hundred and eighty-five patients participated in this study. There were 26 reports (3.03%) of symptom grade deterioration (increased blurred vision, eye swelling, nausea, and floater/flash) in 12 patients (6.5%). No gender difference for the earlier medical consultation. One case occurred endophthalmitis and improved after 2 times prompt IVI antibiotics. 87% of patients were satisfied or very satisfied to communicate their symptoms instantly with the app, willing to use it again and considered it could improve quality of care. The incidence of earlier medical consultation is 3.8% (7/185) and the incidence of endophthalmitis is 0.5% (1/185). CONCLUSIONS: The chatbot system, designed for self-reporting postoperative symptoms and providing instant bidirectional feedback on smartphones, could be beneficial for enhancing early medical consultation without gender differences in AMD patients who receiving intravitreal injections. It achieves satisfactory response from patients.

Femtosecond laser-assisted astigmatic keratotomy versus toric IOL implantation for correcting astigmatism in cataract patients: a systematic review and meta-analysis with trial sequential analysis.

AIMS: To compare the refractive and visual outcomes of femtosecond laser-assisted astigmatic keratotomy (FSAK) and toric intraocular lens (IOL) implantation for correcting astigmatism in cataract patients. METHODS: Studies were retrieved from the Ovid-Medline, EMBASE, Cochrane Central Register of Controlled Trials and Scopus which compared FSAK and toric IOL for astigmatism correction in cataract patients. Outcome measures included postoperative refractive cylinder, correction index, uncorrected distance visual acuity (UDVA), the proportion of patients achieving a residual refractive cylinder of 1.00 dioptre or less, target-induced astigmatism (TIA) and surgically induced astigmatism (SIA). The trial sequential analysis (TSA) was used to collect firm evidence supporting our conclusion. RESULTS: 9 studies encompassing 590 participants were analysed. The meta-analysis revealed that toric IOLs could result in less postoperative refractive cylinder and provide better UDVA compared with FSAK. The TSA disclosed strong evidence of lower postoperative refractive cylinder in the toric IOL group compared with that of the FSAK group. FSAK showed a smaller correction index and lower mean TIA and SIA compared with toric IOLs. CONCLUSIONS: For cataract patients, both FSAK and toric IOLs are effective methods for correcting astigmatism. However, toric IOLs offer less postoperative astigmatism and result in better postoperative UDVA compared with FSAK. In vector analysis of astigmatism, toric IOLs can also produce higher TIA and SIA. Additionally, neither method is associated with severe untreatable complications. Therefore, the conclusion is that toric IOLs are the preferred choice for astigmatism correction in cataract patients and FSAK serves as a viable alternative when toric IOLs are contraindicated.

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

Unified and vector theory of Raman scattering in gas-filled hollow-core fiber across temporal regimes.

Raman scattering has found renewed interest owing to the development of gas-filled hollow-core fibers, which constitute a unique platform for exploration of novel ultrafast nonlinear phenomena beyond conventional solid-core-fiber and free-space systems. Much progress has been made through models for particular interaction regimes, which are delineated by the relation of the excitation pulse duration to the time scales of the Raman response. However, current experimental settings are not limited to one regime, prompting the need for tools spanning multiple regimes. Here, we present a theoretical framework that accomplishes this goal. The theory allows us to review recent progress with a fresh perspective, makes new connections between distinct temporal regimes of Raman scattering, and reveals new degrees of freedom for controlling Raman physics. Specific topics that are addressed include transient Raman gain, the interplay of electronic and Raman nonlinearities in short-pulse propagation, and interactions of short pulses mediated by phonon waves. The theoretical model also accommodates vector effects, which have been largely neglected in prior works on Raman scattering in gases. The polarization dependence of transient Raman gain and vector effects on pulse interactions via phonon waves is investigated with the model. Throughout this Perspective, theoretical results are compared to the results of realistic numerical simulations. The numerical code that implements the new theory is freely available. We hope that the unified theoretical framework and numerical tool described here will accelerate the exploration of new Raman-scattering phenomena and enable new applications.

Investigating the Association of Polygenic Risk Scores With Thyroid Cancer Susceptibility in a Han Chinese Population.

Background: Thyroid cancer, the leading endocrine tumor with a rising global incidence, especially in women, is influenced by both genetic and environmental factors. This study examines the relationship between polygenic risk scores (PRS) and thyroid cancer susceptibility in the Han Chinese population, as well as the impact of genetic variants on clinical outcomes. Methods: Analyzing data from 57 257 participants in the Taiwan Precision Medicine Initiative, the study employed the Affymetrix Genome-Wide TWB 2.0 SNP Array for genotyping. PRS were calculated using single nucleotide polymorphisms (SNPs) from prior genome-wide association studies, specifically PGS000087 and PGS000797, and correlated with clinical parameters like age, sex, comorbidities, and treatment methods. Results: Among 4063 participants with thyroid tumors (839 malignant, 3224 benign), higher PRS quartiles correlated significantly with increased thyroid cancer incidence. The highest quartile showed a 1.15-fold (PGS000797) and 1.14-fold (PGS000087) greater risk than the lowest quartile. Key findings included an association between higher PRS quartiles and younger onset age, along with a notable link to chronic kidney disease and thyroid hormone levels in specific SNPs. Conclusion: The study demonstrates PRS's utility in predicting thyroid cancer risk in the Han Chinese population, with higher PRS associated with increased risk and distinct clinical features. While this study focuses on the Han Chinese population, we recognize the importance of comparing PRS performance across different ancestries to fully understand ethnic genetic diversity in cancer risk assessment. Future studies should aim to include such comparative analysis.

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration.

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

A method for rapid nanobody screening with no bias of the library diversity.

Nanobody, referred to the variable domain of heavy-chain-only antibodies, has several advantages such as small size and feasible Escherichia coli expression, making them promising for scientific research and therapies. Conventional nanobody screening and expression methods often suffer from the need for subcloning into expression vectors and amplification-induced diversity loss. Here, we developed an integrated method for simultaneous screening and expression. Nanobody libraries were cloned and secretly expressed in the culture medium. Target-specific nanobodies were isolated through 1-3 rounds of dilution and regrowth following the Poisson distribution. This ensured no dismissal of positive clones, with populations of positive clones increasing over 10-fold in each dilution round. Ultimately, we isolated 5 nanobodies against death domain receptor 5 and 5 against Pyrococcus furiosus DNA polymerase directly from their immunized libraries. Notably, our approach enables nanobody screening without specialized instruments, demonstrating broad applicability in routine monoclonal nanobody production for diverse biomedical applications.

Temperature-Controllable Liquid Crystalline Medium for Stereochemical Elucidation of Organic Compounds via Residual Chemical Shift Anisotropies.

The configuration elucidation of organic molecules continues to pose significant challenges in studies involving stereochemistry. Nuclear magnetic resonance (NMR) techniques are powerful for obtaining such structural information. Anisotropic NMR techniques, such as measurement of residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), complementing isotropic NMR parameters, provide relative configuration information. RCSAs provide valuable structural information, especially for nonprotonated carbons, yet have been severely underutilized due to the lack of an easily operational alignment medium capable of rapid transition from anisotropic to isotropic environments, especially in aqueous conditions. In this study, an oligopeptide-based alignment media (FK)4 is presented for RCSA measurements. Temperature variation manipulates the assembly of (FK)4, yielding tunable anisotropic and isotropic phases without the requirement of any special devices or time-consuming correction procedures during data analysis. Decent observed ΔΔRCSA values from sp3 carbons benefit the utilization of RCSA measurements in the structural elucidation of organic molecules highly composed with sp3 carbons. Moreover, the (FK)4 alignment medium is applicable for both RDC and RCSA measurements in one sample, further advancing the configuration analysis of molecules of interest.

A meta-analysis of sutureless scleral-fixated intraocular lens versus retropupillary iris claw intraocular lens for the management of aphakia.

This study compared the visual outcomes and complications between sutureless scleral-fixated intraocular lens and iris claw intraocular lens implantation in aphakia without adequate capsule and/or zonule support. Studies comparing the clinical outcomes of scleral-fixated intraocular lens and iris claw intraocular lens implantation published until April 2022 were retrieved from the PubMed, EMBASE, Cochrane Library, and Google Scholar databases. The outcomes included postoperative final visual acuity, surgical time, surgery-induced astigmatism, and complications. The weighted mean difference and odds ratio were calculated. Two randomized controlled trials and five cohort studies, including 244 and 290 eyes in the scleral-fixated intraocular lens group and iris claw group, respectively, were included. Scleral-fixated intraocular lens implantation results in a better postoperative final corrected distance visual acuity compared with iris claw intraocular lens implantation; however, it is more time-consuming. Scleral-fixated intraocular lens implantation seems to have lesser incidences of surgery-induced astigmatism. Furthermore, both procedures have a similar complication rate. Therefore, based on current best evidence, these two procedures should be considered according to patient's conditions.

Efficacy and safety of intravitreal faricimab for neovascular age-related macular degeneration: a systematic review and meta-analysis.

We conducted a systematic review and meta-analysis to evaluate the visual, anatomical, and safety outcomes of the intravitreal faricimab, a novel vascular endothelial growth factor (VEGF)/angiopoietin-2 (Ang-2) bispecific agent, in neovascular age-related macular degeneration (nAMD) patients. The follow-up times in the included studies ranged from a minimum of 36 weeks to a maximum of 52 weeks. EMBASE, Ovid-Medline, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Scopus, the WHO ICTRP, ClinicalTrial.gov, the EU Clinical Trials Register, and Chinese Clinical Trial Registry (ChiCTR) were searched (The last literature search was performed on August 17, 2023) for randomized controlled trials (RCTs) comparing faricimab with control groups for neovascular age-related macular degeneration (nAMD). The risk of bias for eligible RCTs was independently assessed using the Cochrane Risk of Bias Tool by two authors (W.-T.Y. and C.-S.W.). The meta-analysis was conducted using Review Manager 5.4 software. The mean best corrected visual acuity (BCVA), central subfield thickness (CST), total choroidal neovascularization (CNV) area, and total lesion leakage were analyzed as continuous variables and the outcome measurements were reported as the weighted mean difference (WMD) with a 95% confidence interval (CI). The ocular adverse events and ocular serious adverse events were analyzed as dichotomous variables and the outcome measurements were analyzed as the odds ratios (ORs) with a 95% CI. Random-effects model was used in our study for all outcome synthesizing due to different clinical characteristics. Four RCTs with 1,486 patients were eligible for quantitative analysis. There was no statistically significant difference between intravitreal faricimab and anti-VEGF in BCVA [weighted mean difference (WMD) = 0.47; 95% CI: (- 0.17, 1.11)]. The intravitreal faricimab group showed numerically lower CST [WMD = - 5.96; 95% CI = (- 7.11, - 4.82)], total CNV area [WMD = - 0.49; 95% CI = (- 0.68, - 0.30)], and total lesion leakage [WMD = - 0.88; 95% CI = (- 1.08, - 0.69)] after intravitreal therapy compared with the intravitreal anti-VEGF group. There were no statistically significant differences between intravitreal faricimab and anti-VEGF in ocular adverse events (AEs) [pooled odds ratio (OR) = 1.10; 95% CI = (0.81, 1.49)] and serious adverse events (SAEs) [pooled OR = 0.84; 95% CI = (0.37, 1.90)]. The intravitreal bispecific anti-VEGF/angiopoietin 2 (Ang2) antibody faricimab with a extended injection interval was non-inferior to first-line anti-VEGF agents in BCVA. It was safe and had better anatomical recovery. Large, well-designed RCTs are needed to explore the potential benefit of extended faricimab for nAMD. This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42022327450).

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors.

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.