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This study aimed to evaluate whether Mycoplasma pneumoniae resistance gene detection can independently guide antibiotic therapy for Mycoplasma pneumoniae pneumonia in children and to identify key predictors for antibiotic adjustment. We conducted a retrospective cohort study of children with Mycoplasma pneumoniae pneumonia who underwent resistance gene testing. Participants were recruited from two centers (Affiliated Hospital of North Sichuan Medical College and People's Hospital of Nanbu County) between January 2023 and October 2025. Resistance gene testing results served as the basis for assigning patients to either the positive or negative group. The expression of Mycoplasma pneumoniae resistance genes was analyzed. The positive group was further divided into a "switch group" (switched to doxycycline) and a "maintenance group" (continued azithromycin). We performed a comparative analysis of demographic, clinical, and laboratory parameters across the groups. Multivariate logistic regression identified factors associated with switching antibiotics, and ROC curves assessed predictive performance. A total of 150 children were enrolled, with 13 in the macrolide resistance gene-negative group and 137 in the positive group, resulting in a macrolide resistance gene positivity rate of 91.3%. Of these patients, 65 patients were in the switch group and 72 patients were in the maintenance group. Significant differences were observed in baseline characteristics (age), inflammatory markers (CRP, PCT, lymphocyte count), disease severity (severe MPP, pleural effusion, bronchoscopy), and clinical management (peak fever, hospital stay, antibiotic duration) (P&#x2009;<&#x2009;0.05). Logistic regression analysis identified increased age, elevated CRP, and prolonged hospital stay as independent predictors for switching to second-line antibiotics in macrolide-resistant Mycoplasma pneumoniae. In the Nanchong area, macrolide resistance mediated by the 23S rRNA A2063G mutation predominates. Resistance gene detection alone indicates only potential resistance but cannot independently guide antibiotic switching. Among gene-positive children, elevated CRP and older age are early predictors of switching need, while prolonged hospital stay serves as a retrospective confirmatory marker. Clinical decisions should integrate these biomarkers rather than relying solely on genetic results.

Telitacicept, a dual BAFF/APRIL inhibitor, has shown clinical efficacy in systemic lupus erythematosus (SLE), but the underlying molecular mechanisms remain incompletely understood. We performed an integrative longitudinal study in ten patients with SLE before and after telitacicept treatment, alongside ten healthy controls. Paired transcriptome RNA sequencing and dedicated B-cell receptor (BCR) sequencing and repertoire analysis were integrated with serial clinical phenotyping including SLEDAI-2&#xa0;K, anti-dsDNA antibodies, and complement C3/C4 levels over 12 months of follow-up. Differential expression analysis, pathway enrichment, cell deconvolution, and BCR clonal tracking were conducted. Telitacicept treatment was associated with reduced B-cell clonal diversity and decreased estimated plasma cell abundance, alongside partial reconstitution of naive B cells. Downregulation of PI3K-Akt and NF-&#x3ba;B pathway genes was observed. BCR repertoire analysis suggested loss of 58.7% of clones expressing IGHV4-34, IGHV3-30, and IGHV1-46, which correlated positively with baseline disease activity scores. An unexpected upregulation of interferon-stimulated genes (ISGs) was noted post-treatment, a signature not observed in independent cohorts receiving standard-of-care therapies. Exploratory subgrouping based on ISG dynamics suggested potential differences in clinical trajectory: the ISG-upregulated subgroup showed a more pronounced initial reduction in SLEDAI-2&#xa0;K scores at three months, followed by a subsequent increase in disease activity scores during extended follow-up, whereas the non-upregulated subgroup maintained relatively stable scores. These preliminary findings suggest that telitacicept may remodel the B-cell repertoire while concurrently modulating interferon pathways in SLE. The observed ISG upregulation generates hypotheses regarding post-treatment immune dynamics that warrant validation in larger, independent cohorts.

Radioactive Ba2+ poses significant risks to nuclear safety and environmental protection, yet its efficient removal from nuclear wastewater remains a considerable challenge. Herein, Mg-Al layered double hydroxides (LDHs) were synthesized via a co-precipitation method and systematically optimized by tuning the Mg/Al molar ratio and calcination temperature. The optimal material, obtained by calcining Mg-Al LDH with a Mg/Al ratio of 4:1 at 450 &#xb0;C (denoted as HT-450), exhibited a high apparent Ba2+ uptake capacity of 416 mg g-1 and reached equilibrium within 15 min. Structural and spectroscopic analyses indicate that Ba2+ immobilization is more appropriately described as a reconstruction-coupled, interfacially mediated mineralization process, in which insoluble BaCO3 forms in close association with the reconstructed HT-450 surface rather than through simple reversible adsorption or ion exchange. HT-450 also exhibited stable performance over a wide pH range of 3-7, high selectivity toward Ba2+ in the presence of competing mono-, di-, and trivalent cations, and excellent radiation tolerance, retaining approximately 95% of its initial uptake capacity after exposure to 200 kGy high-energy electron irradiation. These results demonstrate that HT-450 is a promising candidate for the rapid and stable immobilization of Ba2+ from Ba-containing radioactive wastewater.

Type 2 Diabetes (T2D) and Colorectal Cancer (CRC) share a complex bidirectional relationship driven by common metabolic and inflammatory pathways. This review comprehensively examines the pivotal role of Long Non-Coding RNAs (lncRNAs) as molecular bridges between T2D and CRC, regulating gene expression at chromatin, transcriptional, and post-transcriptional levels. We focus on specific lncRNAs including H19, ANRIL, KCNQ1OT1, UCA1, GAS5, MIR31HG, HNF1A-AS1, and MALAT1, which modulate shared oncogenic and metabolic signaling cascades such as PI3K/AKT, Wnt/&#x3b2;-catenin, NF-&#x3ba;B, and HIF-1&#x3b1;. Furthermore, we expand the scope beyond isolated lncRNA regulation to emphasize the lncRNA-miRNA crosstalk and the systemic involvement of the cardiovascular system. Recent evidence highlights that miR-217, miR-122, and the NBAT1/miR-21 axis are critical regulators not only in CRC progression but also in myocardial injury associated with T2D. Consequently, we propose that a holistic biomarker strategy must integrate panels of both lncRNAs and miRNAs to capture the full spectrum of metabolic, oncogenic, and cardiac risks. This updated perspective underscores the translational potential of targeting multi-ncRNA networks for early diagnosis, prognosis, and therapeutic intervention in patients with multimorbidity.

Children conceived through infertility treatments may have altered birth outcomes and growth patterns compared to those conceived naturally. However, evidence on long-term growth trajectories, particularly distinguishing singletons and multiples, remains inconsistent. This study aimed to compare childhood growth trajectories from birth to 12 years of age between children conceived via infertility treatments and those conceived naturally, using a large national birth cohort. We analyzed the follow-up data (6 months to 12 years of age) from the Taiwan Birth Cohort Study, an ongoing, nationwide, longitudinal study of children. Our final sample included 20,507 singletons (20,306 naturally conceived [NC], 104 via intrauterine insemination [IUI], 97 via in vitro fertilization [IVF]) and 950 multiples (670 NC, 68 IUI, 212 IVF). We used linear mixed models with random coefficient cubic splines for age to assess the growth trajectories for weight, height, and body mass index (BMI) by infertility treatment status and logistic regression to analyze risk of rapid weight gain, overweight, obesity, and underweight. Children conceived via IUI or IVF showed lower weight, height, and BMI in pooled analyses; however, these differences were largely attributable to the higher proportion of multiple births in the infertility treatment groups. After stratification by plurality, most differences were attenuated and not statistically significant, with the exception of slightly lower weight (-0.353&#x2009;kg; p&#x2009;=&#x2009;0.039) among IUI-conceived multiples. The risk of rapid weight gain (6 months-2 years) and underweight, overweight, or obesity (5.5-12 years) did not differ significantly by the conception mode after plurality was controlled. After accounting for plurality, children conceived through infertility treatments showed similar growth trajectories to those conceived naturally. These findings are reassuring for families using infertility treatments and highlight the importance of minimizing multiple births in assisted reproduction to support optimal child growth and health outcomes.

Visceral adiposity has been implicated in metabolic dysregulation and chronic inflammation, both of which may contribute to kidney stone recurrence. However, accurate and reproducible quantification of visceral fat in routine clinical practice remains challenging. This study aimed to investigate the association between visceral fat area (VFA) quantified from computed tomography (CT) images and the risk of kidney stone recurrence. We retrospectively analyzed patients with urolithiasis who underwent abdominal CT imaging. Visceral fat area was automatically quantified using a previously validated artificial intelligence (AI)-based CT segmentation system. Clinical characteristics and stone recurrence outcomes were collected. Multivariable regression models were applied to assess the association between VFA and stone recurrence after adjustment for relevant confounders. A total of 131 patients were included, of whom 73 (48%) experienced stone recurrence during a mean follow-up of 47 weeks. Patients with recurrence had significantly higher visceral fat area. High VFA was independently associated with increased recurrence risk (adjusted hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.03 to 2.82). Subgroup analyses demonstrated a stronger association in younger patients (HR 2.45, 95% CI 1.23 to 4.89), while no significant association was observed in older patients or across sexes. CT-derived visceral fat area was independently associated with kidney stone recurrence in this retrospective cohort. These findings suggest that visceral adiposity may serve as a useful imaging biomarker for risk stratification. Further prospective studies are warranted to validate its clinical utility.

Current guidelines recommend endoscopic variceal ligation (EVL) and endoscopic cyanoacrylate injection (ECI) for esophageal and fundal variceal bleeding, respectively. However, the optimal strategy for treating acute variceal bleeding (AVB) from the cardia to the lesser curvature remains undefined. This study compared the efficacy of EVL and ECI for AVB at this region. We retrospectively analyzed patients with AVB from the cardia to the lesser curvature treated with EVL or ECI. Varices were classified as type 1 gastroesophageal varices-cardiac varices (GOV1-CVs; 0-2&#x2009;cm below the gastroesophageal junction) or conventional GOV1s (2-5&#x2009;cm below). Outcomes including 5-day treatment failure, 6-week rebleeding, and 6-week survival were compared using Kaplan-Meier analysis. Independent predictors of rebleeding and mortality were identified via Cox regression, and subgroup analyses by variceal location. We enrolled 129 patients (EVL: 59; ECI: 70) between 2006 and 2024. Both modalities achieved high hemostasis rates, with no significant differences in 5-day treatment failure (3.4% vs. 4.3%, p = 1.000), 6-week rebleeding (10.2% vs. 12.9%, p = 0.630), or 6-week survival (76.3% vs. 77.1%, p = 0.956). On multivariable analysis, portal vein thrombosis (HR 5.4, p = 0.020) and clinically significant bleeding (HR 4.4, p = 0.016) predicted 6-week rebleeding, whereas Child-Pugh class C (HR 4.3, p < 0.001) and creatinine >1.3&#x2009;mg/dL (HR 2.5, p = 0.024) predicted 6-week mortality. EVL demonstrated lower 6-week rebleeding than ECI for GOV1-CVs (9.8% vs. 40.0%, p = 0.011), but not GOV1s (p = 0.408). In the GOV1-CVs subgroup, EVL (HR 0.2, p = 0.030) was the only independent predictor of rebleeding. EVL and ECI demonstrated comparable efficacy for AVB from the cardia to the lesser curvature. Notably, EVL is superior to ECI in reducing 6-week rebleeding in GOV1-CVs, supporting EVL as the preferred approach and underscores the importance of precise variceal classification.

Retinitis pigmentosa (RP) is a leading cause of inherited blindness, yet current gene supplementation strategies are limited by heterogeneous responses, with more than 40% of patients showing insufficient rescue. Moreover, oxidative stress constitutes a defining pathological feature of RP and critically impairs the efficacy of gene therapy. Consistently, transcriptomic and ultrastructural analyses of Pde6brd10/rd10 (rd10) retinas revealed early and progressive dysregulation of oxidative stress-related pathways and photoreceptor degeneration. To overcome this barrier, we engineered an adeno-associated virus (AAV) vector covalently conjugated with a catalytic G-quadruplex-hemin DNAzyme (CoG4) via genetic code expansion and click chemistry. This design enables synchronized delivery of CoG4 and therapeutic Pde6b into photoreceptors, where CoG4 directly scavenges excess ROS and restores mitochondrial homeostasis, thereby creating a favorable microenvironment for gene supplementation. In rd10 mice, AAV-CoG4 treatment resulted in sustained expression of Pde6b, preservation of photoreceptor morphology, restoration of rod and cone function as evidenced by electroretinogram, and improved visual behavior, outperforming AAV or CoG4 monotherapies. Our findings establish oxidative stress as a major barrier to retinal gene therapy and demonstrate a dual-function platform that couples microenvironment modulation with genetic correction, offering a broadly applicable strategy for treating degenerative retinal diseases.

Advanced renal cell carcinoma (RCC) often stops responding to targeted drugs, and those drugs can be toxic. Traditional Chinese Medicine (TCM) works differently: it hits many targets at once and tries to rebalance the whole body. That makes TCM a plausible add-on, not a replacement. This review aims to comprehensively summarize and critically appraise the molecular mechanisms by which TCM components and formulas exert anti-RCC activities, focusing on key signaling pathways, non-apoptotic cell death, reversal of drug resistance, and potential synergy with conventional treatments. We searched PubMed, Web of Science, Embase, Scopus, and CNKI, covering studies published from January 2015 to January 2026. Search terms included "renal cell carcinoma" (MeSH/title/abstract), "traditional Chinese medicine", "natural product", "phytochemical", and "signaling pathway", applied in Boolean combination. Studies were included if they reported molecular-level mechanistic data for TCM-derived compounds or formulas in RCC cell lines, animal models, or clinical settings; studies reporting only descriptive ethnobotanical data without mechanistic investigation, conference abstracts without full data, or non-peer-reviewed sources were excluded. Due to the heterogeneity of study designs across the included literature, this work is framed as a comprehensive narrative review rather than a formal systematic review, and findings should be interpreted accordingly. TCM-derived compounds inhibit RCC proliferation by modulating the PI3K/AKT/mTOR, MAPK/ERK, and NF-&#x3ba;B pathways. They also induce ferroptosis and necroptosis, reverse sunitinib resistance, and enhance anti-PD-L1 immunotherapy. TCM formulas target hub genes such as CCND1, PTGS2, and ABCG2. However, most studies lack pharmacokinetic data and rigorous clinical validation. TCM acts as a multi-target network modulator against RCC. That could help overcome drug resistance and improve current therapies. Future research should focus on chemical standardization, pharmacokinetic profiling, and biomarker-driven clinical trials.

Silver-based compounds represent a promising strategy against oral biofilm-related diseases. This review systematically examines their evolution from simple salts to advanced delivery platforms, including silver diamine fluoride (SDF), nano-silver fluoride (NSF), silver nanoparticles (AgNPs), and emerging silver-organic complexes. The antibacterial action, common to all forms, stems from bioavailable Ag+ ions that attack multiple bacterial targets, disrupting cell membranes, metabolic pathways, and DNA, while inducing oxidative stress. Clinically, SDF and NSF effectively arrest caries, whereas AgNPs are primarily integrated into restorative materials, endodontic systems, and periodontal delivery platforms to inhibit dysbiotic biofilm formation. However, therapeutic success is highly formulation-dependent, facing challenges such as rapid Ag+ inactivation in saliva, esthetic concerns, and nanoparticle agglomeration. Safety profiles reveal a narrow therapeutic window, with risks of argyria from chronic accumulation, highlighting the critical importance of controlled release kinetics. Future translation hinges on the rational design of smart delivery systems, particularly ligand-engineered silver-organic complexes, which offer precise control over Ag+ release for sustained, localized efficacy with improved safety. Advancing these compounds from broad-spectrum antimicrobials to precision therapeutic platforms holds significant potential for improving outcomes in caries management, periodontal therapy, endodontics, and restorative dentistry.

Signal Transducer and Activator of Transcription 3 (STAT3), a key oncogenic target, faces clinical development hurdles for its inhibitors. A systematic retrospective analysis of Trialtrove database search yielded 130 eligible oncology trials, this perspective reveals critical developmental patterns: Trial numbers peaked in 2018 but declined post-2019, reflecting Phase III attrition due to efficacy-safety imbalances and enrollment challenges. The evolving landscape demonstrates a paradigm shift from conventional Src homology 2 (SH2) domain inhibitors toward next-generation strategies targeting the coiled-coil domain, mitochondrial STAT3, and biomarker-guided patient stratification. Realizing STAT3's therapeutic potential requires biomarker-guided patient selection, innovative inhibitors for enhanced specificity, and optimized trial strategies to bridge preclinical-clinical gaps. Learning from past outcomes and prioritizing predictive biomarkers are critical for advancing STAT3-targeted cancer therapy.

Individuals commonly exhibit different sleep patterns between weekdays and weekends, notably weekend catch-up sleep (wCUS: increased duration) and social jet lag (SJL: delayed timing). This study simultaneously examined both sleep timing and duration to identify distinct patterns of weekday-weekend sleep differences using latent class analysis and to explore their associations with chronotype, sleep disturbance, and well-being. A total of 850 adults (aged 20-55) without diagnosed sleep or psychiatric disorders and not engaged in shift work participated in an online survey assessing their sleep patterns and daytime functioning, including levels of sleepiness, fatigue, depressive symptoms, and stress. Four distinct latent classes of sleep patterns were identified: Regular Sleepers with minimal SJL and no wCUS, Mild Weekend Shift Sleepers with minimal SJL and mild wCUS, Moderate Weekend Shift Sleepers with moderate SJL and wCUS, and Extreme Weekend Shift Sleepers with high SJL and high wCUS. Individuals in the Extreme Weekend Shift Sleepers group reported significantly higher levels of sleepiness, depressive emotions, and insomnia symptoms compared to the other groups, while no significant differences were found among the remaining three groups. These findings suggest that extreme weekend sleep shifts are associated with poorer symptom profiles, consistent with the possibility that larger timing delays and weekday sleep restrictions jointly characterize this subgroup. The pattern indicates a non-linear association in which modest weekend extensions did not correlate with worse symptoms in this sample. These results are descriptive and highlight the need for longitudinal studies to confirm these associations.

Digital sequence information on genetic resources plays an increasingly vital role in research on improving crop yields and reducing impacts of disease. However, there are still problems such as the imperfect rights' protection system, which hinders the protection and sustainable use of genetic resources and constrains the realization of the fair and equitable benefit-sharing objectives established by the Convention on Biological Diversity. To address these issues, we first define the connotation and legal nature of digital sequence information on genetic resources. Then, we elaborate how digital sequence information on genetic resources aligns with the modern intellectual property systems. For digital sequence information on genetic resources to be protected by intellectual property rights, we analyze the substantive conditions of novelty and practicality as well as the procedural conditions of registration. Building on this analysis, we identify the limitations of existing intellectual property frameworks in accommodating digital sequence information on genetic resources, and propose a normative structure of sui generis right of digital sequence information on genetic resources. The subjects of sui generis right are divided into two levels, i.e. static right attributed to the state, and dynamic right exercised by the farmer collective of specific communities and the uploaders of digital sequence information, exercising respectively in accordance with different functions. The contents of sui generis right include the informed consent right, the source indication right and benefit-sharing right based on licensing. Furthermore, we propose to establish the limitations on sui generis right, such as restriction on scope of application, compulsory licensing and protection periods. 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Electroreduction of low-concentration nitrate is constrained due to the competitive hydrogen evolution side reaction and sluggish reaction dynamics. Herein, we present a NiFe-layered double hydroxide featuring hydrophobicity and oxygen vacancies formed by in situ electrochemical intercalation of sodium dodecylbenzene sulphonate (VO-NiFe-LDH/CF). Different from traditional intercalation by the ion-exchange method, VO-NiFe-LDH/CF retains the hydrophobic properties of the alkyl chains in sodium dodecylbenzene sulphonate, while the sulfonic acid functional groups inhibit the formation of surface hydroxyl groups. The introduction of oxygen vacancies induces local electron redistribution and d-band center optimization, thereby enhancing nitrate adsorption capacity and reducing the energy barrier for the rate-determining step (*NO &#x2192; *NOH). In situ FTIR measurement confirms the electrochemical hydrogenation route of nitrate. The optimized VO-NiFe-LDH/CF can realize 96.8% nitrate removal efficiency and 96% ammonia selectivity at an initial NO3 --N concentration of 50 mg L-1. The NO3 --N removal rate of VO-NiFe-LDH/CF is 2.1-fold higher than that of NiFe-LDH/CF. Furthermore, VO-NiFe-LDH/CF has excellent practicality for practical industrial nitrate-wastewater; the total nitrogen was reduced from 114.7 mg L-1 to 7 mg L-1. VO-NiFe-LDH/CF shows outstanding stability and robust anti-interference performance during the long-term experiments.

High-quality total nitrogen (TN) and total phosphorus (TP) data are essential for wastewater management and aquatic ecosystem protection, yet routine determinations rely on labor- and reagent-intensive wet chemistry. Ultraviolet-visible (UV-Vis) spectroscopy offers a rapid, low-consumable alternative, but TN/TP quantification in heterogeneous wastewater remains challenging because they are composite indices with weak, dispersed spectral features and strong matrix effects. Here we introduce a task-specific machine learning framework for TN/TP measurement via UV-Vis. TN is modeled with eXtreme Gradient Boosting (XGB) on pretreated spectra to capture nonlinearities with built-in regularization; TP, whose spectral cues are weaker and more dispersed, is addressed by a 1D-CNN-XGB hybrid pipeline, where the CNN distills shift-tolerant local motifs and reduces collinearity before nonlinear regression by XGB. The proposed method achieves R2&#xa0;=&#xa0;0.9691 (RPD&#xa0;=&#xa0;5.94) for TN and R2&#xa0;=&#xa0;0.8738 (RPD&#xa0;=&#xa0;3.43) for TP. Wavelength-resolved attributions with latent back-projection reveal distinct spectral drivers: TN is dominated by compact deep-UV motifs with a secondary UV band and minor near-UV features consistent with matrix/background variability, whereas TP follows a distributed, shape-based multi-band UV rule captured by the CNN. This framework outlines an effective and interpretable UV-Vis approach for TN/TP measurement in wastewater and may enable rapid, in-situ, low-consumable monitoring workflow towards field deployment.

Our aim was to explore the impact of wearable devices on rehabilitation of stroke patients. We searched the CINAHL, Cochrane, Embase, PsycINFO, PubMed, Web of Science, China National Knowledge Internet, Wanfang, SineMed, and VIP databases to identify articles from inception through Janu 15th, 2026. The primary endpoint was limbs function, comprising upper limb function, lower limb function, and overall motor function, while secondary endpoints included quality of life and activities of daily living. The standard mean difference was taken as the effect value and the random effects method was adopted when I 2 was greater than 50%. The risk bias assessment employed the Cochrane Collaboration's tool, while the sensitivity analysis and publication bias were respectively evaluated through the sequential exclusion method and the Egger's method. This systematic review included 20 studies and a total sample of 1007 participants. Significant statistical differences were observed in upper limb function (SMD 0.27, 95%CI 0.09 to 0.45, P=0.004), lower limb function (SMD 1.58, 95%CI 0.31 to 2.85, P=0.01), overall motor function (SMD 4.59 95% CI, 0.80 to 8.38, P=0.02) and activities of daily living (SMD 2.61, 95%CI 1.18 to 4.04, P<0.01) among stroke patients. Wearable devices are found to have positive impact on the limb function and activity of daily living of stroke patients, but have no significant intervention effect on quality of life.

The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the determination of physicochemical properties are crucial in drug development. However, these tasks remain challenging due to issues such as the limited generalizability of single-modal representations, the absence of multitask prediction frameworks, and weak adaptability in cold-start scenarios. In this study, we present DrugDL, a framework for comprehensive drug molecule representation and the prediction of multiple downstream tasks, including drug-target interactions, binding affinities, binding sites, physicochemical properties, toxicity, and drug-drug interactions. DrugDL jointly learns representations of the drug chemical space and the target protein biological space, while capturing multiscale interaction mechanisms between drug molecules and target proteins through the integration of cross-modal contrastive learning and single-modal feature enhancement algorithms. Specifically, DrugDL employs a multitask prediction framework to predict multiple properties of drug molecules. In practical applications, it consistently outperforms state-of-the-art methods, particularly in cold-start tasks. The framework has been successfully applied to high-throughput screening, the identification of inhibitors of SARS-CoV-2 and metabolic enzymes, and the prediction of cancer-targeted drugs. Experimental validations on EGFR and ALK targets further demonstrate its effectiveness as a precise drug discovery tool. By enabling accurate molecular representation and multi-property prediction, DrugDL provides end-to-end technical support for drug development, thereby significantly accelerating the drug discovery process. The datasets and code are available at https://github.com/ZhangQi9910/DrugDL. The version of record is archived in Zenodo with the DOI: 10.5281/zenodo.20579718. Supplementary data are available at Bioinformatics online.