Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR sc...Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR screening due to a shortage of ophthalmologists.This study reports the implementation and outcomes of the Chinese local standard DB52/T 1726-2023,Regulations for the application of diabetic retinopathy screening artificial intelligence,in Cambodian healthcare institutions.A pilot DR screening program with independent operational capability is established by providing a non-mydriatic fundus camera and deploying a localized diabetic retinopathy artificial intelligence(DR-AI)screening platform at the Cambodia-Kingdom Friendship Hospital in Phnom Penh,along with comprehensive training.From January to August 2025,a total of 565 patients with type 2 diabetes were screened,yielding a DR detection rate of 26.0%(147 cases).Research findings demonstrate that applying mature Chinese DR-AI screening standards and technological solutions through international collaboration in regions with a scarcity of ophthalmic professionals is both feasible and effective.This project serves as a reference for promoting DR-AI in resource-constrained countries and regions,highlighting its significant potential to leverage AI in addressing the global burden of chronic diseases and advancing the modernization of health systems.展开更多
Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual pat...Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Colorectal cancer(CRC)is a prevalent malignancy worldwide,posing a significant public health concern.Mounting evidence has confirmed that timely early screening facilitates the detection of incipient CRC,thereby enhan...Colorectal cancer(CRC)is a prevalent malignancy worldwide,posing a significant public health concern.Mounting evidence has confirmed that timely early screening facilitates the detection of incipient CRC,thereby enhancing patient prognosis.Obviously,non-participation of asymptomatic individuals in screening programs hampers early diagnosis and may adversely affect long-term outcomes for CRC patients.In this letter,we provide a comprehensive overview of the current status of early screening practices,while also thoroughly examine the dilemmas and potential solutions associated with early screening for CRC.In response to these issues,we proffer a set of recommendations directed at governmental authorities and the general public,which focus on augmenting financial investment,establishing standardized screening protocols,advancing technological capabilities,and bolstering public awareness campaigns.The importance of collaborative efforts from various stakeholders cannot be overstated in the quest to enhance early detection rates and alleviate the societal burden of CRC.展开更多
The problem of gastric cancer(GC)prevention remains relevant for a long time.Various methods of population serological screening of atrophic gastritis and precancerous changes in the gastric mucosa have been created a...The problem of gastric cancer(GC)prevention remains relevant for a long time.Various methods of population serological screening of atrophic gastritis and precancerous changes in the gastric mucosa have been created at present.Modern endoscopic and morphological methods of verification of the diagnosis of precancerous diseases and changes in the gastric mucosa have been introduced into the practice of gastroenterologists and oncologists.GC risk stratification systems allow the formation of risk groups that require population screening.Practical hints for population serological screening of atrophic gastritis,endoscopic and morphological verification of precancerous changes and diseases of the stomach recommend using it:When developing state programs for the prevention of stomach cancer;when implementing preventive measures for stomach cancer by doctors of all specialties;the authors also offer the possibility of use by anyone over the age of 40,provided that they seek methodological help from their doctor;in the work of health schools in any medical and preventive institutions.The use of an assessment system of certain risk factor signatures with prognostic value would add significant assistance to preventive measures against GC.展开更多
Natural antimicrobial peptides(AMPs)are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens.They have found extensive applications in the fields of m...Natural antimicrobial peptides(AMPs)are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens.They have found extensive applications in the fields of medicine,food,and agriculture.However,efficiently screening AMPs from natural sources poses several challenges,including low efficiency and high antibiotic resistance.This review focuses on the action mechanisms of AMPs,both through membrane and non-membrane routes.We thoroughly examine various highly efficient AMP screening methods,including whole-bacterial adsorption binding,cell membrane chromatography(CMC),phospholipid membrane chromatography binding,membranemediated capillary electrophoresis(CE),colorimetric assays,thin layer chromatography(TLC),fluorescence-based screening,genetic sequencing-based analysis,computational mining of AMP databases,and virtual screening methods.Additionally,we discuss potential developmental applications for enhancing the efficiency of AMP discovery.This review provides a comprehensive framework for identifying AMPs within complex natural product systems.展开更多
Rapid screening of inorganic arsenic(iAs)in groundwater used for drinking by hundreds of millions of mostly rural residents worldwide is crucial for health protection.Most commercial field test kits are based on the G...Rapid screening of inorganic arsenic(iAs)in groundwater used for drinking by hundreds of millions of mostly rural residents worldwide is crucial for health protection.Most commercial field test kits are based on the Gutzeit reaction that uses mercury-based reagents for color development,an environmental concern that increasingly limits its utilization.This study further improves the Molybdenum Blue(MB)colorimetric method to allow for faster screening with more stable reagents.More importantly,a portable three-channel colorimeter is developed for screening iAs relative to the WHO drinking water guideline value(10μg/L).Adding the reducing reagents in sequence not only prolongs the storage time to>7 days,but also accelerates the color development time to 6 min in conjunction with lowering the H_(2)SO_(4) concentration in chromogenic reagents.The optimal pH ranges from 1.2 to 1.3 and is achieved by acidifying groundwater to 1%(V/V)HCl.With detection limits of 3.7μg/L for inorganic arsenate(iAs(V))and 3.8μg/L for inorganic arsenite(iAs(Ⅲ)),testing groundwater with-10μg/L of As has a precision<20%.The method works well for a range of phosphate concentrations of 48-950μg/L(0.5-10μmol/L).Concentrations of total_iAs(6-300μg/L),iAs(V)(6-230μg/L)and iAs(Ⅲ)(0-170μg/L)for 14 groundwater samples from Yinchuan Plain,Pearl River Delta,and Jianghan Plain,are in excellent agreements(linear regression slope:0.969-1.029)with the benchmark methods.The improved chemistry here lays the foundation for the MB colorimetric method to become a commercially viable screening tool,with further engineering and design improvement of the colorimeter.展开更多
Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state b...Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.展开更多
As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as l...As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.展开更多
Background:It is found to have association of facial parameters with trisomy 21 fetuses(T 21).We have compared prenasal thickness(PNT),nasal bone length(NBL),and the PNT:NBL ratio of normal fetuses with fetuses with t...Background:It is found to have association of facial parameters with trisomy 21 fetuses(T 21).We have compared prenasal thickness(PNT),nasal bone length(NBL),and the PNT:NBL ratio of normal fetuses with fetuses with trisomy 21(T 21)between 16 and 25 weeks of gestation as a diagnostic tool for T 21.Methods:Facial profile images in the two dimensional(2D)gray scale were assessed to measure fetal NBL and PNT between 16 and 25 weeks of gestation.The PNT:NBL ratio of the fetuses was calculated.Nomograms were constructed from the data of morphologically normal fetuses at live birth.The PNT,NBL,and PNT:NBL ratio of fetuses with confirmed T 21(n=31)and morphologically normal fetuses at live birth(controls,n=3485)were compared.Results:Nomograms for PNT,NBL,and the PNT:NBL ratio were constructed.In T 21 fetuses,PNT(>95th percentile),NBL(<5th percentile),and the PNT:NBL ratio(>95th percentile)showed a sensitivity of 25%,29%,and 45%for PNT,NBL,and PNT:NBL,respectively,and specificity of 95%,96%,and 94%,for PNT,NBL,and PNT:NBL,respectively.All of these markers showed a negative predictive value of 99%.Conclusion:PNT,NBL,and the PNT:NBL ratio have high diagnostic value for fetuses with Down syndrome and can be incorporated easily in the current second trimester screening protocol for T 21.PNT,NBL,and the PNT:NBL ratio are more specific markers for Down syndrome than those used in previous studies.展开更多
Liver transplantation(LT)is the definitive treatment for end-stage liver disease,acute liver failure,and liver cancer.Although advancements in surgical techniques,postoperative care,and immunosuppressive therapies hav...Liver transplantation(LT)is the definitive treatment for end-stage liver disease,acute liver failure,and liver cancer.Although advancements in surgical techniques,postoperative care,and immunosuppressive therapies have significantly improved outcomes,the long-term use of immunosuppression has increased the risk of complications,including infections,cardiovascular disease,and cancer.Among these,de novo malignancies(DNMs)are a major concern,accounting for 20%-25%of deaths in LT recipients surviving beyond the early post-transplant period.Non-melanoma skin cancers,particularly squamous cell carcinoma are the most prevalent DNMs.Other significant malignancies include Kaposi's sarcoma,post-transplant lymphoproliferative disorders,and various solid organ cancers,including head and neck cancers.Compared to the general population,LT patients face a twofold increase in solid organ malignancies and a 30-fold increase in lymphoproliferative disorders.Risk factors for DNM include chronic immunosuppression,alcohol or tobacco use,viral infections,and underlying liver disease.Emerging evidence emphasizes the importance of tailored cancer screening and prevention strategies,including regular dermatological examinations,targeted screenings for high-risk cancers,and patient education on lifestyle modifications.Early detection through enhanced surveillance protocols has been shown to improve outcomes.Management of DNMs involves a combination of standard oncological therapies and adjustments to immunosuppressive regimens,with promising results from the use of mTOR inhibitors in select patients.The review highlights the critical need for ongoing research to refine risk stratification,optimize screening protocols,and improve treatment approaches to mitigate the burden of DNMs in LT recipients.By implementing personalized preventive and therapeutic strategies,we can enhance long-term outcomes and quality of life for this vulnerable population.展开更多
This study investigated environmental distribution and human exposure of polycyclic aromatic hydrocarbons(PAHs)and their derivatives in one Chinese petroleum refinery facility.It was found that,following with high con...This study investigated environmental distribution and human exposure of polycyclic aromatic hydrocarbons(PAHs)and their derivatives in one Chinese petroleum refinery facility.It was found that,following with high concentrations of 16 EPA PAHs(∑Parent-PAHs)in smelting subarea of studied petroleum refinery facility,total derivatives of PAHs[named as XPAHs,including nitro PAHs(NPAHs),chlorinated PAHs(Cl-PAHs),and brominated PAHs(Br-PAHs)]in gas(mean=1.57×10^(4)ng/m^(3)),total suspended particulate(TSP)(mean=4.33×10^(3) ng/m^(3))and soil(mean=4.37×10^(3) ng/g)in this subarea had 1.76-6.19 times higher levels than those from other subareas of this facility,surrounding residential areas and reference areas,indicating that petroleum refining processes would lead apparent derivation of PAHs.Especially,compared with those in residential and reference areas,gas samples in the petrochemical areas had higher∑NPAH/∑PAHs(mean=2.18),but lower∑Cl-PAH/∑PAHs(mean=1.43×10^(-1))and∑Br-PAH/∑PAHs ratios(mean=7.49×10^(-2)),indicating the richer nitrification of PAHs than chlorination during petrochemical process.The occupational exposure to PAHs and XPAHs in this petroleum refinery facility were 24-343 times higher than non-occupational exposure,and the ILCR(1.04×10^(-4))for petrochemical workers was considered to be potential high risk.Furthermore,one expanded high-resolution screening through GC Orbitrap/MS was performed for soils from petrochemical area,and another 35 PAHs were found,including alkyl-PAHs,phenyl-PAHs and other species,indicat-ing that profiles and risks of PAHs analogs in petrochemical areas deserve further expanded investigation.展开更多
High-entropy alloys(HEAs)have emerged as promising catalysts for the hydrogen evolution reaction(HER)due to their compositional diversity and synergistic effects.In this study,machine learning-accelerated density func...High-entropy alloys(HEAs)have emerged as promising catalysts for the hydrogen evolution reaction(HER)due to their compositional diversity and synergistic effects.In this study,machine learning-accelerated density functional theory(DFT)calculations were employed to assess the catalytic performance of PtPd-based HEAs with the formula PtPdXYZ(X,Y,Z=Fe,Co,Ni,Cu,Ru,Rh,Ag,Au;X≠Y≠Z).Among 56 screened HEA(111)surfaces,PtPdRuCoNi(111)was identified as the most promising,with adsorption energies(E_(ads))between−0.50 and−0.60 eV and high d-band center of−1.85 eV,indicating enhanced activity.This surface showed the hydrogen adsorption free energy(ΔG_(H^(*)))of−0.03 eV for hydrogen adsorption,outperforming Pt(111)by achieving a better balance between adsorption and desorption.Machine learning models,particularly extreme gradient boosting regression(XGBR),significantly reduced computational costs while maintaining high accuracy(root-mean-square error,RMSE=0.128 eV).These results demonstrate the potential of HEAs for efficient and sustainable hydrogen production.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor ...Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor agents a research hotspot.In this study,artificial intelligence(AI)technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline,which combined Voting strategy based on compound-protein interaction(CPI)prediction models,cascade molecular docking,and molecular dynamic(MD)simulations.The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays.Among the identified compounds,Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity(IC_(50)=5.41 nM)than that of tubastatin A(TubA)(IC_(50)=15.11 nM),along with a favorable subtype selectivity profile(selectivity index z 117.23 for HDAC1),which was further verified by the Western blot analysis.Additionally,Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells,exerting desirable antiproliferative activity(IC_(50)=2.59 mM).Furthermore,based on long-term MD simulation trajectory,the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis,thereby elucidating its binding mechanism.Moreover,the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation,thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.展开更多
Helicobacter pylori(H.pylori)infection induces pathological changes via chronic inflammation and virulence factors,thereby increasing the risk of gastric cancer development.Compared with invasive examination methods,H...Helicobacter pylori(H.pylori)infection induces pathological changes via chronic inflammation and virulence factors,thereby increasing the risk of gastric cancer development.Compared with invasive examination methods,H.pylori-related serum indicators are cost-effective and valuable for the early detection of gastric cancer(GC);however,large-scale clinical validation and sufficient understanding of the specific molecular mechanisms involved are lacking.Therefore,a comprehensive review and analysis of recent advances in this field is necessary.In this review,we systematically analyze the relationship between H.pylori and GC and discuss the application of new molecular biomarkers in GC screening.We also summarize the screening potential and application of anti-H.pylori immunoglobulin G and virulence factor-related serum antibodies for identifying GC risk.These indicators provide early warning of infection and enhance screening accuracy.Additionally,we discuss the potential combination of multiple screening indicators for the comprehensive analysis and development of emerging testing methods to improve the accuracy and efficiency of GC screening.Although this review may lack sufficient evidence due to limitations in existing studies,including small sample sizes,regional variations,and inconsistent testing methods,it contributes to advancing personalized precision medicine in high-risk populations and developing GC screening strategies.展开更多
Colorectal cancer(CRC)is the third most commonly diagnosed cancer and the second leading cause of cancer death worldwide.The leading risk factors for CRC include male gender,age over 50,family history,obesity,tobacco ...Colorectal cancer(CRC)is the third most commonly diagnosed cancer and the second leading cause of cancer death worldwide.The leading risk factors for CRC include male gender,age over 50,family history,obesity,tobacco smoking,alco-hol consumption,and unhealthy diet.CRC screening methods vary considerably between countries and depend on incidence,economic resources and healthcare structure.Important aspects of screening include adherence,which can vary signi-ficantly across ethnic and socioeconomic groups.Basic concepts of CRC screening include pre-stratification of patients by identifying risk factors and then using fecal immunochemical test or guaiac-based fecal occult blood test and/or colono-scopy or radiologic imaging techniques.Technological capabilities for CRC scree-ning are rapidly evolving and include stool DNA test,liquid biopsy,virtual colo-nography,and the use of artificial intelligence.A CRC prevention strategy should be comprehensive and include active patient education along with targeted imple-mentation of screening.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
基金funded by the Chronic Disease Management Research Project of National Health Commission Capacity Building and Continuing Education Center 2025(GWJJMB202510024146)the Post-Subsidy Project for Standard Development of Guizhou Provincial Market Supervision and Administration Bureau 2025(DB52/T1726-2023)the Guizhou Provincial Health Commission Science and Technology Fund Project(gzwkj2024-076,gzwkj2026-146).
文摘Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR screening due to a shortage of ophthalmologists.This study reports the implementation and outcomes of the Chinese local standard DB52/T 1726-2023,Regulations for the application of diabetic retinopathy screening artificial intelligence,in Cambodian healthcare institutions.A pilot DR screening program with independent operational capability is established by providing a non-mydriatic fundus camera and deploying a localized diabetic retinopathy artificial intelligence(DR-AI)screening platform at the Cambodia-Kingdom Friendship Hospital in Phnom Penh,along with comprehensive training.From January to August 2025,a total of 565 patients with type 2 diabetes were screened,yielding a DR detection rate of 26.0%(147 cases).Research findings demonstrate that applying mature Chinese DR-AI screening standards and technological solutions through international collaboration in regions with a scarcity of ophthalmic professionals is both feasible and effective.This project serves as a reference for promoting DR-AI in resource-constrained countries and regions,highlighting its significant potential to leverage AI in addressing the global burden of chronic diseases and advancing the modernization of health systems.
基金supported by the Natural Science Foundation of Guangdong Province(No.2021B1515120053)Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515140166).
文摘Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘Colorectal cancer(CRC)is a prevalent malignancy worldwide,posing a significant public health concern.Mounting evidence has confirmed that timely early screening facilitates the detection of incipient CRC,thereby enhancing patient prognosis.Obviously,non-participation of asymptomatic individuals in screening programs hampers early diagnosis and may adversely affect long-term outcomes for CRC patients.In this letter,we provide a comprehensive overview of the current status of early screening practices,while also thoroughly examine the dilemmas and potential solutions associated with early screening for CRC.In response to these issues,we proffer a set of recommendations directed at governmental authorities and the general public,which focus on augmenting financial investment,establishing standardized screening protocols,advancing technological capabilities,and bolstering public awareness campaigns.The importance of collaborative efforts from various stakeholders cannot be overstated in the quest to enhance early detection rates and alleviate the societal burden of CRC.
文摘The problem of gastric cancer(GC)prevention remains relevant for a long time.Various methods of population serological screening of atrophic gastritis and precancerous changes in the gastric mucosa have been created at present.Modern endoscopic and morphological methods of verification of the diagnosis of precancerous diseases and changes in the gastric mucosa have been introduced into the practice of gastroenterologists and oncologists.GC risk stratification systems allow the formation of risk groups that require population screening.Practical hints for population serological screening of atrophic gastritis,endoscopic and morphological verification of precancerous changes and diseases of the stomach recommend using it:When developing state programs for the prevention of stomach cancer;when implementing preventive measures for stomach cancer by doctors of all specialties;the authors also offer the possibility of use by anyone over the age of 40,provided that they seek methodological help from their doctor;in the work of health schools in any medical and preventive institutions.The use of an assessment system of certain risk factor signatures with prognostic value would add significant assistance to preventive measures against GC.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82373835,82304437,and 82173781)Regional Joint Fund Project of Guangdong Basic and Applied Basic Research Fund,China(Grant Nos.:2023A1515110417 and 2023A1515140131)+2 种基金Regional Joint Fund-Key Project of Guangdong Basic and Applied Basic Research Fund,China(Grant No.:2020B1515120033)the Key Field Projects of General Universities in Guangdong Province,China(Grant Nos.:2020ZDZX2057 and 2022ZDZX2056)Medical Scientific Research Foundation of Guangdong Province of China(Grant No.:A2022061).
文摘Natural antimicrobial peptides(AMPs)are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens.They have found extensive applications in the fields of medicine,food,and agriculture.However,efficiently screening AMPs from natural sources poses several challenges,including low efficiency and high antibiotic resistance.This review focuses on the action mechanisms of AMPs,both through membrane and non-membrane routes.We thoroughly examine various highly efficient AMP screening methods,including whole-bacterial adsorption binding,cell membrane chromatography(CMC),phospholipid membrane chromatography binding,membranemediated capillary electrophoresis(CE),colorimetric assays,thin layer chromatography(TLC),fluorescence-based screening,genetic sequencing-based analysis,computational mining of AMP databases,and virtual screening methods.Additionally,we discuss potential developmental applications for enhancing the efficiency of AMP discovery.This review provides a comprehensive framework for identifying AMPs within complex natural product systems.
基金the National Key R&D Program of China(No.2021YFA0715900)the National Natural Science Foundation of China(No.41831279)+2 种基金the Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks(No.ZDSYS20220606100604008)the Guangdong Province Bureau of Education(No.2020KCXTD006)the Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control(No.2023B1212060002).
文摘Rapid screening of inorganic arsenic(iAs)in groundwater used for drinking by hundreds of millions of mostly rural residents worldwide is crucial for health protection.Most commercial field test kits are based on the Gutzeit reaction that uses mercury-based reagents for color development,an environmental concern that increasingly limits its utilization.This study further improves the Molybdenum Blue(MB)colorimetric method to allow for faster screening with more stable reagents.More importantly,a portable three-channel colorimeter is developed for screening iAs relative to the WHO drinking water guideline value(10μg/L).Adding the reducing reagents in sequence not only prolongs the storage time to>7 days,but also accelerates the color development time to 6 min in conjunction with lowering the H_(2)SO_(4) concentration in chromogenic reagents.The optimal pH ranges from 1.2 to 1.3 and is achieved by acidifying groundwater to 1%(V/V)HCl.With detection limits of 3.7μg/L for inorganic arsenate(iAs(V))and 3.8μg/L for inorganic arsenite(iAs(Ⅲ)),testing groundwater with-10μg/L of As has a precision<20%.The method works well for a range of phosphate concentrations of 48-950μg/L(0.5-10μmol/L).Concentrations of total_iAs(6-300μg/L),iAs(V)(6-230μg/L)and iAs(Ⅲ)(0-170μg/L)for 14 groundwater samples from Yinchuan Plain,Pearl River Delta,and Jianghan Plain,are in excellent agreements(linear regression slope:0.969-1.029)with the benchmark methods.The improved chemistry here lays the foundation for the MB colorimetric method to become a commercially viable screening tool,with further engineering and design improvement of the colorimeter.
基金the National Key Research Program of China under granted No.92164201National Natural Science Foundation of China for Distinguished Young Scholars No.62325403+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(62304147).
文摘Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.
基金supported by the National Natural Science Foundation of China(Grant Nos.22225801,W2441009,22408228)。
文摘As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.
文摘Background:It is found to have association of facial parameters with trisomy 21 fetuses(T 21).We have compared prenasal thickness(PNT),nasal bone length(NBL),and the PNT:NBL ratio of normal fetuses with fetuses with trisomy 21(T 21)between 16 and 25 weeks of gestation as a diagnostic tool for T 21.Methods:Facial profile images in the two dimensional(2D)gray scale were assessed to measure fetal NBL and PNT between 16 and 25 weeks of gestation.The PNT:NBL ratio of the fetuses was calculated.Nomograms were constructed from the data of morphologically normal fetuses at live birth.The PNT,NBL,and PNT:NBL ratio of fetuses with confirmed T 21(n=31)and morphologically normal fetuses at live birth(controls,n=3485)were compared.Results:Nomograms for PNT,NBL,and the PNT:NBL ratio were constructed.In T 21 fetuses,PNT(>95th percentile),NBL(<5th percentile),and the PNT:NBL ratio(>95th percentile)showed a sensitivity of 25%,29%,and 45%for PNT,NBL,and PNT:NBL,respectively,and specificity of 95%,96%,and 94%,for PNT,NBL,and PNT:NBL,respectively.All of these markers showed a negative predictive value of 99%.Conclusion:PNT,NBL,and the PNT:NBL ratio have high diagnostic value for fetuses with Down syndrome and can be incorporated easily in the current second trimester screening protocol for T 21.PNT,NBL,and the PNT:NBL ratio are more specific markers for Down syndrome than those used in previous studies.
文摘Liver transplantation(LT)is the definitive treatment for end-stage liver disease,acute liver failure,and liver cancer.Although advancements in surgical techniques,postoperative care,and immunosuppressive therapies have significantly improved outcomes,the long-term use of immunosuppression has increased the risk of complications,including infections,cardiovascular disease,and cancer.Among these,de novo malignancies(DNMs)are a major concern,accounting for 20%-25%of deaths in LT recipients surviving beyond the early post-transplant period.Non-melanoma skin cancers,particularly squamous cell carcinoma are the most prevalent DNMs.Other significant malignancies include Kaposi's sarcoma,post-transplant lymphoproliferative disorders,and various solid organ cancers,including head and neck cancers.Compared to the general population,LT patients face a twofold increase in solid organ malignancies and a 30-fold increase in lymphoproliferative disorders.Risk factors for DNM include chronic immunosuppression,alcohol or tobacco use,viral infections,and underlying liver disease.Emerging evidence emphasizes the importance of tailored cancer screening and prevention strategies,including regular dermatological examinations,targeted screenings for high-risk cancers,and patient education on lifestyle modifications.Early detection through enhanced surveillance protocols has been shown to improve outcomes.Management of DNMs involves a combination of standard oncological therapies and adjustments to immunosuppressive regimens,with promising results from the use of mTOR inhibitors in select patients.The review highlights the critical need for ongoing research to refine risk stratification,optimize screening protocols,and improve treatment approaches to mitigate the burden of DNMs in LT recipients.By implementing personalized preventive and therapeutic strategies,we can enhance long-term outcomes and quality of life for this vulnerable population.
基金supported by the National Key Research and Development Program of China(No.2019YFC1804501)the National Natural Science Foundation of China(Nos.22036007 and 22122611)+1 种基金the Natural Science Foundation of Shandong Province(No.ZR2020ME228)the Introduction and Cultivation Plan for Young Innovative Talents of Colleges and Universities.
文摘This study investigated environmental distribution and human exposure of polycyclic aromatic hydrocarbons(PAHs)and their derivatives in one Chinese petroleum refinery facility.It was found that,following with high concentrations of 16 EPA PAHs(∑Parent-PAHs)in smelting subarea of studied petroleum refinery facility,total derivatives of PAHs[named as XPAHs,including nitro PAHs(NPAHs),chlorinated PAHs(Cl-PAHs),and brominated PAHs(Br-PAHs)]in gas(mean=1.57×10^(4)ng/m^(3)),total suspended particulate(TSP)(mean=4.33×10^(3) ng/m^(3))and soil(mean=4.37×10^(3) ng/g)in this subarea had 1.76-6.19 times higher levels than those from other subareas of this facility,surrounding residential areas and reference areas,indicating that petroleum refining processes would lead apparent derivation of PAHs.Especially,compared with those in residential and reference areas,gas samples in the petrochemical areas had higher∑NPAH/∑PAHs(mean=2.18),but lower∑Cl-PAH/∑PAHs(mean=1.43×10^(-1))and∑Br-PAH/∑PAHs ratios(mean=7.49×10^(-2)),indicating the richer nitrification of PAHs than chlorination during petrochemical process.The occupational exposure to PAHs and XPAHs in this petroleum refinery facility were 24-343 times higher than non-occupational exposure,and the ILCR(1.04×10^(-4))for petrochemical workers was considered to be potential high risk.Furthermore,one expanded high-resolution screening through GC Orbitrap/MS was performed for soils from petrochemical area,and another 35 PAHs were found,including alkyl-PAHs,phenyl-PAHs and other species,indicat-ing that profiles and risks of PAHs analogs in petrochemical areas deserve further expanded investigation.
基金the Second Century Fund(C2F),Chulalongkorn UniversityThailand Science Research and Innovation Fund Chulalongkorn University(No.IND_FF_68_054_2100_009)National Science and Technology Development Agency,Thailand,Hub of Knowledge funding,and the Mid-Career Research Grant 2024,National Research Council of Thailand(No.N42A670295).
文摘High-entropy alloys(HEAs)have emerged as promising catalysts for the hydrogen evolution reaction(HER)due to their compositional diversity and synergistic effects.In this study,machine learning-accelerated density functional theory(DFT)calculations were employed to assess the catalytic performance of PtPd-based HEAs with the formula PtPdXYZ(X,Y,Z=Fe,Co,Ni,Cu,Ru,Rh,Ag,Au;X≠Y≠Z).Among 56 screened HEA(111)surfaces,PtPdRuCoNi(111)was identified as the most promising,with adsorption energies(E_(ads))between−0.50 and−0.60 eV and high d-band center of−1.85 eV,indicating enhanced activity.This surface showed the hydrogen adsorption free energy(ΔG_(H^(*)))of−0.03 eV for hydrogen adsorption,outperforming Pt(111)by achieving a better balance between adsorption and desorption.Machine learning models,particularly extreme gradient boosting regression(XGBR),significantly reduced computational costs while maintaining high accuracy(root-mean-square error,RMSE=0.128 eV).These results demonstrate the potential of HEAs for efficient and sustainable hydrogen production.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
基金funded by Central Guidance on Local Science and Technology Development Fund of Hebei Province,China(Grant No.:226Z2605G)the Key Project from Hebei Provincial Department of Science and Technology,China(Grant No.:21372601D)+6 种基金Graduate Student Innovation Grant Program of Hebei Medical University,China(Grant No.:XCXZZB202303)Science Research Project of Hebei Education Department,China(Grant Nos.:BJ2025046,and CYZD202501)Program for Young Scientists in the Field of Natural Science of Hebei Medical University,China(Program Nos.:CYCZ2023010,CYCZ2023011,CYQD2021011,CYQD2021015 and CYQD2023012)Traditional Chinese Medicine Administration Project of Hebei Province,China(Project No.:2025427)National Natural Science Foundation of China(Grant No.:32100771)the Hebei Provincial Medical Science Research Project Plan,China(Project Nos.:20240241 and 20220200)Shijiazhuang Science and Technology Bureau,China(Grant Nos.:241200487A,and 07202204).
文摘Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor agents a research hotspot.In this study,artificial intelligence(AI)technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline,which combined Voting strategy based on compound-protein interaction(CPI)prediction models,cascade molecular docking,and molecular dynamic(MD)simulations.The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays.Among the identified compounds,Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity(IC_(50)=5.41 nM)than that of tubastatin A(TubA)(IC_(50)=15.11 nM),along with a favorable subtype selectivity profile(selectivity index z 117.23 for HDAC1),which was further verified by the Western blot analysis.Additionally,Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells,exerting desirable antiproliferative activity(IC_(50)=2.59 mM).Furthermore,based on long-term MD simulation trajectory,the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis,thereby elucidating its binding mechanism.Moreover,the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation,thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.
文摘Helicobacter pylori(H.pylori)infection induces pathological changes via chronic inflammation and virulence factors,thereby increasing the risk of gastric cancer development.Compared with invasive examination methods,H.pylori-related serum indicators are cost-effective and valuable for the early detection of gastric cancer(GC);however,large-scale clinical validation and sufficient understanding of the specific molecular mechanisms involved are lacking.Therefore,a comprehensive review and analysis of recent advances in this field is necessary.In this review,we systematically analyze the relationship between H.pylori and GC and discuss the application of new molecular biomarkers in GC screening.We also summarize the screening potential and application of anti-H.pylori immunoglobulin G and virulence factor-related serum antibodies for identifying GC risk.These indicators provide early warning of infection and enhance screening accuracy.Additionally,we discuss the potential combination of multiple screening indicators for the comprehensive analysis and development of emerging testing methods to improve the accuracy and efficiency of GC screening.Although this review may lack sufficient evidence due to limitations in existing studies,including small sample sizes,regional variations,and inconsistent testing methods,it contributes to advancing personalized precision medicine in high-risk populations and developing GC screening strategies.
文摘Colorectal cancer(CRC)is the third most commonly diagnosed cancer and the second leading cause of cancer death worldwide.The leading risk factors for CRC include male gender,age over 50,family history,obesity,tobacco smoking,alco-hol consumption,and unhealthy diet.CRC screening methods vary considerably between countries and depend on incidence,economic resources and healthcare structure.Important aspects of screening include adherence,which can vary signi-ficantly across ethnic and socioeconomic groups.Basic concepts of CRC screening include pre-stratification of patients by identifying risk factors and then using fecal immunochemical test or guaiac-based fecal occult blood test and/or colono-scopy or radiologic imaging techniques.Technological capabilities for CRC scree-ning are rapidly evolving and include stool DNA test,liquid biopsy,virtual colo-nography,and the use of artificial intelligence.A CRC prevention strategy should be comprehensive and include active patient education along with targeted imple-mentation of screening.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
