HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

Objective:To explore the potential of the novel fibroblast activation protein (FAP)-targeted theranostic agent 68Ga/ 177Lu-1, 4, 7, 10-tetraazacyclododecane-1, 4, 7, 10-tetraacetic acid (DOTA)-2P (FAP inhibitor (FAPI)) 2 in microsatellite stable (MSS) colorectal cancer, and to evaluate the efficacy and underlying mechanism of 177Lu-DOTA-2P(FAPI) 2 in combination with immune checkpoint inhibitors (ICIs). Methods:This study was a randomized, parallel-group design. DOTA-2P(FAPI) 2 was labeled with 68Ga or 177Lu respectively. The binding performance of DOTA-2P(FAPI) 2 to FAP was validated through in vitro cell experiments. FAP-positive CT26-FAP tumor-bearing mouse model was constructed, and microPET imaging and biodistribution were performed. The in vivo antitumor efficacy was assessed for the 177Lu-DOTA-2P(FAPI) 2 monotherapy, α programmed death-ligand 1 (PD-L1) monotherapy, and the combination of 177Lu-DOTA-2P(FAPI) 2 with αPD-L1 therapy groups. Changes in the tumor microenvironment were analyzed using single-cell RNA sequencing to elucidate the mechanism of the combined treatment. Independent-sample t test was used to analyze data. Survival analysis was performed using the log-rank test. Results:The labeling yields of 68Ga/ 177Lu-DOTA-2P(FAPI) 2 were both >90%, with the radiochemical purities both >95%. In vitro cellular uptake and blocking assays showed that FAPI-46 significantly inhibited the binding of 68Ga-DOTA-2P(FAPI) 2 to FAP in CT26-FAP cells, with the cellular uptake values at 60min of (51.5±0.8)% and (1.0±0.3)%, respectively ( t=102.40, P<0.001). MicroPET imaging showed that the tumor uptake of 68Ga-DOTA-2P(FAPI) 2 remained stable even at 4 h post-injection, with a significantly higher uptake value compared to 68Ga-FAPI-46 ((7.3±1.6) vs (3.7±0.2) percentage activity of injection dose per gram of tissue (%ID/g); t=3.87, P=0.018). The biodistribution results indicated significant tumor uptake of 177Lu-DOTA-2P(FAPI) 2 even at 24 h post-injection ((4.30±0.52)%ID/g). The combination of 177Lu-DOTA-2P(FAPI) 2 and αPD-L1 achieved the 30-day survival rate of 100%, which was significantly superior to that of the control group (saline injection; χ2=9.53, P=0.002). Further mechanistic studies revealed that the combination therapy reprogramed the tumor microenvironment, enhanced anti-tumor intercellular communication, and activated signaling pathways such as Fas-FasL between T cells/natural killer (NK) cells and tumor cells, thereby synergistically inhibiting tumor progression. Conclusions:68Ga/ 177Lu-DOTA-2P(FAPI) 2 exhibits theranostic potential for MSS colorectal cancer. The combination of 177Lu-DOTA-2P(FAPI) 2 with ICIs may significantly prolong survival, demonstrating significant potential for clinical translation.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

This article reports a first case of combined infection with severe fever with thrombocytopenia syndrome(SFTS)and scrub typhus in Dalian City.The patient was admitted to the hospital due to recurrent fever for 7 days and loss of consciousness for 1 day.Pathogen metagenomic sequencing(mNGS),SFTSV quantitative PCR,and enzyme-linked immunosorbent assay(ELISA)IgM tests were performed,showing positive results for Orientia tsutsugamushi and SFTSV nucleic acids.Based on clinical manifestations and epidemiological history,the patient was diagnosed with combined infections.

Objective:To describe the distribution characteristics of alcohol consumption in adult twins in the Chinese National Twin Registry (CNTR), and further explore the influence of genetic factors on alcohol consumption in adult twins.Methods:The subjects of the study were twins registered by CNTR in 11 project areas across China from 2010 to 2018. A total of 56 966 twins (28 483 pairs) aged 18 years and above who answered questions about drinking behavior were included, and the random effect model was used to describe the population and regional distribution characteristics of alcohol consumption. Intra-pair analysis was performed to calculate the concordance rate and heritability of their alcohol consumption.Results:The age of all subjects was (36.6±12.0) years, and current drinkers accounted for 16.6% (9 461/56 966) of all subjects. In men, those aged 50-59 years, those in northern China, those living in rural area, those with low education level and those with high BMI, the proportions of current drinkers were higher. After excluding 468 pairs of twins who had stopped alcohol use and 21 764 pairs of twins who had no drink or had small amount drink, an intra-pair analysis was conducted in 4 929 pairs of same-sex twins, and found that the concordance rate of alcohol consumption was 64.0% (2 059/3 215) in monozygotic twins, and 52.6% (902/1 714) in dizygotic twins, the difference was significant ( P<0.001), and the heritability of alcohol consumption was 24.1% (95% CI: 18.9%- 29.3%). The further stratified analysis found that in southern men, the heritability was highest in those aged 40-49 years (36.1%, 95% CI: 21.6%-50.7%), while in northern men, the heritability was highest in those aged 50-59 years (34.2%, 95% CI: 18.1%-50.3%). Conclusions:In adult twins in China, there were population and regional differences in the distribution of alcohol consumption behavior, and alcohol consumption was influenced by genetic factors, and gender, age and region had potential modifying effects.

Objective To design an intelligent airborne soldier physical training system based on human body composition analysis to solve the problems in diversity of training mode,targeted training plan and high incidence of military training-related injuries.Methods The intelligent airborne military physical training system was designed with B/S architecture and developed with Python language,which was composed of four functional modules for airborne soldier information acquisition,trainee physical fitness state assessment,physical fitness training program recommendation and airborne soldier physical fitness training program evaluation.The airborne soldier information acquisition module collected and analyzed the trainee physiological parameter information with a human body composition analyzer,clarified the parameter characteristics related to physical training with considerations on military physical training requirements and constructed a trainee physical fitness assessment parameter model;the trainee physical fitness state assessment module established an evaluation model based on machine learning to realize stage-by-stage physical fitness evaluation for airborne soldiers;the physical fitness training program recommendation module was constructed based on the physical training feature similarity algorithm and graph embedding theory to provide decision making assistance for program development of airborne military physical training;the airborne soldier physical fitness training program evaluation module compared the physical fitness and evaluation results before and after training by means of list and chart,and updated the training program based on the evaluation results by calling the physical training program recommendation module.Results The intelligent airborne soldier physical training system contributed to forming an individualized physical fitness training recommendation mechanism after trainee body evaluation,modifying training program based on comparison and feedback for stage-by-stage training evaluation,so as to decrease the incidence of military training-related injuries while increasing the training efficiency.Conclusion The system developed improves airborne soldier physical training in rationality and reliability,and provides references for intelligent military training of the PLA.[Chinese Medical Equipment Journal,2025,46(2):16-23]

Objective:To explore the relationship between obesity indicators and DNA methylation clocks acceleration,and to analyze their temporal sequence.Methods:Data were obtained from two sur-veys conducted in 2013 and 2017-2018 by the Chinese National Twin Registry.Peripheral blood DNA methylation data were measured using the Illumina Infinium Human Methylation 450K BeadChip and EPIC BeadChip.DNA methylation clocks/acceleration metrics(GrimAA,PCGrimAA and Dunedin-PACE)were calculated using the DNA methylation online tool(https://dnamage.genetics.ucla.edu/)or R code provided by researchers.Obesity indicators included weight,body mass index(BMI),waist circumference,waist-hip ratio,and waist-height ratio.A total of 1 070 twin individuals were included in the cross-sectional analysis,comprising 378 monozygotic(MZ)twin pairs and 155 dizygotic(DZ)twin pairs for within-pair analysis.Mixed-effects models were used to examine the associations between obesity indicators and DNA methylation clocks,as well as their acceleration measures.The longitudinal analysis included 314 twin individuals,comprising 95 MZ twin pairs and 62 DZ twin pairs for within-pair analy-sis.Cross-lagged panel models were applied to further explore the temporal relationships between obesity and DNA methylation clock indicators.All analyses were conducted both in the full twin sample and separately within MZ and DZ twin pairs.Results:In the cross-sectional analysis population,monozygotic twins accounted for 71.0％,males for 68.0％,and the mean chronological age was(49.9±12.1)years.In the longitudinal analysis population,monozygotic twins accounted for 60.5％,males for 60.8％,with a mean baseline chronological age of(50.4±10.2)years and a mean follow-up duration of(4.6±0.6)years.Except for the waist-to-hip ratio,which was significantly higher at follow-up com-pared with baseline,no statistically significant differences were observed in the means of other obesity in-dicators between baseline and follow-up.Correlation analysis revealed that weight,BMI,waist circumfe-rence,waist-hip ratio(WHR),and waist-height ratio(WHtR)were positively correlated with Dunedin-PACE in all the twins,with WHtR showing the strongest association(β=0.21,95％CI:0.11 to 0.31).Weight and BMI were negatively associated with GrimAA(β=-0.03,95％CI:-0.05 to-0.01;β=-0.07,95％CI:-0.12 to-0.02),while weight was negatively associated with PCGrim-AA(β=-0.02,95％CI:-0.03 to 0.00).However,within-twin-pair analyses showed no statistically significant correlations.Cross-lagged panel model analysis indicated that higher baseline weight might lead to increased GrimAA at follow-up,while elevated baseline weight,BMI,and waist circumference might increase PCGrimAA.Higher baseline WHR was associated with increased DunedinPACE at follow-up.Conclusion:Obesity indicators correlate with DNA methylation clock acceleration metrics.Baseline obesity may influence changes in certain DNA methylation clock indicators over time,suggesting that obesity could exert long-term health effects by accelerating DNA methylation aging.However,these associations may be confounded by shared genetic or environmental factors among the twins.

Objective:To explore the effectiveness of digital management platform led by outpatient specialist nurses among patients with type 2 diabetes in outpatient settings.Methods:From March 2024 to March 2025, adult patients with type 2 diabetes who visited the Endocrinology Clinic at Peking Union Medical College Hospital were selected using convenience sampling. Patients were randomly assigned to the control group and intervention group using the random number table method. Patients in control group received routine education on type 2 diabetes during their clinic visits after enrollment. Intervention group was managed by nurses through a digital management platform, establishing long-term connections with patients. Based on the platform, nurses regularly provided patients with knowledge updates, promptly responded to patient inquiries, reviewed daily dietary records, monitored blood glucose data, conducted weekly telephone follow-ups, and scheduled regular clinic visits to precisely intervene in patients' lifestyles. The glycated hemoglobin (HbA1c) and self-management behavior scores were compared between two groups of patients before and after three months of intervention.Results:A total of 46 patients were enrolled and completed the 3-month follow-up, including 25 in control group and 21 in intervention group. There were no statistically significant differences in HbA1c or self-management behavior scores between the two groups of patients before intervention ( P>0.05). At three months of intervention, the HbA1c reduction in intervention group was greater than that in control group, with a statistically significant difference ( P<0.05). There was no statistically significant difference in Summary of Diabetes Self-Care Activities Scale scores between the two groups ( P>0.05). The difference in changes in diabetic foot self-screening scores between intervention group and control group was statistically significant ( P<0.05) . Conclusions:Specialist nurse-led precision health care management utilizing digital platforms can improve HbA1c and enhance self-management behaviors for diabetic foot in patients with type 2 diabetes, which is expected to be promoted and applied in the outpatient management of diabetes patients.

Objective:To explore the effectiveness of digital management platform led by outpatient specialist nurses among patients with type 2 diabetes in outpatient settings.Methods:From March 2024 to March 2025, adult patients with type 2 diabetes who visited the Endocrinology Clinic at Peking Union Medical College Hospital were selected using convenience sampling. Patients were randomly assigned to the control group and intervention group using the random number table method. Patients in control group received routine education on type 2 diabetes during their clinic visits after enrollment. Intervention group was managed by nurses through a digital management platform, establishing long-term connections with patients. Based on the platform, nurses regularly provided patients with knowledge updates, promptly responded to patient inquiries, reviewed daily dietary records, monitored blood glucose data, conducted weekly telephone follow-ups, and scheduled regular clinic visits to precisely intervene in patients' lifestyles. The glycated hemoglobin (HbA1c) and self-management behavior scores were compared between two groups of patients before and after three months of intervention.Results:A total of 46 patients were enrolled and completed the 3-month follow-up, including 25 in control group and 21 in intervention group. There were no statistically significant differences in HbA1c or self-management behavior scores between the two groups of patients before intervention ( P>0.05). At three months of intervention, the HbA1c reduction in intervention group was greater than that in control group, with a statistically significant difference ( P<0.05). There was no statistically significant difference in Summary of Diabetes Self-Care Activities Scale scores between the two groups ( P>0.05). The difference in changes in diabetic foot self-screening scores between intervention group and control group was statistically significant ( P<0.05) . Conclusions:Specialist nurse-led precision health care management utilizing digital platforms can improve HbA1c and enhance self-management behaviors for diabetic foot in patients with type 2 diabetes, which is expected to be promoted and applied in the outpatient management of diabetes patients.