In In recent years, small-molecule targeted protein degraders inducing protein degradation have been developing rapidly. These molecules are attracting substantial interest from researchers since they can overcome such limitations of traditional small-molecule inhibitors as their inapplicability to ‘undruggable’ targets and tendency to induce drug resistance. Compared with other targeted protein degraders, small-molecule hydrophobic tags (HyTs) may have a smaller number of hydrogen bond donors/acceptors, smaller molecular weights, and better pharmacokinetic profiles, thus attracting extensive attention from researchers. This review focuses on the possible mechanisms and popular types of HyTs, with special attention to the potential application value of adamantane, a typical hydrophobic tag, in the fields of cancer and neurodegeneration. In general, there are still some problems like fewer types of hydrophobic tags and insufficient research on degradation mechanisms, which still need to be further explored. This review is expected to provide researchers working in the fields of small-molecule targeted protein degraders with some valuable reference.

Perceptual decision making refers to the process by which individuals make choices and judgments based on sensory information, serving as a fundamental ability for human adaptation to complex environments. While traditional research has focused on perceptual decision making in isolated contexts, growing evidence highlights the profound influence of social contexts prevalent in real-world scenarios. As a crucial factor supporting individual survival and development, social context not only provides rich information sources but also shapes perceptual decision making through top-down processing mechanisms, prompting researchers to recognize the inherently social nature of human decisions. Empirical studies have demonstrated that social information, such as others’ choices or group norms, can systematically bias individuals’ perceptual decisions, often manifesting as conformity behaviors. Social influence can also facilitate performance under certain conditions, particularly when individuals can accurately identify and adopt high-quality social information. The impact of social context on perceptual decisions is modulated by a variety of external and internal factors, including group characteristics(e.g., group size, response consistency), attributes of peers (e.g., familiarity, social status, distinctions between human and artificial agents), as well as individual differences such as confidence, personality traits, and developmental stage. The motivations driving social influence encompass three primary mechanisms: improving decision accuracy through informational influence, gaining social acceptance through normative influence, and maintaining positive self-concept. Recent computational approaches have employed diverse theoretical frameworks to provide valuable insights into the cognitive mechanisms underlying social influence in perceptual decision making. Reinforcement learning models demonstrate how social feedback shapes future choices through reward-based updating. Bayesian inference frameworks describe how individuals integrate personal beliefs with social information based on their respective reliabilities, dynamically updating beliefs to optimize decisions under uncertainty. Drift diffusion models offer powerful tools to decompose social influence into distinct cognitive components, allowing researchers to differentiate between changes in perceptual processing and shifts in decision criteria. Collectively, these models establish a comprehensive methodological foundation for disentangling the multiple pathways by which social context shapes perceptual decisions. Neuroimaging and electrophysiological studies provide converging evidence that social context influences perceptual decision making through multi-level neural mechanisms. At early perceptual processing stages, social influence modulates sensory evidence accumulation in parietal cortex and directly alters primary visual cortex activity, while guiding selective attention to stimulus features consistent with social norms through attentional alignment mechanisms. At higher cognitive levels, the reward system (ventral striatum, ventromedial prefrontal cortex) is activated during group-consistent decisions; emotion-processing networks (anterior cingulate cortex, insula, amygdala) regulate experiences of social acceptance and rejection; and mentalizing-related brain regions (dorsomedial prefrontal cortex, temporoparietal junction) support inference of others’ mental states and social information integration. These neural circuits work synergistically to achieve top-down multi-level modulation of perceptual decision making. Understanding the mechanisms by which social context shapes perceptual decision making has broad theoretical and practical implications. These insights inform the optimization of collective decision-making, the design of socially adaptive human-computer interaction systems, and interventions for cognitive disorders such as autism spectrum disorder and anorexia nervosa. Future studies should combine computational modeling and neuroimaging approaches to systematically investigate the multi-level and dynamic nature of social influences on perceptual decision making.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Acute ST-segment elevation myocardial infarction with multivessel disease is one of the high-risk types of coronary heart disease.Early opening of infarct-related artery and reperfusion of myocardium could significantly reduce the mortality in acute phase.However,the presence of non-culprit lesions in non-infarct-related arteries is still at risk and has an important impact on the long-term prognosis of patients.It remains controversial on how to precisely evaluate the clinical significance and revascularization value of non-culprit lesions.This article aims to review the research status and progress of guidance strategies of non-culprit lesion revascularization in patients with ST-segment elevation myocardial infarction and multivessel disease.

Objective To compare the cost-utility of tislelizumab and sorafenib in the first-line treatment of advanced unresectable hepatocellular carcinoma,and to provide a reference for the selection of treatment regimens from the perspective of pharmacoeconomics.Methods A partitioned survival model was used to simulate the survival status of patients using tislelizumab or sorafenib within 10 years,and the cost and health output were calculated respectively to obtain the incremental cost-utility ratio(ICUR).The 3 times China's per capita gross domestic product(GDP)in 2022 was taken as the threshold for willingness to pay(WTP).Results During the simulation period,the ICER of tislelizumab versus sorafenib was 280 691.4 yuan/quality-adjusted life year(QALY),which was significantly higher than that of the sorafenib group,which had obvious economic performance.Univariate sensitivity analysis showed that the incidence of grade 3 or above adverse reactions in the tislelizumab group,the cost of tislelizumab,and the incidence of grade 3 or higher adverse reactions in the sorafenib group were important factors affecting ICUR.Probabilistic sensitivity analysis showed that tislelizumab had a significant cost-utility advantage when WTP was 3 times GDP,with an economic probability of 81.4％,and the results were robust.Conclusion For the first-line treatment of advanced unresectable hepatocellular carcinoma,tislelizumab has a significant cost-utility advantage over sorafenib.

Objective To establish ecological suitability zone of Forsythia suspensa(Thunb.)Vahl in Shanxi Province;To study the quality regionalization of Forsythia suspensa(Thunb.)Vahl from different producing areas in Shanxi Province;To provide reference for reasonable planting and wild tending of Forsythia suspensa(Thunb.)Vahl.Methods Maximum entropy(MaxEnt)model and ArcGIS software were used to study the ecological suitability of Forsythia suspensa(Thunb.)Vahl in Shanxi Province;By screening the main environmental factors and combining them with the content of forsythoside and forsythoside A in Forsythia suspensa(Thunb.)Vahl of different regions,a quality zoning of Forsythia suspensa Thunb.Vahl medicinal materials in Shanxi Province based on forsythoside,forsythoside A and environmental factors was constructed.Results The ecological suitable areas of Forsythia suspensa Thunb.Vahl in Shanxi Province were mainly distributed in the southern part of Shanxi Province,mainly in Linfen,Yuncheng,Changzhi,and Jincheng.The general contents of forsythoside and forsythoside A in the Forsythia suspensa(Thunb.)Vahl medicinal material were gradually reduced from southern part to northern part of Shanxi Province.The comprehensive quality was high in southern part of Shanxi Province,mainly in Linfen,Changzhi,Yuncheng and Jincheng.Conclusion The results of this study are consistent with the actual survey.The southern part of Shanxi province is a suitable planting area for high quality Forsythia suspensa(Thunb.)Vahl,which provides a reference for the standardized planting and wild tending of Forsythia suspensa(Thunb.)Vahl.

Objective:To analyze the epidemiological characteristics and economic burden of palmoplantar pustulosis (PPP) in China.Methods:A population-based retrospective study was conducted using the data from China′s Urban Basic Medical Insurance data from January 1, 2012, to December 31, 2016. International Classification of Diseases code and diagnoses in Chinese for PPP were used to identify cases and estimate the prevalence, incidence, and cost. Subgroup analyses were performed according to age and sex, and sensitivity analyses were conducted to evaluate the robustness of the results. Age-adjusted prevalence rates were calculated based on the 2010 national census data.Results:The crude prevalence and incidence rate of PPP in 2016 were 2.730/100 000 (95% CI: 2.218/100 000-3.242/100 000) and 1.556/100 000 (95% CI: 1.154/100 000-1.958/100 000), and the prevalence rate of females (2.910/100 000) was higher than that of males (2.490/100 000, χ2=97.48, P=0.001). The incidence rate of females (1.745/100 000) was also higher than that of males (1.418/100 000, χ2=85.02, P=0.001). The age peak of incidence and prevalence of patients with PPP was in the 30-39-year age group and a small peak existed in the 0-3-year age group among people under 20 years old. From 2012 to 2016, the average number of visits was (2.44±0.04) per patient, and the total per-capita cost per year was (982.40±39.19) yuan. Conclusion:In 2016, the prevalence and incidence rate of PPP in China were higher in females than in males, and the highest age peak was in the 30-39-year age group.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.