Metabolic associated fatty liver disease （MAFLD） is a common chronic liver disease worldwide， and timely and precise intervention can delay disease progression and significantly reduce the risk of serious complications such as liver fibrosis， liver cirrhosis， and liver cancer. Although traditional liver biopsy combined with metabolic markers is the gold standard， it may cause complications such as pain and bleeding as an invasive examination， which has promoted scientific research to shift its focus to the construction of noninvasive assessment systems. In recent years， noninvasive diagnostic technologies based on multi-dimensional detection strategies have been continuously updated， including serological models， imaging techniques， and clinical algorithms. This article systematically reviews the screening methods for MAFLD during the fibrotic stages F1—F3， especially deep learning models based on artificial intelligence， in order to provide ideas for the early screening of MAFLD， as well as a scientific reference for optimizing disease management strategies.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

Objective:To analyze Hepatitis B virus(HBV)drug resistance mutations in patients with chronic hepatitis B(CHB)infection who have undergone long-term monotherapy with Entecavir(ETV)and those receiving combination therapy with ETV and Lamivudine(LAM), and to explore the related factors affecting HBV drug resistance mutations.Methods:The study retrospectively analyzed patients with CHB, compensated cirrhosis, decompensated cirrhosis, and liver cancer who received long-term nucleotide analogue antiviral therapy at the Fifth Medical Center of PLA General Hospital from August 2012 to August 2019.The patients were divided into an ETV monotherapy group and a combined LAM+ETV therapy group.Chi-square tests, independent sample t-tests, and Wilcoxon rank-sum tests were used to compare the clinical baseline characteristics and HBV drug resistance mutation features between the two therapy groups.A multivariate logistic regression model was used to analyze the factors related to HBV drug resistance mutations. Results:A total of 533 patients were enrolled in this study, 357 in the ETV monotherapy group and 176 in the LAM+ETV group. The ETV monotherapy group had 122 (34.17%) patients with resistance mutations, while the LAM+ETV group had 126 (71.59%).In general, the difference in gene mutation rate between the two therapy groups was statistically significant( χ2=66.337, P<0.001). The median age and alanine aminotransferase levels of patients with drug resistance mutations in the two therapy groups were higher than those in the non-mutation group[( t=-4.743, P<0.001)/( Z=-4.809, P<0.001), ( Z=-2.667, P=0.007)/( Z=-2.001, P=0.045)].Age( OR=1.044, 95% CI:1.023-1.066), compensated cirrhosis( OR=2.163, 95% CI:1.193-3.922), liver cancer( OR=4.017, 95% CI:2.170-7.436) and the treatment regimen( OR=6.075, 95% CI:3.889-9.489) were associated with drug resistance gene mutations( P<0.001).The mutation rates in different stages of chronic liver disease(CHB, cirrhosis, and liver cancer)showed statistically significant( χ2=41.038, P<0.001; χ2=15.894, P<0.001).The overall mutation rates of ETV-related genes in the two therapy groups were 25.49% and 32.39%, respectively.Additionally, 10 mutation sites and 38 variant combinations were identified, containing five common combinations being rtL180M, rtM204V, rtS202G;rtL180M, rtM204V, rtT184A; rtL180M, rtM204V, rtT184L;rtM204I and rtL180M, rtM204V. Conclusion:In CHB patients undergoing long-term therapy, the rate of HBV resistance mutations is higher in those receiving ETV and LAM combination therapy than in those receiving ETV monotherapy.Monitoring older patients and those with cirrhosis or liver cancer is especially important for preventing resistance mutations.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To study the effects of cognitive behavioral therapy(CBT)on nutritional status and postoperative recovery in patients after gastric cancer surgery.Methods Sixty patients diagnosed with gastric cancer between January 1,2023,and December 30,2023,at the hospital in the department of gastroin-testinal and gland surgery were included in this study.Patients were randomly divided by drawing lots into ei-ther the observation group(received routine care plus CBT—based nursing program)or the control group(received routine care),with 30 patients in each group.Nutritional indicators and postoperative recovery out-comes were compared between the two groups.Results After the intervention,patients in the observation group showed significantly higher levels of albumin and hemoglobin compared to the control group(P＜0.05).However,there was no statistically significant difference in prealbumin levels between the two groups(P＞0.05).Furthermore,patients in the observation group scored higher on the Quality of Recovery-40 scale in terms of emotional state,physical comfort,psychological support,and overall score compared to the control group(P＜0.05).After the intervention,the observation group showed significantly lower HADS scores com-pared to the control group,with a statistically significant difference(P＜0.05).Conclusion CBT demon-strates significant positive effects in improving nutritional status and postoperative recovery quality in gastric cancer patients.CBT improves patients'emotional state,thereby affecting appetite and nutritional status,and promotes postoperative physical function recovery.

Objective: Data on syncopal donation-related vasovagal reaction (DRVR) collected from 74 blood centers between 2020 and 2023 was statistically analyzed to provide a reference for developing preventive strategies against syncopal DRVR. Methods: Data on blood donation adverse reactions and basic information of donors from 2020 to 2023 were collected through the information management system at monitoring sentinel sites. Statistical analysis was performed on the following aspects of syncopal DRVR: characteristics of donors who experienced syncope, reported incidence, triggers, duration, presence and occurrence time of syncope-related trauma, clinical management including outpatient and inpatient treatment, and severity grading. Results: From 2020 to 2023, 45 966 donation-related adverse reactions were recorded. Of these, 1 665 (3.72%) cases were syncopal DRVR. The incidence of syncopal DRVR decreased with age, being the highest in the 18-22 age group. Incidence was significantly higher in female donors than male donors, in first-time donors than repeat donors, and in university and individual donors than group donors (all P<0.05). There was no statistically significant difference among different blood donation locations (P>0.05). The top three triggers were tension, fatigue, and needle phobia or fear of blood. Among syncopal DRVR cases, 60.36% occurred during blood collection, 87.63% lasted for less than 60 seconds, and 5.05% were accompanied by trauma. Notably, 57.14% of these traumas occurred after donor had left the blood collection site. Syncope severity was graded based on required treatment: grade 1 (fully recovered without treatment, 95.50%); grade 2 (recovered after outpatient treatment, 4.02%); and grade 3 (recovered after inpatient treatment, 0.48%). Conclusion: By analyzing the data of syncopal DRVR cases, it is possible to provide a reference for formulating blood donor safety policies.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.