Long-term causal effects estimation via latent surrogates representation learning.

Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications such as marketing and medicine. Most existing methods estimate causal effects in an idealistic and simplistic manner - disregarding unobserved surrogates and treating all short-term outcomes as surrogates. However, such methods are not well-suited to real-world scenarios where the partially observed surrogates are mixed with the proxies of unobserved surrogates among short-term outcomes. To address this issue, we develop our flexible method called LASER to estimate long-term causal effects in a more realistic situation where the surrogates are either observed or have observed proxies. In LASER, we employ an identifiable variational autoencoder to learn the latent surrogate representation by using all the surrogate candidates without the need to distinguish observed surrogates or proxies of unobserved surrogates. With the learned representation, we further devise a theoretically guaranteed and unbiased estimation of long-term causal effects. Extensive experimental results on the real-world and semi-synthetic datasets demonstrate the effectiveness of our proposed method.

Cholinergic hyperintensity pathways are associated with cognitive performance in patients with asymptomatic carotid artery stenosis.

OBJECTIVES: We aimed to determine whether asymptomatic carotid artery stenosis (ACS) induced cognitive impairments were related to the cholinergic hyperintensity pathway. METHODS: This cross-sectional study included patients with moderate-to-severe ACS, who were categorized into mild cognitive impairment (MCI) and normal cognition groups on the basis of Montreal Cognitive Assessment (MoCA) scores. The cholinergic pathway hyperintensity scale (CHIPS), Fazekas, and medial temporal atrophy (MTA) scores were assessed. SPSS software was used for statistical analyses. RESULTS: A total of 117 ACS patients (70.89 ± 8.81 years) and 105 controls (67.87 ± 9.49 years) were evaluated (t = 2.46, p = 0.015). The ACS group showed a worse median Mini-Mental Status Examination (MMSE) score (z = -2.41, p = 0.016) and MoCA score (z = -3.51, p < 0.001), and a significantly higher median total CHIPS score (z = 4.88, p < 0.001) and mean Fazekas score (t = 2.39, p = 0.018). In the correlation analysis, the MoCA score showed a significant negative correlation with the CHIPS score (ρ = -0.41, p < 0.001) and Fazekas score (ρ = -0.31, p < 0.001) in ACS group. Logistic regression analyses suggested that CHIPS scores were risk factors for MCI in patients with ACS (odds ratio [OR] = 1.07, 95% Confidence Interval [CI]1.01-1.13 and controls (OR = 1.09, 95%CI 1.01-1.17), while the MTA and Fazekas scores showed no predictive power. The receiver operating characteristic curve showed that the area under the curve of the CHIPS score for predicting MCI was 0.71 in ACS group, but was only 0.57 in controls. CONCLUSIONS: Patients with ACS showed poorer cognitive performance and higher CHIPS and Fazekas scores. CHIPS, but not Fazekas, scores were risk factors for cognitive impairment and were a valuable factor to predict MCI in patients with ACS.

Improved empirical mode decomposition method based on amplitude-frequency characteristic of vortex signals.

The vortex flowmeter occupies a vital position in flow measurement with its unique advantages. It is essentially a fluid vibration instrument, and its measurement process is susceptible to interference, which seriously affects measurement accuracy. In particular, at low flow rates, it is an urgent problem to extract vortex signals from the complex noise. Among many signal processing methods, Empirical Mode Decomposition (EMD) is a time-frequency analysis method suitable for nonlinear, non-stationary signals. EMD can adaptively decompose noisy signals into noise and useful signal components arranged from high frequency to low frequency. For the above problems, an innovative, improved EMD method is proposed in this paper. The digital filter is designed according to the amplitude-frequency characteristic of vortex signals. After filtering, the vortex signal is adjusted to a fixed value, and high-frequency noise is filtered. According to the consistency of the filtered signal's amplitude, we design a decomposition stop criterion for EMD to process the output signal of the vortex sensor. This method not only maintains the characteristic of adaptive decomposition in EMD but also completes the automatic extraction of the vortex signal under complex noise. It provides a new comprehensive method for realizing high-precision and anti-interference vortex flowmeters.

Metformin enhances METTL14-Mediated m6A methylation to alleviate NIT-1 cells apoptosis induced by hydrogen peroxide.

Injuries to pancreatic ß-cells are intricately linked to the onset of diabetes mellitus (DM). Metformin (Met), one of the most widely prescribed medications for diabetes and metabolic disorders, has been extensively studied for its antioxidant, anti-aging, anti-glycation, and hepatoprotective activities. N6-methyladenosine (m6A) plays a crucial role in the regulation of ß-cell growth and development, and its dysregulation is associated with metabolic disorders. This study aimed to elucidate the mechanistic basis of m6A involvement in the protective effects of Met against oxidative damage in pancreatic ß-cells. Hydrogen peroxide (H2O2) was employed to induce ß-cell damage. Remarkably, Met treatment effectively increased methylation levels and the expression of the methyltransferase METTL14, subsequently reducing H2O2-induced apoptosis. Knocking down METTL14 expression using siRNA significantly compromised cell viability. Conversely, targeted overexpression of METTL14 specifically in ß-cells substantially enhanced their capacity to withstand H2O2-induced stress. Molecular evidence suggests that the anti-apoptotic properties of Met may be mediated through Bcl-xL and Bim proteins. In conclusion, our findings indicate that Met induces METTL14-mediated alterations in m6A methylation levels, thereby shielding ß-cells from apoptosis and oxidative damage induced by oxidative stress.

Increased serum extrachromosomal circular DNA SORBS1circle level is associated with insulin resistance in patients with newly diagnosed type 2 diabetes mellitus.

BACKGROUND: Extrachromosomal circular DNAs (eccDNAs) exist in human blood and somatic cells, and are essential for oncogene plasticity and drug resistance. However, the presence and impact of eccDNAs in type 2 diabetes mellitus (T2DM) remains inadequately understood. METHODS: We purified and sequenced the serum eccDNAs obtained from newly diagnosed T2DM patients and normal control (NC) subjects using Circle-sequencing. We validated the level of a novel circulating eccDNA named sorbin and SH3-domain- containing-1circle97206791-97208025 (SORBS1circle) in 106 newly diagnosed T2DM patients. The relationship between eccDNA SORBS1circle and clinical data was analyzed. Furthermore, we explored the source and expression level of eccDNA SORBS1circle in the high glucose and palmitate (HG/PA)-induced hepatocyte (HepG2 cell) insulin resistance model. RESULTS: A total of 22,543 and 19,195 eccDNAs were found in serum samples obtained from newly diagnosed T2DM patients and NC subjects, respectively. The T2DM patients had a greater distribution of eccDNA on chromosomes 1, 14, 16, 17, 18, 19, 20 and X. Additionally, 598 serum eccDNAs were found to be upregulated, while 856 eccDNAs were downregulated in T2DM patients compared with NC subjects. KEGG analysis demonstrated that the genes carried by eccDNAs were mainly associated with insulin resistance. Moreover, it was validated that the eccDNA SORBS1circle was significantly increased in serum of newly diagnosed T2DM patients (106 T2DM patients vs. 40 NC subjects). The serum eccDNA SORBS1circle content was positively correlated with the levels of glycosylated hemoglobin A1C (HbA1C) and homeostasis model assessment of insulin resistance (HOMA-IR) in T2DM patients. Intracellular eccDNA SORBS1circle expression was significantly enhanced in the high glucose and palmitate (HG/PA)-induced hepatocyte (HepG2 cell) insulin resistance model. Moreover, the upregulation of eccDNA SORBS1circle in the HG/PA-treated HepG2 cells was dependent on generation of apoptotic DNA fragmentation. CONCLUSIONS: These results provide a preliminary understanding of the circulating eccDNA patterns at the early stage of T2DM and suggest that eccDNA SORBS1circle may be involved in the development of insulin resistance.

Effectiveness and Safety of Chinese Herbal Injections Combined with SOX Chemotherapy Regimens for Advanced Gastric Cancer: a Bayesian Network Meta-Analysis.

Background: Randomized controlled trials (RCTs) have demonstrated that combining Chinese herbal injections (CHIs) with oxaliplatin plus tegafur (SOX) chemotherapy regimens improves clinical effectiveness and reduces adverse reactions in patients with advanced gastric cancer (AGC). These RCTs highlight the potential applications of CHIs and their impact on AGC patient prognosis. However, there is insufficient comparative evidence on the clinical effectiveness and safety of different CHIs when combined with SOX. Therefore, we performed a network meta-analysis to rank the clinical effectiveness and safety of different CHIs when combined with SOX chemotherapy regimens. This study aimed to provide evidence for selecting appropriate CHIs in the treatment of patients with AGC. Methods: We searched eight databases from their inception until March 2023. Surface Under the Cumulative Ranking Curve (SUCRA) probability values were used to rank the treatment measures, and the Confidence in Network Meta-Analysis (CINeMA) software assessed the grading of evidence. Results: A total of 51 RCTs involving 3,703 AGC patients were identified. Huachansu injections + SOX demonstrated the highest clinical effectiveness (SUCRA: 78.17%), significantly reducing the incidence of leukopenia (93.35%), thrombocytopenia (80.19%), and nausea and vomiting (95.15%). Shenfu injections + SOX improved Karnofsky's Performance Status (75.59%) and showed a significant reduction in peripheral neurotoxicity incidence (88.26%). Aidi injections + SOX were most effective in reducing the incidence of liver function damage (75.16%). According to CINeMA, most confidence rating results were classified as "low". Conclusion: The combination of CHIs and SOX shows promising effects in the treatment of AGC compared to SOX alone. Huachansu and Shenfu injections offer the greatest overall advantage among the CHIs, while Aidi injections are optimal for reducing the incidence of liver damage. However, further rigorous RCTs with larger sample sizes and additional pharmacological studies are necessary to reinforce these findings.

Hemodynamic assessment for intracranial atherosclerosis from angiographic images: a clinical validation study.

BACKGROUND: Intracranial atherosclerotic stenosis (ICAS) is one of the leading causes of ischemic stroke. Conventional anatomical analysis by CT angiography, MRI, or digital subtraction angiography can provide valuable information on the anatomical changes of stenosis; however, they are not sufficient to accurately evaluate the hemodynamic severity of ICAS. The goal of this study was to assess the diagnostic performance of the pressure ratio across intracranial stenoses (termed as fractional flow (FF)) derived from cerebral angiography for the diagnosis of hemodynamically significant ICAS defined by pressure wire-derived FF. METHODS: This retrospective study represents a feasible and reliable method for calculating the FF from cerebral angiography (AccuFFicas). Patients (n=121) who had undergone wire-based measurement of FF and cerebral angiography were recruited. The accuracy of the computed pressure ratio was evaluated using wire-based FF as the reference standard. RESULTS: The mean value of wire-based FF was 0.78±0.19, while the computed AccuFFicas had an average value of 0.79±0.18. Good correlation (Pearson's correlation coefficient r=0.92, P<0.001) between AccuFFicas and FF was observed. Bland-Altman analysis showed that the mean difference between AccuFFicas and FF was -0.01±0.07, indicating good agreement. The area under the curve (AUC) of AccuFFicas in predicting FF≤0.70, FF≤0.75, and FF≤0.80 was 0.984, 0.986, and 0.962, respectively. CONCLUSION: Angiography-based FF computed from cerebral angiographic images could be an effective computational tool for evaluating the hemodynamic significance of ICAS.

Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort.

OBJECTIVES: The Alberta Stroke Program Early CT Score (ASPECTS) is a semi-quantitative method to evaluate the severity of early ischemic change on non-contrast computed tomography (NCCT) in patients with acute ischemic stroke (AIS). In this work, we propose an automated ASPECTS method based on large cohort of data and machine learning. METHODS: For this study, we collected 3626 NCCT cases from multiple centers and annotated directly on this dataset by neurologists. Based on image analysis and machine learning methods, we constructed a two-stage machine learning model. The validity and reliability of this automated ASPECTS method were tested on an independent external validation set of 300 cases. Statistical analyses on the total ASPECTS, dichotomized ASPECTS, and region-level ASPECTS were presented. RESULTS: On an independent external validation set of 300 cases, for the total ASPECTS results, the intraclass correlation coefficient between automated ASPECTS and expert-rated was 0.842. The agreement between ASPECTS threshold of ≥ 6 versus < 6 using a dichotomized method was moderate (κ = 0.438, 0.391-0.477), and the detection rate (sensitivity) was 86.5% for patients with ASPECTS threshold of ≥ 6. Compared with the results of previous studies, our method achieved a slight lead in sensitivity (67.8%) and AUC (0.845), with comparable accuracy (78.9%) and specificity (81.2%). CONCLUSION: The proposed automated ASPECTS method driven by a large cohort of NCCT images performed equally well compared with expert-rated ASPECTS. This work further demonstrates the validity and reliability of automated ASPECTS evaluation method. CLINICAL RELEVANCE STATEMENT: The automated ASPECTS method proposed by this study may help AIS patients to receive rapid intervention, but should not be used as a stand-alone diagnostic basis. KEY POINTS: NCCT-based manual ASPECTS scores were poorly consistent. Machine learning can automate the ASPECTS scoring process. Machine learning model design based on large cohort data can effectively improve the consistency of ASPECTS scores.

An integral blood-brain barrier in adulthood relies on microglia-derived PDGFB.

Pericyte is an indispensable cellular constituent of blood-brain barrier (BBB) and its homeostasis heavily rely on PDGFB-PDGFRß signaling. However, the primary cellular sources of PDGFB in the central nervous system (CNS) are unclear. Microglia is not considered a component of BBB and its role in maintaining BBB integrity in steady state is controversial. In this study, by analyzing transcriptomic data and performing in situ hybridization, we revealed a transition of the primary central PDGFB producers from endothelial cells in newborns to microglia in adults. Acute loss of microglial PDGFB profoundly impaired BBB integrity in adult but not newborn mice, and thus, adult mice deficient of microglial PDGFB could not survive from a sublethal endotoxin challenge due to rampant microhemorrhages in the CNS. In contrast, acute abrogation of endothelial PDGFB had minimal effects on the BBB of adult mice but led to a severe impairment of CNS vasculature in the neonates. Moreover, we found that microglia would respond to a variety of BBB insults by upregulating PDGFB expression. These findings underscore the physiological importance of the microglia-derived PDGFB to the BBB integrity of adult mice both in steady state and under injury.

Lambda-free light chain: A serum marker of dengue disease via NS3 protease-mediated antibody cleavage.

Dengue poses a significant global public health threat, with diverse clinical manifestations due to complex interactions between the host and the pathogen. Recent reports have highlighted elevated serum-free light chain (FLC) levels in viral infectious diseases. Hence, our study aimed to investigate serum FLC levels in dengue patients. The findings revealed elevated serum λ FLCs, which were associated with the severity of dengue. Receiver operating characteristic curve (ROC) analysis demonstrated that λ FLCs may serve as a serum marker for identifying dengue disease (AUC: 0.7825, sensitivity: 80, specificity: 71.43) and classifying severe dengue (AUC: 0.8102, sensitivity: 75, specificity: 79.52). The viral protease, Dengue virus (DENV) nonstructural protein 3 (NS3), acts as a protease that cleaves viral polyproteins as well as host substrates. Therefore, we proposed that antibodies might be potential targets of NS3 protease, leading to an increase in FLCs. LC/MS-MS analysis confirmed that λ FLCs were the predominant products after antibody degradation by NS3 protease. Additionally, purified NS3 protease cleaved both human IgG and DENV2-neutralizing antibodies, resulting in the presence of λ FLCs. Moreover, NS3 protease administration in vitro led to a reduction in the neutralizing efficacy of DENV2-neutralizing antibodies. In summary, the elevated serum λ FLC levels effectively differentiate dengue patients from healthy individuals and identify severe dengue. Furthermore, the elevation of serum λ FLCs is, at least in part, mediated through NS3 protease-mediated antibody cleavage. These findings provide new insights for developing diagnostic tools and understanding the pathogenesis of DENV infection.

Antibody Profiling of Dengue Severities Using Flavivirus Protein Microarrays.

Dengue is a viral disease transmitted by Aedes aegypti mosquitoes. According to the World Health Organization, about half of the world's population is at risk of dengue. There are four serotypes of the dengue virus. After infection with one serotype, it will be immune to such a serotype. However, subsequent infection with other serotypes will increase the risk of severe outcomes, e.g., dengue hemorrhagic fever, dengue shock syndrome, and even death. Since severe dengue is challenging to predict and lacks molecular markers, we aim to build a multiplexed Flavivirus protein microarray (Flaviarray) that includes all of the common Flaviviruses to profile the humoral immunity and cross-reactivity in the dengue patients with different outcomes. The Flaviarrays we fabricated contained 17 Flavivirus antigens with high reproducibility (R-square = 0.96) and low detection limits (172-214 pg). We collected serums from healthy subjects (n = 36) and dengue patients within 7 days after symptom onset (mild dengue (n = 21), hospitalized nonsevere dengue (n = 29), and severe dengue (n = 36)). After profiling the serum antibodies using Flaviarrays, we found that patients with severe dengue showed higher IgG levels against multiple Flavivirus antigens. With logistic regression, we found groups of markers with high performance in distinguishing dengue patients from healthy controls as well as hospitalized from mild cases (AUC > 0.9). We further reported some single markers that were suitable to separate dengue patients from healthy controls (AUC > 0.9) and hospitalized from mild outcomes (AUC > 0.8). Together, Flaviarray is a valuable tool to profile antibody specificities, uncover novel markers for decision-making, and shed some light on early preventions and treatments.

An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion.

Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system's classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.

Computed tomography perfusion software pipelines to assess parameter maps and ischemic volumes: A comparative study.

BACKGROUND AND PURPOSE: This study was dedicated to investigating the agreement of the calculated results of two CT perfusion (CTP) postprocessing software packages, including parameter maps and ischemic volume, focusing on the infarct core volume (ICV) and penumbra volume (PV). METHODS: A retrospective collection of 235 patients with acute ischemic stroke who underwent CTP examination were enrolled. All images had been analyzed with two software pipelines, RAPID CTP and AccuCTP, and the comparative analysis was based on ICV and PV results calculated by both software packages. The agreement of parameter maps was evaluated by root mean square error and Bland-Altman analysis. The ICV and PV agreement was evaluated by intraclass correlation coefficient (ICC) and Bland-Altman analysis. The accuracy of ICV and PV based on multiple thresholds was also analyzed. RESULTS: The ICV and PV of AccuCTP and RAPID CTP show excellent agreement. The relative differences of the parameter maps were all within 10% and the Bland-Altman analysis also showed a strong agreement. From ordinary least squares fitting results, both ICV and PV had a remarkably high goodness of fit (ICV, R2 = 0.975 [p<.001]; PV, R2 = 0.964 [p<.001]). For the ICC analysis, both had high ICC scores (ICV ICC 0.984, 95% CI [confidence interval] 0.973-0.989; PV ICC 0.955, 95% CI 0.947-0.964). Furthermore, multi-threshold analysis on the basis of ICV and PV also achieved reliable analytical accuracy. CONCLUSIONS: The image analysis results of AccuCTP are in excellent agreement with RAPID CTP and can be used as an alternative analysis tool to RAPID CTP software in stroke clinical practice.

[Soil Bacterial Community Structure and Function Prediction of Millet/Peanut Intercropping Farmland in the Lower Yellow River].

The objective of this study was to explore the microecological variability in farmland soil fertility in response to millet-peanut intercropping patterns by clarifying the effects of millet-peanut 4:4 intercropping on soil bacterial community structure and its diversity, as well as to provide a reference basis for promoting ecological restoration and arable land quality improvement in the lower Yellow River farmland. The Illumina MiSeq high-throughput sequencing technology and QIIME 2 platform were used to analyze the differences in bacterial community composition and their influencing factors in five soils[sole millet (SM), sole peanut (SP), intercropping millet (IM), intercropping peanut (IP), and millet-peanut intercropping (MP)] and to predict their ecological functions. The results showed that the α-diversity of intercropping soil bacterial communities differed from that of monocropping, though not significantly, whereas the ß-diversity was significantly different (P<0.05). A total of 7081 ASVs were obtained from all soil samples, classified into 34 phyla, 109 orders, 256 class, 396 families, 710 genera, and 1409 species, of which 727 ASVs were shared, accounting for 24.5% to 27.8% in five soil species. The bacterial communities of millet-peanut intercropping and its monocropping soils were similar in phylum composition but varied in relative abundance. All five soils were dominated by the Actinobacteria, Proteobacteria, Acidobacteria, and Chloroflexi, with a relative abundance of 79.40%-81.33%. Soil organic carbon and alkaline nitrogen were the most important factors causing differences in the structures of the five soil bacterial communities at the phylum and genus levels, respectively. The PICRUSt functional prediction revealed that the relative abundance of primary functional metabolism was the largest (78.9%-79.3%), and the relative abundance of secondary functional exogenous biodegradation and metabolism fluctuated the most (CV=3.782%). In terms of the BugBase phenotype, the relative abundance of oxidative stress-tolerant bacteria increased in intercropping millet or peanut soils compared to that in the corresponding monocultures and significantly increased in intercropping millet soils compared to that in sole millet (P<0.05). Oxidative stress-tolerant, Gram-positive, and aerobic phenotypes were highly significantly positively correlated with each other (P<0.01), and all three showed highly significant negative correlations with potential pathogenicity and Gram-negative and anaerobic phenotypes (P<0.01). This showed that millet-peanut intercropping resulted in differences in soil bacterial community diversity, abundance, and metabolic functions and the possibility of reducing the occurrence of potential soil diseases. It can be used to regulate the soil microbiological environment to promote ecological restoration and sustainable development of farmland in the lower Yellow River.

Integration of AIEgens into covalent organic frameworks for pyroptosis and ferroptosis primed cancer immunotherapy.

Immunogenic programmed cell death, such as pyroptosis and ferroptosis, efficiently induces an acute inflammatory response and boosts antitumor immunity. However, the exploration of dual-inducers, particularly nonmetallic inducers, capable of triggering both pyroptosis and ferroptosis remains limited. Here we show the construction of a covalent organic framework (COF-919) from planar and twisted AIEgen-based motifs as a dual-inducer of pyroptosis and ferroptosis for efficient antitumor immunity. Mechanistic studies reveal that COF-919 displays stronger near-infrared light absorption, lower band energy, and longer lifetime to favor the generation of reactive oxygen species (ROS) and photothermal conversion, triggering pyroptosis. Because of its good ROS production capability, it upregulates intracellular lipid peroxidation, leading to glutathione depletion, low expression of glutathione peroxidase 4, and induction of ferroptosis. Additionally, the induction of pyroptosis and ferroptosis by COF-919 effectively inhibits tumor metastasis and recurrence, resulting in over 90% tumor growth inhibition and cure rates exceeding 80%.

Activation of Pyroptosis Using AIEgen-Based sp2 Carbon-Linked Covalent Organic Frameworks.

Covalent organic frameworks (COFs) have emerged as a promising class of crystalline porous materials for cancer phototherapy, due to their exceptional characteristics, including light absorption, biocompatibility, and photostability. However, the aggregation-caused quenching effect and apoptosis resistance often limit their therapeutic efficacy. Herein, we demonstrated for the first time that linking luminogens with aggregation-induced emission effect (AIEgens) into COF networks via vinyl linkages was an effective strategy to construct nonmetallic pyroptosis inducers for boosting antitumor immunity. Mechanistic investigations revealed that the formation of the vinyl linkage in the AIE COF endowed it with not only high brightness but also strong light absorption ability, long lifetime, and high quantum yield to favor the generation of reactive oxygen species for eliciting pyroptosis. In addition, the synergized system of the AIE COF and αPD-1 not only effectively eradicated primary and distant tumors but also inhibited tumor recurrence and metastasis in a bilateral 4T1 tumor model.

Identifying risk factors for in-stent restenosis in symptomatic intracranial atherosclerotic stenosis: a systematic review and meta-analysis.

Background: In-stent restenosis (ISR) is an adverse and notable event in the treatment of intracranial atherosclerotic stenosis (ICAS) with percutaneous transluminal angioplasty and stenting (PTAS). The incidence and contributing factors have not been fully defined. This study was performed to evaluate factors associated with ISR after PTAS. Data source: We identified studies on ISR after PTAS from an electronic search of articles in PubMed, Ovid MEDLINE, and the Cochrane Central Database (dated up to July 2022). Results: A total of 19 studies, including 452 cases of ISR after 2,047 PTAS, were included in the meta-analysis. The pooled incidence rate of in-stent restenosis was 22.08%. ISR was more likely to occur in patients with coronary artery disease (OR = 1.686; 95% CI: 1.242-2.288; p = 0.0008), dissection (OR = 6.293; 95% CI: 3.883-10.197; p < 0.0001), and higher residual stenosis (WMD = 3.227; 95% CI: 0.142-6.311; p = 0.0404). Patients treated with Wingspan stents had a significantly higher ISR rate than those treated with Enterprise stents (29.78% vs. 14.83%; p < 0.0001). Conclusions: The present study provides the current estimates of the robust effects of some risk factors for in-stent restenosis in intracranial atherosclerotic stenosis. The Enterprise stent had advantages compared with the Wingspan stent for ISR. The significant risk factors for ISR were coronary artery disease, dissection, and high residual stenosis. Local anesthesia was a suspected factor associated with ISR.

CSRP2 promotes cell stemness in head and neck squamous cell carcinoma.

BACKGROUND: Cysteine-rich protein 2 (CSRP2) is discovered as oncogene. The study aims to investigate the clinical significance and potential mechanism of CSRP2 in head and neck squamous cell carcinoma (HNSCC). METHODS: CSRP2 expression was explored by immunohistochemistry tissue microarrays and Western blotting in HNSCC. The effect of CSRP2 on the cancer stemness and epithelial-to-mesenchymal transition (EMT) of HNSCC cells was investigated by sphere formation, wound healing, and transwell assays. The vitro and vivo experiments revealed that CSRP2 modulated cancer stemness and EMT phenotypes in HNSCC. RESULTS: CSRP2 was overexpressed in HNSCC patients and presented poor prognosis. CSRP2 knockdown inhibited the migration and invasion ability of the HNSCC cells. And CSRP2 expression was closely associated with CSCs markers, EMT-transcription factor, new oncoprotein, and immune checkpoint. CONCLUSION: The overexpression of CSRP2 indicates poor prognosis and plays a key role in maintaining the cancer cell stemness and EMT.

Targeting PCSK9 reduces cancer cell stemness and enhances antitumor immunity in head and neck cancer.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) has been demonstrated to play a critical role in regulating cholesterol homeostasis and T cell antitumor immunity. However, the expression, function, and therapeutic value of PCSK9 in head and neck squamous cell carcinoma (HNSCC) remain largely unexplored. Here, we found that the expression of PCSK9 was upregulated in HNSCC tissues, and higher PCSK9 expression indicated poorer prognosis in HNSCC patients. We further found that pharmacological inhibition or siRNA downregulating PCSK9 expression suppressed the stemness-like phenotype of cancer cells in an LDLR-dependent manner. Moreover, PCSK9 inhibition enhanced the infiltration of CD8+ T cells and reduced the myeloid-derived suppressor cells (MDSCs) in a 4MOSC1 syngeneic tumor-bearing mouse model, and it also enhanced the antitumor effect of anti-PD-1 immune checkpoint blockade (ICB) therapy. Together, these results indicated that PCSK9, a traditional hypercholesterolemia target, may be a novel biomarker and therapeutic target to enhance ICB therapy in HNSCC.