Delineation Errors Caused by Replication and Expansion Operations in Monaco.

BACKGROUND: This study aims to investigate the errors in structure volume and shape caused by the replication, expansion, and merging operations of the Monaco system and analyze their influence on dosimetry evaluation. METHODS: A retrospective collection of 30 patients undergoing radiotherapy was utilized. Cylinders with radii of 5, 10, and 30 mm were delineated in computerized tomography (CT) images from 10 patients with thoracic and abdominal issues, and the Margins function in Monaco was used to expand the margins by 0, 3, 5, and 10 mm in 2D mode. In 10 patients with vertebral metastases, the Margins function was utilized to replicate and merge targets, and the Copy Structure function was employed to replicate targets. Cross-CT replication was performed for the targets of 10 patients with nasopharyngeal carcinoma. The deviation between the processed structure volume and the ideal value was compared. The difference in the maximum dose (Dmax) before and after lens replication was evaluated in 10 patients undergoing whole-brain radiotherapy. RESULTS: Monaco's Margins function increased the volume of the processed structure during the copying procedure. The margin error was equivalent to expanding the structure by 0.3-0.4 mm, and a margin error of 0.3-0.4 mm was produced in each expansion instance. The volume deviation for a cylinder with a radius of 5 mm was 12.99%. The Merge function of Margins copied substructures and merged them. The Copy Structure function did not alter the structure during copying, but the volume was reduced by less than 1% after copying across CT. Dmax after lens replication was higher than that before replication, with a median difference of 31.3 cGy for the left lenses. CONCLUSION: Monaco's Margins function introduces errors in organ replication, expansion, and merging, resulting in incorrect dose assessment. Physicians should be mindful of the potential effects when utilizing them.

Long-term exposure to polystyrene microplastics reduces macrophages and affects the microbiota-gut-brain axis in mice.

The remarkably increase in plastic use has led to worldwide pollution involving microplastics (MPs), which have been shown to be potentially hazardous substances. Although several studies have focused on the effects of small MPs on the brain and behavior of aquatic species, their effects on the mouse brain and the underlying mechanisms remain unclear. Our study's aim was to investigate the effects of long-term oral ingestion of different sizes of MPs (0.1, 5, and 50 µm) on mouse colon tissue. Of these sizes, the smallest (0.1 µm) had the greatest effect. Pre-administration of MP promotes colitis but reduces tumor growth in a colitis-associated colorectal cancer (CAC) mouse mode. MPs can increase inflammation in mice via activation of the very late antigen 4-vascular cell adhesion molecule 1 (VLA4-VCAM1) signaling pathway in macrophages, while also inducing macrophage reduction in the late phase of inflammation. In the microbiota-gut-brain axis, polystyrene MP treatment altered bile acid and carbohydrate metabolism in the intestine, inhibited intestinal motility, reduced water reabsorption, and led to a certain degree of depression in mice. These findings suggest that small MPs can induce macrophage reduction, thereby affecting the physical and mental health by modulating the microbiota-gut-brain axis.

Two 2D spin-crossover coordination polymers constructed by [Pd(SCN)4]2- building blocks.

Two new two-dimensional (2D) coordination polymers, [FeII(L)2{PdII(SCN)4}] (L1 = 2-methoxypyrazine, 1; and L2 = (E)-3-(phenyldiazenyl)pyridine, 2), were successfully constructed by using square-planar [Pd(SCN)4]2- building blocks. Complex 1 exhibits complete and one-step spin-crossover (SCO) behavior, while 2 exhibits incomplete and two-step SCO behavior. Further structural insight into this synergy reveals that the flat/flexing [Fe{Pd(SCN)4}]∞ sheets in 1 and 2 are stabilized by interlayered/intralayered supramolecular interactions.

Study on Shipboard Carbon Capture Technology with the MEA-AMP-Mixed Absorbent Based on Ship Applicability Perspective.

Ocean-going ships powered by diesel engines were the main source of CO2 emissions in the marine environment, and it was imperative to study shipboard carbon capture technology. Based on this, the whole process of CO2 absorption and desorption was studied with a mixed amine (MEA + AMP). The effective cycle time, CO2 absorption capacity, desorption efficiency, energy consumption, foaming and volatilization characteristics, and other key parameters were compared and analyzed under the conditions of required capture efficiency by ships. The MEA20% + AMP10% solution showed good CO2 absorption performance. At 80% capture efficiency, the desorption efficiency was more than 50%. The absorption capacity was close to that of the MEA30% solution, and the cycle time was basically the same. The effective cycle time under the reaction time of 2 s was also determined, which was an important parameter for setting the desorption frequency and had a great effect on reducing the desorption energy consumption. This work can provide an important reference value for the real ship application of carbon capture devices.

Synthesis of D-A-type groups modified aza-BODIPY fluorescent dye encapsulated by amphiphilic polypeptide nanoparticles for NIR-II phototheranostics.

An innovative organic small molecule with a D-A structure was synthesized by connecting triphenylamine to BODIPY via a thiophene bridge. Triphenylamine and thiophene units ingeniously modulate the balance between steric hindrance and π-π interactions around the flat aza-BODIPY core. The molecule exhibits near-infrared fluorescence absorption and emits at roughly 1100 nm, featuring a significant Stokes shift. Both the molecule and its nanoparticles demonstrate high stability and achieve a remarkable 35 % photothermal conversion efficiency when conjugated with the P(OEGMA)20-P(Asp)14 copolymer. In vitro assessments show low dark toxicity and outstanding biocompatibility. Moreover, in vivo studies and photothermal therapy in mice indicate substantial tumor shrinkage and reduced recurrence, confirming its potential in cancer treatment. These results highlight the promise of this organic molecule and its nanoparticles for NIR-II imaging-guided photothermal therapy, introducing a novel approach to phototheranostic applications for cancer management.

Predictors of mortality in severe pneumonia patients: a systematic review and meta-analysis.

BACKGROUND: Severe pneumonia has consistently been associated with high mortality. We sought to identify risk factors for the mortality of severe pneumonia to assist in reducing mortality for medical treatment. METHODS: Electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, and Scopus were systematically searched till June 1, 2023. All human research were incorporated into the analysis, regardless of language, publication date, or geographical location. To pool the estimate, a mixed-effect model was used. The Newcastle-Ottawa Scale (NOS) was employed for assessing the quality of included studies that were included in the analysis. RESULTS: In total, 22 studies with a total of 3655 severe pneumonia patients and 1107 cases (30.29%) of death were included in the current meta-analysis. Significant associations were found between age [5.76 years, 95% confidence interval [CI] (3.43, 8.09), P < 0.00001], male gender [odds ratio (OR) = 1.47, 95% CI (1.07, 2.02), P = 0.02], and risk of death from severe pneumonia. The comorbidity of neoplasm [OR = 3.37, 95% CI (1.07, 10.57), P = 0.04], besides the presence of complications such as diastolic hypotension [OR = 2.60, 95% CI (1.45, 4.67), P = 0.001], ALI/ARDS [OR = 3.63, 95% CI (1.78, 7.39), P = 0.0004], septic shock [OR = 9.43, 95% CI (4.39, 20.28), P < 0.00001], MOF [OR = 4.34, 95% CI (2.36, 7.95), P < 0.00001], acute kidney injury [OR = 2.45, 95% CI (1.14, 5.26), P = 0.02], and metabolic acidosis [OR = 5.88, 95% CI (1.51, 22.88), P = 0.01] were associated with significantly higher risk of death among patients with severe pneumonia. Those who died, compared with those who survived, differed on multiple biomarkers on admission including serum creatinine [Scr: + 67.77 mmol/L, 95% CI (47.21, 88.34), P < 0.00001], blood urea nitrogen [BUN: + 6.26 mmol/L, 95% CI (1.49, 11.03), P = 0.01], C-reactive protein [CRP: + 33.09 mg/L, 95% CI (3.01, 63.18), P = 0.03], leukopenia [OR = 2.63, 95% CI (1.34, 5.18), P = 0.005], sodium < 136 mEq/L [OR = 2.63, 95% CI (1.34, 5.18), P = 0.005], albumin [- 5.17 g/L, 95% CI (- 7.09, - 3.25), P < 0.00001], PaO2/FiO2 [- 55.05 mmHg, 95% CI (- 60.11, - 50.00), P < 0.00001], arterial blood PH [- 0.09, 95% CI (- 0.15, - 0.04), P = 0.0005], gram-negative microorganism [OR = 2.56, 95% CI (1.17, 5.62), P = 0.02], and multilobar or bilateral involvement [OR = 3.65, 95% CI (2.70, 4.93), P < 0.00001]. CONCLUSIONS: Older age and male gender might face a greater risk of death in severe pneumonia individuals. The mortality of severe pneumonia may also be significantly impacted by complications such diastolic hypotension, ALI/ARDS, septic shock, MOF, acute kidney injury, and metabolic acidosis, as well as the comorbidity of neoplasm, and laboratory indicators involving Scr, BUN, CRP, leukopenia, sodium, albumin, PaO2/FiO2, arterial blood PH, gram-negative microorganism, and multilobar or bilateral involvement. SYSTEMATIC REVIEW REGISTRATION: PROSPERO Protocol Number: CRD 42023430684.

Metal implant segmentation in CT images based on diffusion model.

BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

Efficient detection of driver fatigue state based on all-weather illumination scenarios.

Among the causes of the annually traffic accidents, driving fatigue is the main culprit. In consequence, it is of great practical significance to carry out the research of driving fatigue detection and early warning system. However, there are still two problems in the latest methods of driving fatigue detection: one is that a single information cannot precisely reflect the actual state of the driver in different fatigue phases, another one is the detection effect is not very well or even difficult to detect under abnormal illumination. In this paper, the multi-task cascaded convolutional networks (MTCNN) and infrared-based remote photo-plethysmography (rPPG) theory are used to extract the driver's facial and physiological information, and the multi-modal specific fatigue information is deeply excavated, and the multi-modal feature fusion model is constructed to comprehensively analyze the driver's fatigue variation tendency. Aiming at the matter of low detection accuracy under abnormal illumination, the multi-modal features extracted from visible light images and infrared images are fused by multi-loss reconstruction (MLR) module, and the driving fatigue detection module is established which is based on Bi-LSTM model by utilizing fatigue timing. The experiments were validated under all-weather illumination scenarios and were carried out on the datasets NTHU-DDD, UTA-RLDDD and FAHD. The results show that the multi-modal driving fatigue detection model has better performance than the single-modal model, and the accuracy is improved by 8.1%. In the abnormal illumination such as strong and weak light, the accuracy of the method can reach 91.7% at the highest and 83.6% at the lowest. Meanwhile, in the normal illumination, it can reach 93.2%.

Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification.

In this study, we propose a novel method for identifying lithology using an attention mechanism-enhanced graph convolutional neural network (AGCN). The aim of this method is to address the limitations of traditional approaches that evaluate unbalanced lithology by improving the identification of thin layers and small samples, while providing reliable data support for reservoir evaluation. To achieve this goal, we begin by using Principal Component Analysis (PCA) with maximum and minimum distance clustering (Max-min-distance) to correct the logging curves, which compensates for the low resolution of thin layers and enhances the accuracy of stratigraphic representation. Subsequently, we transform the logging data into graph-structured data by connecting distance similarity points and feature similarity points of the logging samples. We then use the graph convolutional network (GCN) to identify lithology, leveraging both labeled and unlabeled data to enhance the ability to identify lithology in small sample datasets. Additionally, our model incorporates a channel and spatial attention mechanism that assigns weights to the graph structure during lithology identification, improving the model's capability to discern differences across samples. To evaluate the performance of our model, we constructed a lithology dataset comprising five wells and conducted experiments. The results indicate that our approach achieves a maximum accuracy of 97.67%, surpassing the performance of a singlestructure model in lithology identification. In conclusion, our proposed method provides a promising and effective approach for unbalanced lithology identification, significantly improving accuracy levels.

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy.

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

Transdermal microarrayed electroporation for enhanced cancer immunotherapy based on DNA vaccination.

Despite the tremendous clinical potential of nucleic acid-based vaccines, their efficacy to induce therapeutic immune response has been limited by the lack of efficient local gene delivery techniques in the human body. In this study, we develop a hydrogel-based organic electronic device (µEPO) for both transdermal delivery of nucleic acids and in vivo microarrayed cell electroporation, which is specifically oriented toward one-step transfection of DNAs in subcutaneous antigen-presenting cells (APCs) for cancer immunotherapy. The µEPO device contains an array of microneedle-shaped electrodes with pre-encapsulated dry DNAs. Upon a pressurized contact with skin tissue, the electrodes are rehydrated, electrically triggered to release DNAs, and then electroporate nearby cells, which can achieve in vivo transfection of more than 50% of the cells in the epidermal and upper dermal layer. As a proof-of-concept, the µEPO technique is employed to facilitate transdermal delivery of neoantigen genes to activate antigen-specific immune response for enhanced cancer immunotherapy based on a DNA vaccination strategy. In an ovalbumin (OVA) cancer vaccine model, we show that high-efficiency transdermal transfection of APCs with OVA-DNAs induces robust cellular and humoral immune responses, including antigen presentation and generation of IFN-γ+ cytotoxic T lymphocytes with a more than 10-fold dose sparing over existing intramuscular injection (IM) approach, and effectively inhibits tumor growth in rodent animals.

Hierarchically Assembled Gigantic Fe/Co Cyanometallate Clusters Exhibiting Electron Transfer Behavior Above Room Temperature.

The construction of large and complex supramolecular architectures through self-assembly is at the forefront of contemporary coordination chemistry. Notwithstanding great success in various systems using anionic bridges (e.g., O2- or S2-) or organic ligands (e.g., pyridine or carboxylate ligands), the assembly of large cyanide-bridged clusters with increasing nuclearity remains a formidable synthetic challenge. In this study, it is achieved in preparing two heterometallic cyanometallate clusters with unprecedented complexity, [Fe20Co20] (1) and [Fe12Co15] (2), by creating the "flexibility" through a versatile ligand of bis((1H-imidazol-4-yl)methylene)hydrazine (H2L) and low-coordinate cobalt. Complex 1 features a super-square array of four cyanide-bridged [Fe4Co4] cube subunits as the corners that are interconnected by four additional [FeCo] units, resulting in a torus-shaped architecture. Complex 2 contains a lantern-like core-shell cluster with a triple-helix kernel of [Co3L3] enveloped by a [Fe12Co12] shell. The combined structure analysis and mass spectrometry study reveal a hierarchical assembly mechanism, which sheds new light on constructing cyanometallate nanoclusters with atomic precision. Moreover, complex 1 undergoes a thermally induced electron-transfer-coupled spin transition (ETCST) between the diamagnetic {FeII LS(µ-CN)CoIII LS} and paramagnetic {FeIII LS(µ-CN)CoII HS} configurations (LS = low spin, HS = high spin) above room temperature, representing the largest molecule displaying electron transfer and spin transition characteristic.

Iridium(III) complexes conjugated with naproxen exhibit potent anti-tumor activities by inducing mitochondrial damage, modulating inflammation, and enhancing immunity.

A series of Ir(III)-naproxen (NPX) conjugates with the molecular formula [Ir(C^N)2bpy(4-CH2ONPX-4'-CH2ONPX)](PF6) (Ir-NPX-1-3) were designed and synthesized, including C^N = 2-phenylpyridine (ppy, Ir-NPX-1), 2-(2-thienyl)pyridine (thpy, Ir-NPX-2) and 2-(2,4-difluorophenyl)pyridine (dfppy, Ir-NPX-3). Cytotoxicity tests showed that Ir-NPX-1-3 exhibited excellent antitumor activity, especially in A549R cells. The cellular uptake experiment showed that the complexes were mainly localized in mitochondria, and induced apoptosis in A549R cells by damaging the structure and function of mitochondria. The main manifestations are a decrease in the mitochondrial membrane potential (MMP), an increase in reactive oxygen species (ROS) levels, and cell cycle arrest. Furthermore, Ir-NPX-1-3 could inhibit the migration and colony formation of cancer cells, demonstrating potential anti-metastatic ability. Finally, the anti-inflammatory and immunological applications of Ir-NPX-1-3 were verified. The downregulation of cyclooxygenase-2 (COX-2) and programmed death-ligand 1 (PD-L1) expression levels and the release of immunogenic cell death (ICD) related signaling molecules such as damage-associated molecular patterns (DAMPs) (cell surface calreticulin (CRT), high mobility group box 1 (HMGB1), and adenosine triphosphate (ATP)) indicate that these Ir(III) -NPX conjugates are novel ICD inducers with synergistic effects in multiple anti-tumor pathways.

Predicting the risk of primary Sjögren's syndrome with key N7-methylguanosine-related genes: A novel XGBoost model.

Objectives: N7-methylguanosine (m7G) plays a crucial role in mRNA metabolism and other biological processes. However, its regulators' function in Primary Sjögren's Syndrome (PSS) remains enigmatic. Methods: We screened five key m7G-related genes across multiple datasets, leveraging statistical and machine learning computations. Based on these genes, we developed a prediction model employing the extreme gradient boosting decision tree (XGBoost) method to assess PSS risk. Immune infiltration in PSS samples was analyzed using the ssGSEA method, revealing the immune landscape of PSS patients. Results: The XGBoost model exhibited high accuracy, AUC, sensitivity, and specificity in both training, test sets and extra-test set. The decision curve confirmed its clinical utility. Our findings suggest that m7G methylation might contribute to PSS pathogenesis through immune modulation. Conclusions: m7G regulators play an important role in the development of PSS. Our study of m7G-realted genes may inform future immunotherapy strategies for PSS.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.

OBJECTIVES: Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS: A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS: The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS: Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Extracorporeal membrane oxygenation technology for adults: an evidence mapping based on systematic reviews.

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) is a cutting-edge life-support measure for patients with severe cardiac and pulmonary illnesses. Although there are several systematic reviews (SRs) about ECMO, it remains to be seen how quality they are and how efficacy and safe the information about ECMO they describe is in these SRs. Therefore, performing an overview of available SRs concerning ECMO is crucial. METHODS: We searched four electronic databases from inception to January 2023 to identify SRs with or without meta-analyses. The Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) tool, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system were used to assess the methodological quality, and evidence quality for SRs, respectively. A bubble plot was used to visually display clinical topics, literature size, number of SRs, evidence quality, and an overall estimate of efficacy. RESULTS: A total of 17 SRs met eligibility criteria, which were combined into 9 different clinical topics. The methodological quality of the included SRs in this mapping was "Critically low" to "Moderate". One of the SRs was high-quality evidence, three on moderate, three on low, and two on very low-quality evidence. The most prevalent study used to evaluate ECMO technology was observational or cohort study with frequently small sample sizes. ECMO has been proven beneficial for severe ARDS and ALI due to the H1N1 influenza infection. For ARDS, ALF or ACLF, and cardiac arrest were concluded to be probably beneficial. For dependent ARDS, ARF, ARF due to the H1N1 influenza pandemic, and cardiac arrest of cardiac origin came to an inconclusive conclusion. There was no evidence for a harmful association between ECMO and the range of clinical topics. CONCLUSIONS: There is limited available evidence for ECMO that large sample, multi-center, and multinational RCTs are needed. Most clinical topics are reported as beneficial or probably beneficial of SRs for ECMO. Evidence mapping is a valuable and reliable methodology to identify and present the existing evidence about therapeutic interventions.

Online quantification of nicotine in e-cigarette aerosols by vacuum ultraviolet photoionization mass spectrometry.

The growing popularity of e-cigarettes and the associated risks of nicotine addiction present a new challenge to global public health security. Measuring the nicotine levels in e-cigarette aerosols is essential to assess the safety of e-cigarettes. In this study, a rapid in situ method was developed for online quantification of nicotine in e-cigarette aerosols by using a homemade vacuum ultraviolet photoionization aerosol mass spectrometer (VUV-AMS). E-cigarette liquids with different nicotine concentrations were prepared to generate aerosols containing different levels of nicotine, which were employed as the calibration sources for nicotine quantification by VUV-AMS. The results showed that the mass concentration of nicotine in e-cigarette aerosols has a good linear relationship with its signal intensity in the mass spectrum, and the limits of detection and quantitation of nicotine by VUV-AMS were found to be 2.0 and 6.2 µg per puff respectively. Then the online method was utilized to measure five commercial e-cigarettes, and their nicotine yields were determined to be between 31 and 188 µg per puff with the nicotine fluxes from 7.7 to 70 µg s-1, agreeing with the results of the gas chromatography with a flame ionization detector (GC-FID). This study demonstrated the feasibility and advantages of VUV-AMS for quick quantification of nicotine in e-cigarette aerosols within seconds.

mTOR signaling pathway regulation HIF-1 α effects on LPS induced intestinal mucosal epithelial model damage.

BACKGROUND: Sepsis-induced small-intestinal injury is associated with increased morbidity and mortality. Our previous study and other papers have shown that HIF-1α has a protective effect on intestinal mucosal injury in septic rats. The purpose of this study is to further verify the protective effect of HIF-1α on intestinal mucosa and its molecular mechanism in vitro experiments. METHODS: Caco-2 cells were selected and experiment was divided into 2 parts. Part I: HIF-1α activator and inhibitor were used to treat lipopolysacchrides (LPS)-stimulated Caco-2 cells respectively, to explore the effect of HIF-1α on LPS induced Caco-2 cell epithelial model; Part II: mTOR activator or inhibitor combined with or without HIF-1α activator, inhibitor to treat LPS-stimulated Caco-2 cells respectively, and then the molecular mechanism of HIF-1α reducing LPS induced Caco-2 cell epithelial model damage was detected. RESULTS: The results showed that HIF-1α activator decreased the permeability and up regulated tight junction (TJ) expression, while HIF-1α inhibitor had the opposite effect with the HIF-1α activator. mTOR activation increased, while mTOR inhibition decreased HIF-1α protein and expression of its downstream target molecules, which can be attenuated by HIF-1α activator or inhibitor. CONCLUSION: This study once again confirmed that HIF-1α alleviates LPS-induced mucosal epithelial model damage through P70S6K signalling pathway. It is of great value to explore whether HIF-2α plays crucial roles in the regulation of mucosal epithelial model functions in the future.

[Research on Position Verification of Multi-Leaf Collimator (MLC) and Dose Verification Based on Electronic Portal Imaging Device].

Objective: A quality control (QC) system based on the electronic portal imaging device (EPID) system was used to realize the Multi-Leaf Collimator (MLC) position verification and dose verification functions on Primus and VenusX accelerators. Methods: The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation. The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation. The dose data obtained by EPID and two-dimensional matrix (MapCheck/PTW) were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis. Results: The position error of VenusX MLC was less than 1 mm. The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID. For the dose reconstructed by EPID, the average γ passing rates of Primus were 98.86% and 91.39% under the criteria of 3%/3 mm, 10% threshold and 2%/2 mm, 10% threshold, respectively. The average γ passing rates of VenusX were 98.49% and 91.11%, respectively. Conclusion: The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.