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Abstract Background Skin cutaneous melanoma (SKCM) poses a significant public health challenge due to its aggressive nature and limited treatment options. To address this, the study introduces the Tumor Mutational Burden-Derived Immune lncRNA Prognostic Index (TILPI) as a potential prognostic tool for SKCM. Methods TILPI was developed using a combination of gene set variation analysis, differential expression analysis, and COX regression analysis. Additionally, functional experiments were conducted to validate the findings, focusing on the effects of STARD4-AS1 knockdown on SKCM tumor cell behavior. These experiments encompassed assessments of tumor cell proliferation, gene and protein expression, migration, invasion, and in vivo tumor growth. Results The results demonstrated that knockdown of STARD4-AS1 led to a significant reduction in tumor cell proliferation and impaired migration and invasion abilities. Moreover, it resulted in the downregulation of ADCY4, PRKACA, and SOX10 gene expression, as well as decreased protein expression of ADCY4, PRKACA, and SOX10. In vivo experiments further confirmed the efficacy of STARD4-AS1 knockdown in reducing tumor growth. Conclusions This study elucidates the mechanistic role of STARD4-AS1 and its downstream targets in SKCM progression, highlighting the importance of the ADCY4/PRKACA/SOX10 pathway. The integration of computational analysis with experimental validation enhances the understanding of TILPI and its clinical implications. Overall, the findings underscore the potential of novel computational frameworks like TILPI in predicting and managing SKCM, particularly through targeting the ADCY4/PRKACA/SOX10 pathway.

Abstract Epigenetic reprogramming plays a critical role in cancer progression of cancer, and N6-methyladenosine (m6A) is the most common RNA modification in eukaryotes. The purpose of this study was to explore the related modification mode of m6A regulator construction and evaluate the invasion and migration of thyroid cancer. Our results showed that m6A levels were significantly increased in papillary thyroid cancer (PTC) and anaplastic thyroid cancer (ATC) samples, which may have been induced by the down-regulation of demethylase fat mass and obesity-associated gene (FTO). Moreover, FTO inhibited PTC and ATC invasion and metastasis through the epithelial-to-mesenchymal transition (EMT) pathway in vivo and in vitro. Mechanistically, an m6A-mRNA epitranscriptomic microarray showed that Cadherin 12 (CDH12) is the key target gene mediated by FTO in an m6A-dependent manner. CDH12 promotes invasion and metastasis through the EMT pathway in thyroid cancer, both in vivo and in vitro. Furthermore, we found that insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is an important m6A reading protein, that regulates the stability of CDH12 mRNA and mediates EMT progression, thereby promoting the invasion and metastasis of PTC and ATC. Thus, FTO, IGF2BP2 and CDH12 may be effective therapeutic targets for PTC and ATC with significant invasion or distant metastasis. Schematic summary of FTO-IGF2BP2 axis in modulation of CDH12 mRNA m6A and upregulation of CDH12 expression in the invasion and metastasis of thyroid carcinoma.

Abstract Background Rosai-Dorfman disease (RDD), known as sinus histiocytosis with massive lymphadenopathy, commonly involves lymph nodes in the neck or mediastinum, although extranodal involvement is observed in approximately 40% of RDD patients. RDD involving only the thymus has rarely been reported. Here, we report a case of RDD originating in the thymus. The lesion was surgically removed, and a cure was finally achieved. There was no recurrence after telephone follow-up for 3 years. Case presentation A 52-year-old male was accidentally found to have a 7 × 6 cm anterior mediastinum lump by chest computed tomography (CT). The mediastinal lesion was resected by surgery, and postoperative pathology revealed RDD originating from the thymus. Regular telephone follow-up after surgery lasted 3 years and showed that the patient remained in good condition without any relevant symptoms. Conclusions RDD originating in the thymus cannot be characterized from CT images and is easily misdiagnosed as a traditional mediastinal tumor. This is mainly because there is so little disease in this area that physicians are not aware of it. We report this case with the hope that clinicians will have a better understanding of this disease. According to our follow-up results, surgery is an effective means of treatment.

Abstract Background Though our previous study has demonstrated that the single-incision plus one-port laparoscopic surgery (SILS + 1) is safe and feasible for sigmoid colon and upper rectal cancer and has better short-term outcomes compared with conventional laparoscopic surgery (CLS), the long-term outcomes of SILS + 1 remains uncertain and are needed to evaluated by an RCT. Methods Patients with clinical stage T1-4aN0-2M0 rectosigmoid cancer were enrolled. The participants were randomly assigned to either SILS + 1 (n = 99) or CLS (n = 99). The 3-year DFS, 5-year OS, and recurrence patterns were analyzed. Results Between April 2014 and July 2016, 198 patients were randomly assigned to either the SILS + 1 group (n = 99) or CLS group (n = 99). The median follow-up in the SILS + 1 group was 64.0 months and in CLS group was 65.0 months. The 3-year DFS was 87.8% (95% CI, 81.6–94.8%) in SILS + 1 group and 86.9% (95% CI, 81.3–94.5%) in CLS group (hazard ratio: 1.09 (95% CI, 0.48–2.47; P = 0.84)). The 5-year OS was 86.7% (95% CI,79.6–93.8%) in the SILS + 1 group and 80.5% (95% CI,72.5–88.5%) in the CLS group (hazard ratio: 1.53 (95% CI, 0.74–3.18; P = 0.25)). There were no significant differences in the recurrence patterns between the two groups. Conclusions We found no significant difference in 3-year DFS and 5-year OS of patients with sigmoid colon and upper rectal cancer treated with SILS + 1 vs. CLS. SILS + 1 is noninferior to CLS when performed by expert surgeons. Trial registration ClinicalTrials.gov: NCT02117557 (registered on 21/04/2014).

IntroductionThe relationship between intervertebral disc degeneration (IVDD) and osteoporosis (OP), diagnosed primarily using bone mineral density (BMD), remains unclear so far. The present study, therefore, aimed to investigate the potential relationship between osteoporosis and intervertebral disc degeneration using Mendelian randomization and genome-wide association analyses. Specifically, the impact of bone mineral density on the development of intervertebral disc degeneration was evaluated.Materials and methodsThe genome-wide association studies (GWAS) summary data of OP/BMDs and IVDD were collected from the FinnGen consortium, the GEFOS consortium, and MRC-IEU. The relationship between IVDD and OP was then explored using TSMR. The inverse-variance weighted (IVW) method was adopted as the primary effect estimate, and the reliability and stability of the results were validated using various methods, including MR-Egger, weighted median, simple mode, weighted mode, and MR-PRESSO.ResultsNo significant causal relationship was observed between OP and IVDD (IVW, P > 0.05) or between femoral neck BMD (FA-BMD) and IVDD when OP and FA-BMD were used as exposures. However, increased levels of total body BMD (TB-BMD) and lumbar spine BMD (LS-BMD) were revealed as significant risk factors for IVDD (TB-BMD: IVW, OR = 1.201, 95% CI: 1.123–1.284, P = 8.72 × 10−8; LS-BMD: IVW, OR = 1.179, 95% CI: 1.083–1.284, P = 1.43 × 10−4). Interestingly, both heel BMD (eBMD) and femur neck BMD (FN-BMD) exhibited potential causal relationships (eBMD: IVW, OR = 1.068, 95% CI: 1.008–1.131, P = 0.0248; FN-BMD, IVW, OR = 1.161, 95% CI: 1.041–1.295, P = 0.0074) with the risk of IVDD. The reverse MR analysis revealed no statistically causal impact of IVDD on OP and the level of BMD (P > 0.05).ConclusionOP and the level of FA-BMD were revealed to have no causal relationship with IVDD. The increased levels of TB-BMD and LS-BMD could promote the occurrence of IVDD. Both eBMD and FN-BMD have potential causal relationships with the risk of IVDD. No significant relationship exists between IVDD and the risk of OP. Further research is warranted to comprehensively comprehend the molecular mechanisms underlying the impact of OP and BMD on IVDD and vice versa.

Abstract Background Azygos vein aneurysms (AVAs) are extremely rare. The majority of patients have no obvious clinical symptoms, so they are found by physical examination or by chance. There is limited clinical treatment experience that can be referred to, and there are no clear guidelines or research evidence standardizing the surgical and interventional therapy. Here, we report a patient with idiopathic AVA whose three-dimensional reconstruction of the tumor was completed before surgery. On the basis of three-dimensional reconstruction, single-port thoracoscopic resection of the AVA was successfully completed and reported for the first time. The previously reported cases are summarized to provide guidance for the diagnosis and treatment of patients with AVAs. Case presentation A 56-year-old man was transferred to our hospital due to “dysphagia”. The diagnosis of AVA was made after enhanced computed tomography, gastroscopy, fiberoptic bronchoscopy, and three-dimensional reconstruction. Congenital weakness or degenerative changes causes the vein walls to be extremely thin that the AVA had the risk of ruptur. Furthermore, the patient had symptoms of dysphagia, he received single-port thoracoscopic surgery. After the operation, his dysphagia disappeared. The postoperative pathology confirmed hemangioma. The patient was discharged 3 days after surgery without any complications. Conclusions AVAs are rare. Preoperative three-dimensional reconstruction can greatly help surgeons clarify the disease diagnosis, formulate the surgical plan, avoid damage to the surrounding vital organs, and reduce intraoperative bleeding. Thoracoscopic surgery to remove AVAs is difficult and has a high risk of bleeding, while more minimally invasive single-port thoracoscopic surgery is also safe and effective for the treatment of AVAs.

Abstract Background The uniportal video-assisted thoracoscopic right upper lobectomy (UVATRUL), as a common procedure for thoracic surgeons, is difficult to manipulate and has some inherent challenges. To solve both of problems, we summarized a series of techniques as the three steps method and investigated its feasibility on the patients of right upper lung cancer. Methods Forty-eight patients with right upper lobe lung cancer who underwent the three steps method UVATRUL in our hospital from January 2020 to May 2022 were selected as the three steps method group. Forty-seven patients who underwent the traditional UVATRUL were selected as the traditional method group. The intraoperative condition and postoperative condition of the two groups were retrospectively analysed. Multiple linear regression analysis was carried out to analyze the relationship between positive results and surgical method. Results All patients had successfully completed their surgeries. There was no significant difference between the two groups in respect of intraoperative blood loss, rate of conversion, day one thoracic drainage volume, chest tube indwelling time, incidence of postoperative complications, number of lymph node, and postoperative hospital stay (P > 0.05). Operative time of the three steps method group was significantly shorter than the traditional method group (P

With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

The authors show that consideration of global backbone strain enables successful de novo design of larger αβ-proteins with five- and six- stranded β-sheets flanked by α-helices.

Abstract Background Intercostal artery pseudoaneurysm is rare and at the risk of rupture. The aetiology is always reported to be iatrogenic and traumatic injury. Embolisation is the most common therapeutic method. Here, we report a case of spontaneous intercostal artery pseudoaneurysm and cured by combining covered stent grafting and surgical management. Case presentation A 60-year-old man complained of acute right back pain for 5 h. Computed tomography showed right massive hemothorax and a giant mass with distinct feeding vessel originated from the thoracic aorta within the right hemithorax. Thoracocentesis was performed, and then a covered stent was positioned across the origin of the feeding vessel. The patient was diagnosed with intercostal artery pseudoaneurysm. Finally, we successfully resected the pseudoaneurysm and ligated the proximal part of the artery. Histologic examination have proved the diagnosis. The postoperative course was uneventful, and the patient was discharged on postoperative day 10. There is no recurrence reported during follow-up. Conclusions Spontaneous intercostal artery pseudoaneurysm is extremly rare. Delayed hemothorax due to rupture of the pseudoaneurysm may occur years after the formation. Early diagnosis is important and a combined treatment of endovascular intervention and surgical management is feasible, especially for the case of ruptured large tumour-like mass presentation of the pseudoaneurysm.

Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named location feature extraction establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The mAP results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network.

The widespread use of renewable energy resources requires more immediate and effective fire alarms as a preventive measure. The fire is usually weak in the initial stages, which is not conducive to detection and identification. This paper validates a solution to resolve that problem by a flame detection algorithm that is more sensitive to small flames. Based on Yolov3, the parallel convolution structure of Inception is used to obtain multi-size image information. In addition, the receptive field of the convolution kernel is increased with the dilated convolution so that each convolution output contains a range of information to avoid information omission of tiny flames. The model accuracy has improved by introducing a Feature Pyramid Network in the feature extraction stage that has enhanced the feature fusion capability of the model. At the same time, a flame detection database for early fire has been established, which contains more than 30 fire scenarios and is suitable for flame detection under various challenging scenes. Experiments validate the proposed method not only improves the performance of the original algorithm but are also advantageous in comparison with other state-of-the-art object detection networks, and its false positives rate reaches 1.2% in the test set.

Objectives: Sm proteins (SNRPB/D1/D2/D3/E/F/G), involved in pre-mRNA splicing, were previously reported in the tumorigenesis of several cancers. However, their specific role in lung adenocarcinoma (LUAD) remains obscure. Our study aims to feature abnormal expressions and mutations of genes for Sm proteins and assess their potential as therapeutic targets via integrated bioinformatics analysis.Methods: In this research, we explored the expression pattern and prognostic worth of genes for Sm proteins in LUAD across TCGA, GEO, UALCAN, Oncomine, Metascape, David 6.8, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via univariate and multivariate cox regression analysis. Meanwhile, their expression patterns were validated by RT-qPCR. Gene mutations and co-expression of genes for Sm proteins were analyzed by the cBioPortal database. The PPI network for Sm proteins in LUAD was visualized by the STRING and Cytoscape. The correlations between genes for Sm proteins and immune infiltration were analyzed by using the “GSVA” R package.Results: Sm proteins genes were found upregulated expression in both LUAD tissues and LUAD cell lines. Moreover, highly expressed mRNA levels for Sm proteins were strongly associated with short survival time in LUAD. Genes for Sm proteins were positively connected with the infiltration of Th2 cells, but negatively connected with the infiltration of mast cells, Th1 cells, and NK cells. Importantly, Cox regression analysis showed that high SNRPD1/E/F/G expression were independent risk factors for the overall survival of LUAD.Conclusion: Our study showed that SNRPD1/E/F/G could independently predict the prognostic outcome of LUAD and was correlated with immune infiltration. Also, this report laid the foundation for additional exploration on the potential treatment target’s role of SNRPD1/E/F/G in LUAD.

Locating the tiny insulator defect object with complex backgrounds in high-resolution aerial images is a challenging task. In this paper, we propose a novel method which cascades detection and segmentation networks to identify the defect from the global and local two levels: (1) The improved Faster R-CNN is carried out to capture both defects and insulators in the entire image. ResNeXt-101 is adopted as the feature extraction network so as to fully extract features, and Feature Pyramid Network (FPN) is built to enhance the ability of detecting small targets. In addition, the Online Hard Example Mining (OHEM) training strategy is applied to solve the imbalance problem of positive and negative samples. (2) All the detected insulators are extracted and fed into the improved U-Net network to futher inspect at pixel level, we utilize the pre-trained ResNeXt-50 as the encoder of U-Net, incorporate an attention module, Spatial and Channel Squeeze & Excitation Block (SCSE), into the decoding path to highlight the meaningful information. A hybrid loss which merges binary cross entropy (BCE) loss and dice coefficient loss is designed to train our network for figuring out the class imbalance issue. The missed detection can be greatly reduced with the combination of two modified network, which makes comprehensive use of the original map information and local information. On the test set of actual images, the insulator defect recognition precision and recall of the cascade network is 91.9% and 95.7%, exhibiting strong robustness and accuracy.

Background. Last two decades, many association studies on genetic variants and chronic obstructive pulmonary disease (COPD) risk have been published. But results from different studies are inconsistent. Therefore, we performed this article to systematically evaluate results from previous meta-analyses and genome-wide association studies (GWASs). Material and Methods. Firstly, we retrieved meta-analyses in PubMed, Embase, and China National Knowledge Infrastructure and GWASs in PubMed and GWAS catalog on or before April 7th, 2022. Then, data were extracted and screened. Finally, two main methods—Venice criteria and false-positive report probability test—were used to evaluate significant associations. Results. As a result, eighty-eight meta-analyses and 5 GWASs were deemed eligible for inclusion. Fifty variants in 26 genes obtained from meta-analyses were significantly associated with COPD risk. Cumulative epidemiological evidence of an association was graded as strong for 10 variants in 8 genes (GSTM1, CHRNA, ADAM33, SP-D, TNF-α, VDBP, HMOX1, and HHIP), moderate for 6 variants in 5 genes (PI, GSTM1, ADAM33, TNF-α, and VDBP), and weak for 40 variants in 23 genes. Five variants in 4 genes showed convincing evidence of no association with COPD risk in meta-analyses. Additionally, 29 SNPs identified in GWASs were proved to be noteworthy based on the FPRP test. Conclusion. In summary, more than half (52.38%) of genetic variants reported in previous meta-analyses showed no association with COPD risk. However, 13 variants in 9 genes had moderate to strong evidence for an association. This article can serve as a useful reference for further studies.

An advanced Neuro-space mapping (Neuro-SM) multiphysics parametric modeling approach for microwave passive components is proposed in this paper. The electromagnetic (EM) domain model, which represents the EM responses with respect to geometrical parameters, is regarded as a coarse model. The multiphysics domain model, which represents the multiphysics responses with respect to both geometrical parameters and multiphysics parameters, is regarded as a fine model. The proposed model is constructed by the input mapping, the output mapping and the coarse model. The input mapping is used to map multiphysics parameters to EM parameters. The output mapping is introduced to further narrow the gap between the output of the coarse model and the multiphysics data. In addition, a three-stage training method is proposed for efficiently developing the proposed multiphysics model. The proposed technique, which combines the efficiency of EM analysis and the accuracy of multiphysics analysis, can achieve better accuracy with less multiphysics data than existing modeling methods. The developed Neuro-SM multiphysics model provides accurate and fast predictions of multiphysics responses. Therefore, the design cycle of microwave passive components is shortened while the modeling cost is significantly reduced. Two microwave filter examples are utilized to demonstrate the accuracy of the proposed parametric modeling technique.

BackgroundsComputed tomography (CT) appearance pattern after lung tumor stereotactic body radiation therapy(SBRT) might predicts survival. This study aimed to investigate the correlation between CT appearance pattern after SBRT and outcomes in patients with early-stage non-small-cell lung cancer (NSCLC).MethodsClinical data of inoperable patients with early-stage NSCLC undergoing SBRT were retrospectively analyzed from 2012 to 2015 at the Zhejiang Cancer Hospital. The relationship between CT appearance pattern after SBRT and patient’s survival was analyzed.ResultsThe data from 173 patients with early-stage lung cancer treated with SBRT were analyzed. One month after SBRT, diffuse consolidation was seen in 17 patients, patchy consolidation in 28 patients, diffuse ground-glass opacity (GGO) in 10 patients, and patchy GGO in 22 patients. The survival time was significantly longer in the “no evidence of increased density” group compared with the “consolidation or GGO” group [2-year overall survival (OS) rate, 96.1% vs 89.3%; hazard ratio (HR), 0.36; 95% confidence interval (CI), 0.16–0.85; P = 0.015]. A similar trend was found in the progression-free survival (PFS) analysis (2-year PFS rate, 91.3% vs 85.0%; HR, 0.35; 95% CI, 0.13–0.95; P = 0.015) and distant metastasis free survival(DMFS) (2-year DMFS rate, 93.3% vs 87.1%; HR, 0.41; 95% CI, 0.20–0.86; P = 0.031). However, no significant difference was found in recurrence-free survival between the two groups (P = 0.212).ConclusionsOne month after SBRT, the radiological change “no evidence of increased density” was prevalent. The OS, PFS, and DMFS were significantly longer in the “no evidence of increased density” group compared with the “consolidation or GGO” group. Further studies are needed to validate these findings.

The cathode buffer layer (CBL) plays a crucial role in organic solar cells (OSCs), and it has been challenging to obtain high-quality CBL by using simple and reliable processes. In this paper, the bilayer structure consisting of ZnO nanoparticles (NPs) and sol–gel SnO2 was prepared by the low-temperature (70BM. The results show that the insertion of SnO2 can effectively fill the cracks and pores on the surface of the ZnO NP film, thereby improving the overall compactness and flatness of the CBL and reducing the defect density inside the CBL. Furthermore, the insertion of SnO2 slightly improves the transmittance of the CBL to photons with wavelengths in the range of 400–600 nm, and also increases the electron mobility of the CBL thus facilitating the extraction and transport of the electrons. Compared to the devices using UVO-ZnO and UVO-SnO2 CBLs, the devices with UVO-ZnO/SnO2 CBL exhibit exceptional performance advantages, the best power conversion efficiency (PCE) reaches 10.56%. More importantly, the stability of the devices with ZnO/SnO2 CBL is significantly improved, the device (PCE) still maintains 60% of the initial value after 30 days in air. The positive results show that the UVO-ZnO/SnO2 is an ideal CBL for OSCs, and due to the low-temperature process, it has great application potential in flexible OSCs.

Video smoke detection is a promising method for early fire prevention. However, it is still a challenging task for application of video smoke detection in real-world detection systems, as the limitations of smoke image samples for training and lack of efficient detection algorithm. This paper proposes a method based on two state-of-the-art fast detectors, a single-shot multi-box detector, and a multi-scale deep convolutional neural network, for smoke detection using synthetic smoke image samples. The virtual data can automatically offer rich samples with ground truth annotations. However, the learning of smoke representation in the detectors will be restricted by the appearance gap between real and synthetic smoke samples, which will cause a significant performance drop. To train a strong detector with synthetic smoke samples, we incorporate the domain adaptation into the fast detectors. A series of branches with the same structure as the detection branches are integrated into the fast detectors for domain adaptation. We design an adversarial training strategy to optimize the model of the adapted detectors, to learn a domain-invariant representation for smoke detection. The domain discrimination, domain confusion, and detection are combined in the iterative training procedure. The performance of the proposed approach surpasses the original baseline in our experiments.

Automated model generation (AMG) is an automated artificial neural network (ANN) modeling algorithm, which integrates all the subtasks (including adaptive sampling/data generation, model structure adaptation, training, and testing) in neural model development into one unified framework. In existing AMG, most of the time is spent on data sampling and model structure adaptation due to the iterative neural network training and the sequential computation mechanism. In this paper, we propose an advanced AMG algorithm using parallel computation and interpolation approaches to speed up the neural modeling of microwave devices. Efficient interpolation approaches are incorporated to avoid repetitive training of the intermediate neural networks during adaptive sampling process in AMG. Parallel computation formulation based on a multi-processor environment is proposed to further save time during interpolation calculation, data generation, and model structure adaptation process. Examples of automated modeling of two microwave filters are presented to show the advantage of this paper.

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

We report on a theoretical study of the electronic structures of freestanding nanowires made from narrow band gap semiconductors GaSb, InSb and InAs. The nanowires are described by the eight-band k.p Hamiltonians and the band structures are computed by means of the finite element method in a mixture basis consisting of linear triangular elements inside the nanowires and constrained Hermite triangular elements near the boundaries. The nanowires with two crystallographic orientations, namely the [001] and [111] orientations, and with different cross-sectional shapes are considered. For each orientation, the nanowires of the three narrow band gap semiconductors are found to show qualitatively similar characteristics in the band structures. However, the nanowires oriented along the two different crystallographic directions are found to show different characteristics in the valence bands. In particular, it is found that all the conduction bands show simple, good parabolic dispersions in both the [001]- and [111]-oriented nanowires, while the top valence bands show double-maximum structures in the [001]-oriented nanowires, but single-maximum structures in the [111]-oriented nanowires. The wave functions and spinor distributions of the band states in these nanowires are also calculated. It is found that significant mixtures of electron and hole states appear in the bands of these narrow band gap semiconductor nanowires. The wave functions exhibit very different distribution patterns in the nanowires oriented along the [001] direction and the nanowires oriented along the [111] direction. It is also shown that single-band effective mass theory could not reproduce all the band state wave functions presented in this work.

A high-quality NMR solution structure is presented for protein hMcl-1(171-327) which comprises residues 171-327 of the human anti-apoptotic protein Mcl-1 (hMcl-1). Since this construct contains the three Bcl-2 homology (BH) sequence motifs which participate in forming a binding site for inhibitors of hMcl-1, it is deemed to be crucial for structure-based design of novel anti-cancer drugs blocking the Mcl1 related anti-apoptotic pathway. While the coordinates of an NMR solution structure for a corresponding construct of the mouse homologue (mMcl-1) are publicly available, our structure is the first atomic resolution structure reported for the 'apo form' of the human protein. Comparison of the two structures reveals that hMcl-1(171-327) exhibits a somewhat wider ligand/inhibitor binding groove as well as a different charge distribution within the BH3 binding groove. These findings strongly suggest that the availability of the human structure is of critical importance to support future design of cancer drugs.

Breastfeeding is the most complete nutritional method of feeding infants, but several impediments affect the decision to breastfeed, including questions of drug safety for medications needed during lactation. Despite recent FDA guidance, few labels provide clear dosing advice during lactation. Physiologically based pharmacokinetic modeling (PBPK) is well suited to mechanistically explore pharmacokinetics and dosing paradigms to fill gaps in the absence of extensive clinical studies and complement existing real-world data. For lactation-focused PBPK (Lact-PBPK) models, information on system parameters (e.g., expression of drug transporters in mammary epithelial cells) is sparse. The breast cancer resistance protein (BCRP) is expressed on the apical side of mammary epithelial cells where it actively transports drugs/substrates into milk (reported milk: plasma ratios range from 2 to 20). A critical review of BCRP and its role in lactation was conducted. Longitudinal changes in BCRP mRNA expression have been identified in women with a maximum reached around 5 months postpartum. Limited data are available on the ontogeny of BCRP in infant intestine; however, data indicate lower BCRP abundance in infants compared to adults. Current status of incorporation of drug transporter information in Lact-PBPK models to predict active secretion of drugs into breast milk and consequential exposure of breast-fed infants is discussed. In addition, this review highlights novel clinical tools for evaluation of BCRP activity, namely a potential non-invasive BCRP biomarker (riboflavin) and liquid biopsy that could be used to quantitatively elucidate the role of this transporter without the need for administration of drugs and to inform Lact-PBPK models., (© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

Purpose: A physiologically based pharmacokinetic (PBPK) model for fedratinib was updated and revalidated to bridge a gap between the observed drug-drug interaction (DDI) of a single sub-efficacious dose in healthy participants and the potential DDI in patients with cancer at steady state. The study aimed to establish an appropriate dose for fedratinib in patients coadministered with dual CYP3A4 and CYP2C19 inhibitors, providing quantitative evidence to inform dosing guidance., Methods: The original minimal PBPK model was developed using Simcyp ® Simulator v17. The model was updated by substituting a single distribution rate (Q sac ) with 2 separate rates (CL in /CL out ) and transitioning to v20. Model parameter updates were further informed with 3 clinical studies, and 3 more studies served as independent validation data. The validated model was applied to simulate potential DDIs between fedratinib and a known dual inhibitor of CYP3A4 and CYP2C19 (fluconazole)., Results: Coadministration of fedratinib with fluconazole in patients was predicted to increase fedratinib exposure by < 2-fold in all simulated scenarios. For patients with cancer receiving the approved dose of fedratinib 400 mg once daily along with fluconazole 200 mg daily, the model predicted an approximate 50% increase in fedratinib exposure at steady state., Conclusions: The updated PBPK model improved description of the observed pharmacokinetics and predicted a low risk of clinically significant DDIs between fedratinib and fluconazole. The quantitative evidence serves as a primary foundation for providing dose guidance in clinical practice for the coadministration of fedratinib with dual CYP3A4 and CYP2C19 inhibitors., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.

Mavacamten is a potential inducer of cytochrome P450 (CYP) 3A4 and, as such, could reduce the exposure of the active components of oral contraceptives, ethinyl estradiol (EE) and norethindrone (NOR), where CYP3A4 is involved in metabolism. This study assessed if repeat doses of mavacamten led to a drug-drug interaction with EE and/or NOR. This was an open-label study in healthy women. In Period 1, participants received 35 mcg of EE and 1 mg of NOR. In Period 2, participants received oral loading doses of mavacamten 25 mg on Days 1-2, 15 mg/day on Days 3-17, and 35 mcg of EE and 1 mg of NOR on Day 15. Plasma concentrations of mavacamten, EE, and NOR were obtained before dosing and up to 72 hours after dosing. For EE only, a physiologically based pharmacokinetic model was used to simulate mavacamten-mediated CYP3A4 induction with EE for various CYP2C19 phenotypes. In total, 13 women were enrolled (mean age, 38.9 [standard deviation, 9.65] years). After mavacamten administration, modest increases in area under the concentration-time curves were observed for both EE and NOR. The maximum concentrations and half-lives for EE and NOR were not affected by coadministration with mavacamten. Criteria for bioequivalence were met or nearly met for EE and NOR exposure with geometric mean ratios between 0.8 and 1.25. All adverse events were mild. The physiologically based pharmacokinetic model predicted a less than 15% decrease in EE exposure across CYP2C19 phenotypes. Coadministration of mavacamten at a therapeutically relevant dose with EE and NOR did not decrease the exposure to either EE or NOR to a level that may lead to reduced effectiveness., (© 2023 Bristol Myers Squibb. The Journal of Clinical Pharmacology published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

Mavacamten is a first-in-class, oral, selective, allosteric, reversible cardiac myosin inhibitor approved by the US Food and Drug Administration for the treatment of adults with symptomatic New York Heart Association functional class II-III obstructive hypertrophic cardiomyopathy. Mavacamten is metabolized in the liver, predominantly via cytochrome P450 (CYP) enzymes CYP2C19 (74%), CYP3A4 (18%), and CYP2C9 (8%). A physiologically-based pharmacokinetic (PBPK) model was developed using Simcyp version 19 (Certara, Princeton, NJ). Following model verification, the PBPK model was used to explore the effects of strong CYP3A4 and CYP2C19 inducers, and strong, moderate, and weak CYP2C19 and CYP3A4 inhibitors on mavacamten pharmacokinetics (PK) in a healthy population, with the effect of CYP2C19 phenotype predicted for poor, intermediate, normal, and ultrarapid metabolizers. The PBPK model met the acceptance criteria for all verification simulations (> 80% of model-predicted PK parameters within 2-fold of those observed clinically). A weak induction effect was predicted when mavacamten was administered with a strong CYP3A4 inducer in poor metabolizers. Moderate reductions in mavacamten exposure were predicted with a strong CYP2C19/CYP3A4 inducer in all CYP2C19 phenotypes. Except for the effect of strong CYP2C19 inhibitors on ultrarapid metabolizers, steady-state area under plasma concentration-time curve and maximum plasma concentration values were weakly affected (< 2-fold) or not affected (< 1.25-fold), regardless of CYP2C19 phenotype. In conclusion, a fit-for-purpose PBPK model was developed and verified, which accurately predicted the available clinical data and was used to simulate the potential impact of CYP induction and inhibition on mavacamten PKs, stratified by CYP2C19 phenotype., (© 2023 Bristol Myers Squibb and The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

At present, chips urgently need breakthrough development in the power consumption and integration. The chip integrates billions or even tens of billions of electronic components, such as field effect transistor, diode and so on. Therefore, the research and development of new low-power electronic components with smaller size is an effective method to reduce chip power consumption and improve chip integration. In this paper, the ferroelectric field effect transistor (Fe-FET) based on two-dimensional heterostructures α -In 2 Se 3 /ZnSe is proposed. Based on the first principle, the program will analyze the stability and band structure of α -In 2 Se 3 /ZnSe under different stacking modes. In the heterojunction, the microphysical mechanism of ferroelectric polarization affecting the electronic structure is revealed from the aspects of charge transfer at the interface and the asymmetric surfaces with different work function. Combined with the non-equilibrium Green's function transport theory, the transport properties of Fe-FET based on their α -In 2 Se 3 /ZnSe will be studied. The application will provide sufficient theoretical support for research and development of the device based on α -In 2 Se 3 /ZnSe structure., (© 2023 IOP Publishing Ltd.)

The phase distribution model, proposed by Atkinson and Begg in 1990, has been widely used for predicting breastmilk-to-plasma drug concentration ratio. However, misrepresentations of the equations have been noted in recent publications. In this perspective, we revisit the derivation of the equations and provide an R/Shiny interface for the model with a view to helping scientists in this field acquire in-depth understanding of the theoretical background and implementation of the model., (© 2022 Bristol Myers Squibb. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Physiologically based pharmacokinetic (PBPK) modeling has a number of applications, including assessing drug−drug interactions (DDIs) in polymorphic populations, and should be iteratively refined as science progresses. The Simcyp Simulator is annually updated and version 21 included updates to hepatic and intestinal CYP2C19 enzyme abundance, including addition of intermediate and rapid metabolizer phenotypes and changes to the ultra-rapid metabolizer enzyme abundance, with implications for population clearance and DDI predictions. This work details verification of the updates with sensitive CYP2C19 substrates, omeprazole and lansoprazole, using available clinical data from literature. Multiple assessments were performed, including recovery of areas under the concentration-time curve (AUC) and Cmax from compiled datasets for each drug, recovery of victim DDI ratios with CYP2C19 and/or CYP3A4 inhibition and recovery of relative exposure between phenotypes. Simulated data were within respective acceptance criteria for >80% of omeprazole AUC values, >70% of lansoprazole AUC and Cmax, >60% of AUC and Cmax DDI ratios and >80% of exposure ratios between different phenotypes. Recovery of omeprazole Cmax was lower (>50−70% within 2-fold) and possibly attributed to the variety of formulations used in the clinical dataset. Overall, the results demonstrated that the updated data used to parameterize CYP2C19 phenotypes reasonably described the pharmacokinetics of omeprazole and lansoprazole in genotyped or phenotyped individuals.

Introduction : Physiological pH and chemical pKa are two sides of the same coin in defining the ionization of a drug in the human body. The Henderson-Hasselbalch equation and pH-partition hypothesis form the theoretical base to define the impact of pH-pKa crosstalk on drug ionization and thence its absorption, distribution, metabolism, excretion, and toxicity (ADMET). Areas covered : Human physiological pH is not constant, but a diverse, dynamic state regulated by various biological mechanisms, while the chemical pKa is generally a constant defining the acidic dissociation of the drug at various environmental pH. Works on pH-pKa crosstalk are scattered in the literature, despite its significant contributions to drug pharmacokinetics, pharmacodynamics, safety, and toxicity. In particular, its impacts on drug ADMET have not been effectively linked to the physiologically based pharmacokinetic (PBPK) modeling and simulation, a powerful tool increasingly used in model-informed drug development (MIDD). Expert opinion: Lacking a full consideration of the interactions of physiological pH and chemical pKa in a PBPK model limits scientists' capability in mechanistically describing the drug ADMET. This mini-review compiled literature knowledge on pH-pKa crosstalk and its impacts on drug ADMET, from the viewpoint of PBPK modeling, to pave the way to a systematic incorporation of pH-pKa crosstalk into PBPK modeling and simulation.

Proteolysis Targeting Chimeras (PROTACs) are a rapidly expanding new therapeutic modality inducing selective protein degradation and offering the potential of a differentiated pharmacological profile across multiple therapeutic areas. As the repertoire of protein targets and E3 ligases available for incorporation into PROTACs continues to grow, understanding the drug- and system-dependent parameters for PROTACs will be critical for achieving tissue/cell specific pharmacology. The review discusses the current knowledge and future direction of in vivo PROTAC study evaluation. The importance of establishing the quantitative relationship between loss of protein target and biological function in vivo, coupled with building mechanistic PK/PD and ultimately PBPK/PD models, is emphasised with the aim to aid translation from preclinical to clinical space., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Determining fetal drug exposure (except at the time of birth) is not possible for both logistical and ethical reasons. Therefore, we developed a novel maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) model to predict fetal exposure to drugs and populated this model with gestational age-dependent changes in maternal-fetal physiology. Then, we used this m-f-PBPK to: 1) perform a series of sensitivity analyses to quantitatively demonstrate the impact of fetoplacental metabolism and placental transport on fetal drug exposure for various drug-dosing regimens administered to the mother; 2) predict the impact of gestational age on fetal drug exposure; and 3) demonstrate that a single umbilical venous (UV)/maternal plasma (MP) ratio (even after multiple-dose oral administration to steady state) does not necessarily reflect fetal drug exposure. In addition, we verified the implementation of this m-f-PBPK model by comparing the predicted UV/MP and fetal/MP AUC ratios with those predicted at steady state after an intravenous infusion. Our simulations yielded novel insights into the quantitative contribution of fetoplacental metabolism and/or placental transport on gestational age-dependent fetal drug exposure. Through sensitivity analyses, we demonstrated that the UV/MP ratio does not measure the extent of fetal drug exposure unless obtained at steady state after an intravenous infusion or when there is little or no fluctuation in MP drug concentrations after multiple-dose oral administration. The proposed m-f-PBPK model can be used to predict fetal exposure to drugs across gestational ages and therefore provide the necessary information to assess the risk of drug toxicity to the fetus., (Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.)

A 4-compartment permeability-limited brain (4Brain) model consisting of brain blood, brain mass, cranial and spinal cerebrospinal fluid (CSF) compartments has been developed and incorporated into a whole body physiologically-based pharmacokinetic (PBPK) model within the Simcyp Simulator. The model assumptions, structure, governing equations and system parameters are described. The model in particular considers the anatomy and physiology of the brain and CSF, including CSF secretion, circulation and absorption, as well as the function of various efflux and uptake transporters existing on the blood-brain barrier (BBB) and blood-CSF barrier (BCSFB), together with the known parameter variability. The model performance was verified using in vitro data and clinical observations for paracetamol and phenytoin. The simulated paracetamol spinal CSF concentration is comparable with clinical lumbar CSF data for both intravenous and oral doses. Phenytoin CSF concentration-time profiles in epileptic patients were simulated after accounting for disease-induced over-expression of efflux transporters within the BBB. Various 'what-if' scenarios, involving variation of specific drug and system parameters of the model, demonstrated that the 4Brain model is able to simulate the possible impact of transporter-mediated drug-drug interactions, the lumbar puncture process and the age-dependent change in the CSF turnover rate on the local PK within the brain., (Copyright © 2016 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.)

Purpose: The objective of this study is to develop a physiologically-based pharmacokinetic (PBPK) model for each omeprazole enantiomer that accounts for nonlinear PK of the two enantiomers as well as omeprazole racemic drug., Methods: By integrating in vitro, in silico and human PK data, we first developed PBPK models for each enantiomer. Simulation of racemic omeprazole PK was accomplished by combining enantiomer models that allow mutual drug interactions to occur., Results: The established PBPK models for the first time satisfactorily predicted the nonlinear PK of esomeprazole, R-omeprazole and the racemic drug. The modeling exercises revealed that the strong time-dependent inhibition of CYP2C19 by esomeprazole greatly altered the R-omeprazole PK following administration of racemic omeprazole as in contrast to R-omeprazole given alone. When PBPK models incorporated both autoinhibition of each enantiomer and mutual interactions, the ratios between predicted and observed AUC following single and multiple dosing of omeprazole were 0.97 and 0.94, respectively., Conclusions: PBPK models of omeprazole enantiomers and racemic drug were developed. These models can be utilized to assess CYP2C19-mediated drug and genetic interaction potential for omeprazole and esomeprazole.

Aims: Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies., Methods: A perfusion-limited form of a 13-compartment full-PBPK model (Simcyp® Simulator) was used for the nonpregnant women, and this was extended to the pregnant state by applying known changes to all model components (including the gestational related activity of specific cytochrome P450 enzymes) and through the addition of an extra compartment to represent the fetoplacental unit. The uterus and the mammary glands were grouped into the muscle compartment. The model was implemented in Matlab Simulink and validated using clinical observations., Results: The p-PBPK model predicted the PK changes of three model compounds (namely caffeine, metoprolol and midazolam) for CYP1A2, CYP2D6 and CYP3A4 during pregnancy within twofold of observed values. The changes during the third trimester were predicted to be a 100% increase, a 30% decrease and a 35% decrease in the exposure of caffeine, metoprolol and midazolam, respectively, compared with the nonpregnant women., Conclusions: In the absence of clinical data, the in silico prediction of PK behaviour during pregnancy can provide a valuable aid to dose adjustment in pregnant women. The performance of the model for drugs metabolized by a single enzyme to different degrees (high and low extraction) and for drugs that are eliminated by several different routes warrants further study., (© 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.)

Background: The physiological fact that a stable level of brain glucose is more important than that of blood glucose suggests that the ultimate goal of the glucose-insulin-glucagon (GIG) regulatory system may be homeostasis of glucose concentration in the brain rather than in the circulation., Methods: In order to demonstrate the relationship between brain glucose homeostasis and blood hyperglycemia in diabetes, a brain-oriented mathematical model was developed by considering the brain as the controlled object while the remaining body as the actuator. After approximating the body compartmentally, the concentration dynamics of glucose, as well as those of insulin and glucagon, are described in each compartment. The brain-endocrine crosstalk, which regulates blood glucose level for brain glucose homeostasis together with the peripheral interactions among glucose, insulin and glucagon, is modeled as a proportional feedback control of brain glucose. Correlated to the brain, long-term effects of psychological stress and effects of blood-brain-barrier (BBB) adaptation to dysglycemia on the generation of hyperglycemia are also taken into account in the model., Results: It is shown that simulation profiles obtained from the model are qualitatively or partially quantitatively consistent with clinical data, concerning the GIG regulatory system responses to bolus glucose, stepwise and continuous glucose infusion. Simulations also revealed that both stress and BBB adaptation contribute to the generation of hyperglycemia., Conclusion: Simulations of the model of a healthy person under long-term severe stress demonstrated that feedback control of brain glucose concentration results in elevation of blood glucose level. In this paper, we try to suggest that hyperglycemia in diabetes may be a normal outcome of brain glucose homeostasis.

Brain hypothermia treatment (BHT) requires proper mechanical ventilation and therapeutic cooling. The cooling strategy for BHT has been mainly discussed in the literature while little information is available on the respiratory management. We first developed a mathematical model that integrates the respiratory and biothermal dynamics to discuss the simultaneous managements of mechanical ventilation and therapeutic cooling. The effect of temperature on the linear approximations of hemoglobin-oxygen dissociation, together with temperature dependency of metabolism, is introduced during modeling to combine the respiratory system with the biothermal system. By comparing its transient behavior with published data, the model is verified qualitatively and then quantitatively. Second, model-based simulation of the current respiratory management in BHT suggests reduction of minute ventilation in reference to cooled brain temperature to stabilize the states of blood and brain oxygenation. Lastly, the relationship between cooling temperature and minute ventilation is approximated by a linear first-order transfer function of static gain 0.61min(-1) degrees C(-1) and time constant 8.9 h, which is used to develop a feedforward control to tune the mechanical ventilator in concert with temperature regulation of the cooling blanket. Discussion of the model encourages further studies that provide direct evidence from clinical experiments.

Background: Although propofol is commonly used for general anaesthesia of normothermic patients in clinical practice, little information is available in the literature regarding the use of propofol anaesthesia for intracranial decompression using brain hypothermia treatment. A novel propofol anaesthesia scheme is proposed that should promote such clinical application and improve understanding of the principles of using propofol anaesthesia for hypothermic intracranial decompression., Methods: Theoretical analysis was carried out using a previously-developed integrative model of the thermoregulatory, hemodynamic and pharmacokinetic subsystems. Propofol kinetics is described using a framework similar to that of this model and combined with the thermoregulation subsystem through the pharmacodynamic relationship between the blood propofol concentration and the thermoregulatory threshold. A propofol anaesthesia scheme for hypothermic intracranial decompression was simulated using the integrative model., Results: Compared to the empirical anaesthesia scheme, the proposed anaesthesia scheme can reduce the required propofol dosage by more than 18%., Conclusion: The integrative model of the thermoregulatory, hemodynamic and pharmacokinetic subsystems is effective in analyzing the use of propofol anaesthesia for hypothermic intracranial decompression. This propofol infusion scheme appears to be more appropriate for clinical application than the empirical one.

Brain hypothermia treatment (BHT) is an intensive care characterized by simultaneous managements of various vital signs, such as intracranial temperature (ICT) and pressure (ICP), of the severe neuropatient. Medical treatments including therapeutic ambient cooling and diuresis are separately carried out based on the experience of the medical staff involved in the clinical management of various pathophysiological processes, such as thermodynamics, hemodynamics and pharmacokinetics. However, no special attention has been paid to the interactions among these subsystems in therapeutic hypothermia because of the lack of theoretical knowledge. Therefore, quantitative analyses using an integrated model of various physiological processes and their interactions are of pressing need. In the present paper, we propose a general compartmental model to describe the pathophysiological processes of the three aforementioned dynamics, on account of the dynamical analogy of temperature, pressure and concentration. The model is verified by the agreement of model-based simulation results with clinical evidence. Based on responses of the integrated model to various stimuli, a transfer function matrix is identified to linearly approximate the characteristic interrelationships between medical treatments (ambient cooling and diuresis) and the vital signs (ICT and ICP). Then a controller that decouples ambient cooling and diuresis is proposed for efficient management of ICT and ICP, enhancement of hypothermic decompression and reduction of diuretic dosage. Decoupling control simulation indicates that ICT and ICP of the integrated model, representing a patient under BHT, can be simultaneously regulated by a single PID controller for ambient cooling and another for diuresis. The proposed decoupler effectively establishes hypothermic decompression, reduces the dosage of diuretic and improves ICP management. Theoretical analyses of the integrated model and decoupling control of ICT and ICP provide insights into the intensive care of various pathophysiological processes in patients undergoing BHT.

Brain hypothermia treatment is used as a neuroprotectant to decompress the elevated intracranial pressure (ICP) in acute neuropatients. However, a quantitative relationship between decompression and brain hypothermia is still unclear, this makes medical treatment difficult and ineffective. The objective of this paper is to develop a general mathematical model integrating hemodynamics and biothermal dynamics to enable a quantitative prediction of transient responses of elevated ICP to ambient cooling temperature. The model consists of a lumped-parameter compartmental representation of the body, and is based on two mechanisms of temperature dependence encountered in hypothermia, i.e. the van't Hoff's effect of metabolism and the Arrhenius' effect of capillary filtration. Model parameters are taken from the literature. The model is verified by comparing the simulation results to population-averaged data and clinical evidence of brain hypothermia treatment. It is possible to assign special model inputs to mimic clinical maneuvers, and to adjust model parameters to simulate pathophysiological states of intracranial hypertension. Characteristics of elevated ICP are quantitatively estimated by using linear approximation of step response with respect to ambient cooling temperature. Gain of about 4.9 mmHg degrees C(-1), dead time of about 1.0 h and a time constant of about 9.8h are estimated for the hypothermic decompression. Based on the estimated characteristics, a feedback control of elevated ICP is introduced in a simulated intracranial hypertension of vasogenic brain edema. Simulation results suggest the possibility of an automatic control of the elevated ICP in brain hypothermia treatment.