SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).

Automated registration-based skull stripping procedure for feline neuroimaging.

Skull stripping is a fundamental preprocessing step in modern neuroimaging analyses that consists of removing non-brain voxels from structural images. When performed entirely manually, this laborious step can be rate-limiting for analyses, with the potential to influence the population size chosen. This emphasizes the need for a fully- or semi-automated masking procedure to decrease man-hours without an associated decline in accuracy. These algorithms are plentiful in human neuroimaging but are relatively lacking for the plethora of animal species used in research. Unfortunately, software designed for humans cannot be easily transformed for animal use due to the high amount of tailoring required to accurately account for the considerable degree of variation within the highly folded human cortex. As most animals have a relatively less complex cerebral morphology, intersubject variability is consequently decreased, presenting the possibility to simply warp the brain mask of a template image into subject space for the purpose of skull stripping. This study presents the use of the Cat Automated Registration-based Skull Stripper (CARSS) tool on feline structural images. Validation metrics revealed that this method was able to perform on par with manual raters on >90 % of scans tested, and that its consistency across multiple runs was superior to that of masking performed by two independent raters. Additionally, CARSS outperformed three well-known skull stripping programs on the validation dataset. Despite a handful of manual interventions required, the presented tool reduced the man-hours required to skull strip 60 feline images over tenfold when compared to a fully manual approach, proving to be invaluable for feline neuroimaging studies, particularly those with large population sizes.

Taohong Siwu decoction enhances the chemotherapeutic efficacy of doxorubicin by promoting tumor vascular normalization.

BACKGROUND: Instead of completely suppressing blood vessels inside tumors, vascular normalization therapy is proposed to normalize and prune the abnormal vasculature in tumor microenvironment (TME) to acquire a normal and stable blood flow and perfusion. The theoretical basis for the use of "blood-activating and stasis-resolving" formulas in Traditional Chinese Medicine to treat cancer is highly consistent with the principle of vascular normalization therapy, suggesting the potential application of these traditional formulas in vascular normalization therapy. PURPOSE: To study the underlying mechanisms of a classical "blood-activating and stasis-resolving" formula, Taohong Siwu decoction (TSD), in enhancing the efficacy of chemotherapy for breast cancer treatment. STUDY DESIGN: HUVECs and transgenic zebrafish embryos were used as the major model in vitro. A 4T1 mouse breast cancer model was applied to study tumor vasculature normalization of TSD and the combination effects with DOX. RESULTS: Our data showed that TSD exhibited anti-angiogenic potential in HUVECs and transgenic zebrafish embryos. After 20 days treatment, TSD significantly normalized the tumor vasculature by remodeling vessel structure, reducing intratumoral hypoxia and vessel leakage, and promoting vessel maturation and blood perfusion in 4T1 breast tumor-bearing mice. Moreover, the anti-tumor efficacy of doxorubicin liposome in 4T1 breast tumors was significantly improved by TSD, including the suppression of tumor cell proliferation, angiogenesis, hypoxia, and the increase of cell apoptosis, which is likely through the vascular normalization induced by TSD. TSD also shifted the macrophage polarization from M2 to M1 phenotype in TME during the combination therapy, as evidenced by the reduced number of CD206+ macrophages and increased number of CD86+ macrophages. Additionally, TSD treatment protected against doxorubicin-induced cardiotoxicity in animals, as evidenced by the reduced cardiomyocytes apoptosis and improved heart function. CONCLUSION: This study demonstrated for the first time that TSD as a classical Chinese formula can enhance the drug efficacy and reduce the side effects of doxorubicin. These findings can support that TSD could be used as an adjuvant therapy in combination with conventional chemotherapy for the future breast cancer treatment.

BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.

Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-line origin is lymphoid or myeloid, respectively. Deep learning (DL) and artificial intelligence (AI) are revolutionizing medical sciences by assisting clinicians with rapid illness identification, reducing workload, and enhancing diagnostic accuracy. This paper proposes a DL-based novel BSNEU-net framework to detect acute leukemia. It comprises 4 Union Blocks (UB) and incorporates block feature map distortion (BFMD) with switchable normalization (SN) in each UB. The UB employs union convolution to extract more discriminant features. The BFMD is adapted to acquire more generalized patterns to minimize overfitting, whereas SN layers are appended to improve the model's convergence and generalization capabilities. The uniform utilization of batch normalization across convolution layers is sensitive to the mini-batch dimension changes, which is effectively remedied by incorporating an SN layer. Here, a new dataset comprising 2400 blood smear images of ALL, AML, and healthy cases is proposed, as DL methodologies necessitate a sizeable and well-annotated dataset to combat overfitting issues. Further, a heterogeneous dataset comprising 2700 smear images is created by combining four publicly accessible benchmark datasets of ALL, AML, and healthy cases. The BSNEU-net model achieved excellent performance with 99.37% accuracy on the novel dataset and 99.44% accuracy on the heterogeneous dataset. The comparative analysis signifies the superiority of the proposed methodology with comparing schemes.

Evaluating the implementation of the Pediatric Acute Care Education (PACE) program in northwestern Tanzania: a mixed-methods study guided by normalization process theory.

BACKGROUND: In low- and middle-income countries (LMICs), such as Tanzania, the competency of healthcare providers critically influences the quality of pediatric care. To address this issue, we introduced Pediatric Acute Care Education (PACE), an adaptive learning program to enhance provider competency in Tanzania's guidelines for managing seriously ill children. Adaptive learning is a promising alternative to current in-service education, yet optimal implementation strategies in LMIC settings are unknown. OBJECTIVES: (1) To evaluate the initial PACE implementation in Mwanza, Tanzania, using the construct of normalization process theory (NPT); (2) To provide insights into its feasibility, acceptability, and scalability potential. METHODS: Mixed-methods study involving healthcare providers at three facilities. Quantitative data was collected using the Normalization MeAsure Development (NoMAD) questionnaire, while qualitative data was gathered through in-depth interviews (IDIs) and focus groups discussions (FGDs). RESULTS: Eighty-two healthcare providers completed the NoMAD survey. Additionally, 24 senior providers participated in IDIs, and 79 junior providers participated in FGDs. Coherence and cognitive participation were high, demonstrating that PACE is well understood and resonates with existing healthcare goals. Providers expressed a willingness to integrate PACE into their practices, distinguishing it from existing educational methods. However, challenges related to resources and infrastructure, particularly those affecting collective action, were noted. Early indicators point toward the potential for long-term sustainability of the PACE, but assessment of reflexive monitoring was limited due to the study's focus on PACE's initial implementation. CONCLUSION: This study offers vital insights into the feasibility and acceptability of implementing PACE in a Tanzanian context. While PACE aligns well with healthcare objectives, addressing resource and infrastructure challenges as well as conducting a longer-term study to assess reflexive monitoring is crucial for its successful implementation. Furthermore, the study underscores the value of the NPT as a framework for guiding implementation processes, with broader implications for implementation science and pediatric acute care in LMICs.

Galunisertib promotes bevacizumab-induced vascular normalization in nasopharyngeal carcinoma: Multi-parameter MRI evaluation.

Tumor vascular normalization (TVN) is associated with antitumor therapeutic efficacy in nasopharyngeal carcinoma (NPC). However, the short time window of TVN is the biggest hindrance to its wide clinical application. We investigated whether targeting transforming growth factor beta can enhance the TVN effect of bevacizumab (BEV)-induced patient-derived xenograft (PDX) models of NPC. We constructed mouse subcutaneous PDX models of NPC and classified the mice into four drug-treatment groups, namely placebo control, galunisertib, BEV, and galunisertib + BEV. We performed MRI multi-parameter examinations at different time points and evaluated the vascular density, vascular structure, and tumor hypoxia microenvironment by histopathology. The efficacy of chemotherapy and drug delivery was evaluated by administering cisplatin. We found that combined therapy with galunisertib and BEV significantly delayed tumor growth, enhanced the TVN effect, and improved chemotherapeutic efficacy compared with monotherapy. Mechanistically, galunisertib reversed the epithelial-mesenchymal transition process and inhibited the expression of hypoxia-inducible factor 1α and vascular endothelial growth factor by downregulating LAMC2. Correlation analysis of MRI data and pathological indicators showed that there was a good correlation between them.

Enhanced efficacy of combined VEGFR peptide-drug conjugate and anti-PD-1 antibody in treating hepatocellular carcinoma.

This study aimed to design a VEGFR-targeting peptide-drug conjugate with the ability to decrease tumor burden and suppress tumor angiogenesis, and to further evaluate the therapeutic effect of anti-PD-1 antibody in HCC therapy. A VEGFR-targeting peptide VEGF125 - 136 (QR) was conjugated with a lytic peptide (KLU) to form a peptide-drug conjugate QR-KLU. And the efficacy of QR-KLU in combination with anti-PD-1 antibody for HCC therapy in vivo and in vitro were evaluated. QR-KLU inhibited the proliferation and migration of mouse HCC cell line (Hepa1-6) cells under normoxic and hypoxic conditions in a dose-dependent manner. In the subcutaneous Hepa1-6 tumor model, QR-KLU combined with the anti-PD-1 antibody substantially inhibited tumor growth, promoted tumor necrosis, and prolonged the survival time of tumor-bearing mice. QR-KLU substantially inhibited hypoxia-induced expression of VEGF, promoted tumor vascular normalization, and increased cluster of differentiation 8+ (CD8+) T cell infiltration in the tumor. In addition, QR-KLU and anti-PD-1 antibody demonstrated a strong synergistic effect in promoting the activation of intratumoral CD8+ T cells, reducing the expression of immune-inhibitory factors, and increasing the expression of immune-stimulatory factors. This study proposed a novel approach for enhancing the efficacy of anti-PD-1 antibody using a VEGFR-targeting peptide-drug conjugate in HCC therapy.

'A void in our community': exploring the complexities of delivering and implementing primary care services for transgender individuals in Northern Ontario.

AIM: To understand how the implementation of primary care services for transgender individuals is undertaken and delivered by practitioners in Northern Ontario. BACKGROUND: Northern Ontario, Canada, has a shortage of primary care health practitioners, and of these, there are a limited number providing transgender primary care. Transgender people in Northern Ontario must also negotiate a lack of allied and specialty services related to transgender health and travel over long distances to access those services that do exist. METHODS: A convergent mixed methods design was guided by normalization process theory (NPT) to explore transgender primary care delivery and implementation by nurses, nurse practitioners, physicians, social workers, and psychotherapists. A survey measuring implementation processes was elaborated through qualitative interviews with participants. Analysis of key themes emerging using the NPT framework informed understanding of primary care successes, barriers, and gaps in Northern Ontario. FINDINGS: Key themes included the need for more education on transgender primary care practice, increased need for training and awareness on transgender resources, identification of unique gaps and barriers to access in Northern Ontario transgender care, and the benefits of embedding and normalizing transgender care in clinical practice to practitioners and transgender patients. These findings are key to understanding and improving access and eliminating healthcare barriers for transgender people in Northern Ontario.

Combined manifestation of two geometric visual illusions.

The present study continued to investigate whether the effects of length misperception caused by cross-shaped (formed by two pairs of the oppositely oriented Müller-Lyer wings) contextual distractors can be explained by the combined manifestation of two different (i.e., the Müller-Lyer and filled-space) geometric illusions of extent. In psychophysical experiments, the luminance of one pair of wings was randomly changed, while the luminance of the other pair remained constant. Two different distractor orientations were used-when the wings with constant luminance formed the right side of the cross or the left side, otherwise. To separately evaluate the manifestation of the Müller-Lyer illusion under different luminance conditions, two distracting crosses of the same orientation were attached to the lateral stimulus terminators in the first series of experiments. In the following four series, a single distracting cross (with different orientation) was attached to one of the lateral stimulus terminators and various combinations of the constant and background luminance were used. To interpret the experimental data, we used the basic computational principles of previously developed quantitative models of hypothetical visual mechanisms underlying the emergence of the Müller-Lyer illusion and the filled-space illusion. It was shown that the results of theoretical calculations adequately approximate the experimental curves obtained for all modifications of stimuli, which strongly supports the suggestion that the joint manifestations of these two illusions can be considered among the main factors determining the features of the illusion investigated.

Stability and expression patterns of housekeeping genes in Mediterranean mussels (Mytilus galloprovincialis) under field investigations.

The use of marine mussels as biological models encompasses a broad range of research fields, in which the application of RNA analyses disclosed novel biomarkers of environmental stress and investigated biochemical mechanisms of action. Quantitative real-time PCR (qPCR) is the gold standard for these studies, and despite its wide use and available protocols, it may be affected by technical flaws requiring reference gene data normalization. In this study, stability of housekeeping genes commonly employed as reference genes in qPCR analyses with Mytilus galloprovincialis was explored under field conditions. Mussels were collected from farms in the Northwestern Adriatic Sea. The sampling strategy considered latitudinal gradients of environmental parameters (proxied by location), gender, and their interactions with seasonality. Analyses of gene stability were performed using different algorithms. BestKeeper and geNorm agreed that combination of the ribosomal genes 18S ribosomal RNA (18S) and 28S ribosomal RNA (28S) was the best normalization strategy in the conditions tested, which agrees with available evidence. NormFinder provided different normalization strategies, involving combinations of tubulin (TUB)/28S (Gender/Season effect) or TUB/helicase (HEL) (Location/Season effect). Since NormFinder considers data grouping and computes both intra- and inter-group stability variations, it should work better with complex experimental designs and dataset structuring. Under the selected normalization strategies, expressions of the variable housekeeping genes actin (ACT) and elongation factor-1α (EF1) correlated with seasonal and latitudinal changes of abiotic environmental factors and mussel physiological status. Results point to consider ACT and EF1 expressions as molecular biomarkers of mussel general physiological status in field studies.

Joint coordinate attention mechanism and instance normalization for COVID online comments text classification.

Background: The majority of extant methodologies for text classification prioritize the extraction of feature representations from texts with high degrees of distinction, a process that may result in computational inefficiencies. To address this limitation, the current study proposes a novel approach by directly leveraging label information to construct text representations. This integration aims to optimize the use of label data alongside textual content. Methods: The methodology initiated with separate pre-processing of texts and labels, followed by encoding through a projection layer. This research then utilized a conventional self-attention model enhanced by instance normalization (IN) and Gaussian Error Linear Unit (GELU) functions to assess emotional valences in review texts. An advanced self-attention mechanism was further developed to enable the efficient integration of text and label information. In the final stage, an adaptive label encoder was employed to extract relevant label information from the combined text-label data efficiently. Results: Empirical evaluations demonstrate that the proposed model achieves a significant improvement in classification performance, outperforming existing methodologies. This enhancement is quantitatively evidenced by its superior micro-F1 score, indicating the efficacy of integrating label information into text classification processes. This suggests that the model not only addresses computational inefficiencies but also enhances the accuracy of text classification.

Identification of hypoxic macrophages in glioblastoma: Unveiling therapeutic insights from tumour microenvironment analysis.

Tumor-associatedmacrophages (TAMs) exhibit remarkable heterogeneity in glioblastoma. Spatially resolved single-cell transcriptomic studies identified a monocyte-derived TAM subset localized in the peri-necrotic niche, driven by hypoxic cues to acquire ahypoxia response signature. These hypoxia-TAMs destabilize endothelial adherens junctions through adrenomedullin paracrine signaling, promoting the formation of hyperpermeable neovasculature that impedes drug delivery. Blocking adrenomedullin produced by hypoxia-TAMs restores vascular integrity, increases drug deliveryinto tumors, and provides combinatorial therapeutic benefits. Here we discuss the heterogeneity of TAMs regarding functional states and locations in glioblastomas, and propose future directions for studying the temporospatial dynamics of multifaceted TAM. HIGHLIGHTS: Single-cell omics reveal a functionally and spatially distinct hypoxia-TAM subset in glioblastoma. Adrenomedullin secreted by hypoxia-TAM destabilizes tumor vasculature and its blockade enhances vessel integrity and drug delivery.

Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.

Purpose: Dopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson's disease (PD) and related parkinsonian disorders. While 18F-FP-CIT PET offers advantages in spatial resolution and sensitivity over 123I-ß-CIT or 123I-FP-CIT SPECT imaging, accurate quantification remains essential. This study presents a novel automatic quantification method for 18F-FP-CIT PET images, utilizing an artificial intelligence (AI)-based robust PET spatial normalization (SN) technology that eliminates the need for anatomical images. Methods: The proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of 18F-FP-CIT PET and 3D structural MRI. Remarkably, only PET images are required as input during inference. A cyclic training strategy enables backward deformation from template to individual space. An additional 89 paired 18F-FP-CIT PET and 3D MRI datasets were used to evaluate the accuracy of striatal activity quantification. MRI-based PET quantification using FIRST software was also conducted for comparison. The proposed method was also validated using 135 external datasets. Results: The proposed AI-based method successfully generated spatially normalized 18F-FP-CIT PET images, obviating the need for CT or MRI. The striatal PET activity determined by proposed PET-only method and MRI-based PET quantification using FIRST algorithm were highly correlated, with R 2 and slope ranging 0.96-0.99 and 0.98-1.02 in both internal and external datasets. Conclusion: Our AI-based SN method enables accurate automatic quantification of striatal activity in 18F-FP-CIT brain PET images without MRI support. This approach holds promise for evaluating presynaptic dopaminergic function in PD and related parkinsonian disorders.

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise.

Bi-stochastic normalization provides an alternative normalization of graph Laplacians in graph-based data analysis and can be computed efficiently by Sinkhorn-Knopp (SK) iterations. This paper proves the convergence of bi-stochastically normalized graph Laplacian to manifold (weighted-)Laplacian with rates, when [Formula: see text] data points are i.i.d. sampled from a general [Formula: see text]-dimensional manifold embedded in a possibly high-dimensional space. Under certain joint limit of [Formula: see text] and kernel bandwidth [Formula: see text], the point-wise convergence rate of the graph Laplacian operator (under 2-norm) is proved to be [Formula: see text] at finite large [Formula: see text] up to log factors, achieved at the scaling of [Formula: see text]. When the manifold data are corrupted by outlier noise, we theoretically prove the graph Laplacian point-wise consistency which matches the rate for clean manifold data plus an additional term proportional to the boundedness of the inner-products of the noise vectors among themselves and with data vectors. Motivated by our analysis, which suggests that not exact bi-stochastic normalization but an approximate one will achieve the same consistency rate, we propose an approximate and constrained matrix scaling problem that can be solved by SK iterations with early termination. Numerical experiments support our theoretical results and show the robustness of bi-stochastically normalized graph Laplacian to high-dimensional outlier noise.

Object and spatial discrimination makes weakly supervised local feature better.

Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precision of keypoints. To address these challenges, this study introduces a novel local feature extraction pipeline named OSDFeat (Object and Spatial Discrimination Feature). OSDFeat employs a decoupling strategy, training descriptor and detection networks independently. Inspired by semantic correspondence, we propose an Object and Spatial Discrimination ResUNet (OSD-ResUNet). OSD-ResUNet captures features from the feature map that differentiate object appearance and spatial context, thus enhancing descriptor performance. To further improve the discriminative capability of descriptors, we propose a Discrimination Information Retained Normalization module (DIRN). DIRN complementarily integrates spatial-wise normalization and channel-wise normalization, yielding descriptors that are more distinguishable and informative. In the detection network, we propose a Cross Saliency Pooling module (CSP). CSP employs a cross-shaped kernel to aggregate long-range context in both vertical and horizontal dimensions. By enhancing the saliency of keypoints, CSP enables the detection network to effectively utilize descriptor information and achieve more precise localization of keypoints. Compared to the previous best local feature extraction methods, OSDFeat achieves Mean Matching Accuracy of 79.4% in local feature matching task, improving by 1.9% and achieving state-of-the-art results. Additionally, OSDFeat achieves competitive results in Visual Localization and 3D Reconstruction. The results of this study indicate that object and spatial discrimination can improve the accuracy and robustness of local feature, even in challenging environments. The code is available at https://github.com/pandaandyy/OSDFeat.

Improvement in antibacterial use in intensive care units from Argentina: A quality improvement collaborative process evaluation using Normalization Process Theory.

BACKGROUND: Healthcare-associated infections and antibiotic resistance worsen globally. Antibiotic stewardship programs (ASP) aim to optimise infection treatment and curb resistance, yet implementation hurdles persist. This study examined ASP challenges in ICUs. METHODS: This study employed a qualitative methodological design to evaluate the implementation process of an antibiotic stewardship program (ASP) in eight intensive care units (ICUs) across Argentina. Thirty-four semi-structured interviews with healthcare workers (HCWs) were conducted. Interviews were analysed guided by Normalisation Process Theory, examining coherence, cognitive participation, collective action, and reflexive monitoring constructs. RESULTS: Key challenges included insufficient human resources, lack of institutional support, and resistance to change, particularly among staff not initially involved in the study. Despite these challenges, the program saw partial success in improving ICU practices, particularly in antibiotic use and communication across departments. The main strategy implemented in this quality improvement collaborative was the use of improvement cycles, which served as the central component for driving change. However, participation in improvement cycles was inconsistent, and sustainability post-intervention remains uncertain due to workload pressures and the need for continuous education. Concerns about workload and communication barriers persisted. Many participants did not perceive training as a separate component, which led to low engagement. Resistance to change became evident during modifications to clinical guidelines. The intervention had a positive impact on various processes, including communication and record keeping. CONCLUSION: This study underscores the persistent challenges in implementing ASPs in healthcare, emphasising the need for enhanced collaboration, workforce capacity building, and evidence-based practices to overcome barriers and optimize antimicrobial use to improve patient outcomes.

CMCN: Chinese medical concept normalization using continual learning and knowledge-enhanced.

Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research achievements, Chinese medical concept normalization (CMCN) remains insufficiently explored due to its complex syntactic structure and the paucity of Chinese medical semantic and ontology resources. In recent years, deep learning has been extensively applied across numerous natural language processing tasks, owing to its robust learning capabilities, adaptability, and transferability. It has proven to be well suited for intricate and specialized knowledge discovery research in the biomedical field. In this study, we conduct research on CMCN through the lens of deep learning. Specifically, our research introduces a model that leverages polymorphic semantic information and knowledge enhanced through multi-task learning and retain more important medical features through continual learning. As the cornerstone of CMCN, disease names are the main focus of this research. We evaluated various methodologies on Chinese disease dataset built by ourselves, finally achieving 76.12 % on Accuracy@1, 87.20 % on Accuracy@5 and 90.02 % on Accuracy@10 with our best-performing model GCBM-BSCL. This research not only advances the fields of knowledge mining and medical concept normalization but also enhances the integration and application of artificial intelligence in the medical and health field. We have published the source code and results on https://github.com/BearLiX/CMCN.

Predicting factors of blood pressure normalization in hypertensive patients after short-term follow-up.

Introduction: Normalization of blood pressure in hypertensive patients is a major challenge for practitioners. Knowledge of the factors associated with normalization of blood pressure could help optimize management of these hypertensive patients. In this study, we analysed the factors predictive of this in a population of hypertensive patients followed as outpatients in a specialised department. Patients and methods: Retrospective and analytic study (January 2021-May 2022) of adult hypertensive patients over 40 years old who had been receiving antihypertensive treatment as outpatients in the Cardiology Department of the Bouake Teaching Hospital for at least 6 months. We studied the epidemiological and clinical parameters as well as the factors involved in the normalization of blood pressure in this population. Statistical analysis was performed using SPPS version 26 software (SPSS Inc., Chicago, IL, USA). Results: We collected 194 patients records (57.7% women). The mean age was 59.13 years [extremes: 40-89 years]. One hundred and nine (56.2%) patients had a low socioeconomic status and 151 (77.83%) had at least 2 cardiovascular risk factors. The mean systolic blood pressure on admission was 171.12 ± 22.38 mmHg [extremes: 140-259 mmHg] and the mean diastolic blood pressure was 97.98 ± 17.83 mmHg [extremes: 60-168 mmHg]. First-line treatment consisted of dual anti-hypertensive therapy (n = 133; 68.55%) and fixed combination (n = 152; 78.35%). Only 25.25% (n = 49) of patients achieved normalization of blood pressure with therapeutic adherence estimated at 37.62% (n = 73). In multivariate analysis adjusted for anti-hypertensive treatment adherence, age (OR = 1.03; 95% CI = 1.002-1.059; p = 0.039), absence of alcoholism (OR = 9.48; 95% CI = 2.13-42.11; p = 0.003), number of cardiovascular risk factors <2 (OR = 1.52; 95% CI = 1.06-2.16; p = 0.021), normalization of uricemia (OR = 1.05; 95% CI = 1.00-1.11; p = 0.039) and natraemia (OR = 1.01; 95% CI = 1.00-1.03; p = 0.021), dual therapy (OR = 0.40; 95% CI = 0.18-0.90; p = 0.027), change in treatment for optimization (OR = 4.22; 95% CI = 1.71-10.37; p = 0.002), intellectual education (OR = 10.40; 95% CI = 4.31-25.10; p < 0.001) and health insurance (OR = 0.09; 95% CI = 0.04-0.21; p < 0.001) were the main factors predicting normalization of blood pressure. Conclusion: Control of cardiovascular risk factors and compliance with treatment are the main factors in normalizing blood pressure.

Low-dose apatinib optimizes the vascular normalization and enhances the antitumor effect of PD-1 inhibitor in gastric cancer.

Background: Apatinib is a tyrosine kinase inhibitor that has shown potential in combination with immune checkpoint inhibitors (ICIs) in gastric cancer (GC); however, its role in GC is unclear. This research aims to investigate the effect of low-dose apatinib in GC, and analyze the mechanisms of its underlying action. Methods: A mouse model of GC was established, and the experimental mice were divided into different groups for different treatment: group NS (normal saline), group A (low-dose apatinib 50 mg/kg), group B (high-dose apatinib 200 mg/kg), group C [programmed cell death protein 1 (PD-1) inhibitor monotherapy], and group D (PD-1 inhibitor combined with low-dose apatinib). After 14 days of treatment, the tumor and blood samples were collected from all mice for histological and cytokine detection. Results: Compared with the control group, mice in the low-dose apatinib group showed smaller tumor volumes and slower growth. CD31/α-smooth muscle actin (α-SMA) double staining revealed significantly higher coverage of perivascular cells in the low-dose apatinib group by contrast to the control and high-dose apatinib groups, suggesting that low-dose apatinib may alleviate hypoxia. Compared to the high-dose apatinib group, the expression of hypoxia inducible factor 1 alpha (HIF1α) significantly decreased in the low-dose apatinib group. Hematoxylin and eosin (HE) staining results showed a higher proportion of necrotic tumor tissues in the group of mice treated with low-dose apatinib combined with PD-1 inhibitor than in other groups. In addition, this combined treatment significantly reduced the expression of NG2 and HIF1α in mouse tumor tissues, indicating a more normalized vascular density, and also increased the proportion of CD8+ T cells. Conclusions: Low-dose apatinib enhances the antitumor effect of PD-1 inhibitor by normalizing tumor-related blood vessels, alleviating intratumor hypoxia and altering immunosuppressive microenvironment (IM).

A cogent technique to circumvent the use of (some) ratio measures in physiology.

Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value - if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. KEY POINTS: The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known. A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values. A sample drawn from a population of these PCA-derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios. The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition.