Chemosensitizing effect of pentoxifylline in sensitive and multidrug-resistant non-small cell lung cancer cells.

Aim: Multidrug resistance (MDR) is frequent in non-small cell lung cancer (NSCLC) patients, which can be due to its fibrotic stroma. This work explores the combination of pentoxifylline, an anti-fibrotic and chitinase 3-like-1 (CHI3L1) inhibitor drug, with conventional chemotherapy to improve NSCLC treatment. Methods: The effect of pentoxifylline in the expression levels of P-glycoprotein (P-gp), CHI3L1 and its main downstream proteins, as well as on cell death, cell cycle profile, and P-gp activity was studied in two pairs of sensitive and MDR counterpart NSCLC cell lines (NCI-H460/NCI-H460/R and A549/A549-CDR2). Association studies between CHI3L1 gene expression and NSCLC patients' survival were performed using The Cancer Genome Atlas (TCGA) analysis. The sensitizing effect of pentoxifylline to different drug regimens was evaluated in both sensitive and MDR NSCLC cell lines. The cytotoxicity of the drug combinations was assessed in MCF10A non-tumorigenic cells. Results: Pentoxifylline slightly decreased the expression levels of CHI3L1, ß-catenin and signal transducer and activator of transcription 3 (STAT3), and caused a significant increase in the G1 phase of the cell cycle in both pairs of NSCLC cell lines. A significant increase in the % of cell death was observed in the sensitive NCI-H460 cell line. TCGA analysis revealed that high levels of CHI3L1 are associated with low overall survival (OS) in NSCLC patients treated with vinorelbine. Moreover, pentoxifylline sensitized both pairs of sensitive and MDR NSCLC cell lines to the different drug regimens, without causing significant toxicity to non-tumorigenic cells. Conclusion: This study suggests the possibility of combining pentoxifylline with chemotherapy to increase NSCLC therapeutic response, even in cases of MDR.

Salivary immunoglobulin levels and periodontal indices in Brazilian children with and without type 1 diabetes.

This cross-sectional study evaluated the association between salivary immunoglobulins, plaque index, and gingival index in Brazilian children with and without type 1 diabetes mellitus (DM1). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for the reporting of observational studies was followed. The DM1 group had 38 children, and an equal number of volunteers matched by sex and age were recruited as controls. Clinical examination was performed for plaque index and gingival index determination. Non-stimulated whole saliva was collected. Concentrations of IgA, IgG, and IgM were determined by ELISA test. Data were tested by the Kolmogorov-Smirnov, Mann-Whitney, and Spearman tests and a multiple linear regression model (p<0.05) was performed. Gingival index was higher in the Control (DM1: 0.16±0.17; Control: 0.24±0.23, p=0.040). In DM1, there was a correlation between IgA and age (rho=0.371, p=0.024), IgM and IgG (rho=0.459, p=0.007), and IgM and gingival index (rho=0.394, p=0.014). In DM1, multiple linear regression showed that age (p=0.041; ß=0.363), gingival index (p=0.041; ß=0.398), and plaque index (p=0.008; ß=-0.506) were good predictors of IgA levels in saliva. Thus, IgA was the only researched immunoglobulin that was directly associated with plaque and gingival indices in Brazilian children with DM1, but not in control subjects.

RIGOROUS DONOR SELECTION FOR FMT IN ACTIVE ULCERATIVE COLITIS: KEY LESSONS FROM A RANDOMIZED CONTROLLED TRIAL HALTED FOR FUTILITY.

BACKGROUND AND AIMS: Rigorous donor preselection on microbiota level, strict anaerobic processing, and repeated FMT administration were hypothesized to improve FMT induction of remission in UC. METHODS: The RESTORE-UC trial was a multi-centric, double-blind, sham-controlled, randomized trial. Patients with moderate to severe UC (defined by total Mayo 4-10) were randomly allocated to receive four anaerobic-prepared allogenic or autologous donor FMTs. Allogenic donor material was selected after a rigorous screening based on microbial cell count, enterotype, and the abundance of specific genera. The primary endpoint was steroid-free clinical remission (total Mayo ≤2, no sub-score >1) at week 8. A pre-planned futility analysis was performed after 66% (n=72) of intended inclusions (n=108). Quantitative microbiome profiling (n=44) was performed at weeks 0 and 8. RESULTS: In total, 72 patients were included of which 66 received at least one FMT (allogenic-FMT n=30 and autologous-FMT n=36). At week 8, respectively 3 and 5 patients reached the primary endpoint of steroid-free clinical remission (p=0.72), indicating no treatment difference of at least 5% in favour of allogenic-FMT. Hence, the study was stopped due to futility. Microbiome analysis showed numerically more enterotype transitions upon allogenic-FMT compared to autologous-FMT and more transitions were observed when patients were treated with a different enterotype than their own at baseline (p=0.01). Primary response was associated with lower total Mayo scores, lower bacterial cell counts, and higher Bacteroides 2 prevalence at baseline. CONCLUSION: The RESTORE-UC trial did not meet its primary endpoint of increased steroid-free clinical remission at week 8. Further research should additionally consider patient-selection, sterilized sham-control, increased frequency, density, and viability of FMT prior to administration.

Fully automated planning for anatomical fetal brain MRI on 0.55T.

PURPOSE: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. METHODS: Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. RESULTS: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 ± $$ \pm $$ 2.6 mm for the fetal eyes and 6.5 ± $$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. CONCLUSIONS: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.

Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development.

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum, did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis, Dialister pneumosintes, Parvimonas micra, Peptostreptococcus anaerobius, Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22-80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies.

Infrared spectroscopy for fast screening of diabetes and periodontitis.

SIGNIFICANCE: FT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool. AIM: Via saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP). APPROACH: Eighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and-1800 cm-1, followed by data preprocessing and analysis using machine learning tools. RESULTS: Various FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased. CONCLUSION: The system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78-93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.

SARS-CoV2 Nsp1 is a metal-dependent DNA and RNA endonuclease.

Over recent years, we have been living under a pandemic, caused by the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). One of the major virulence factors of Coronaviruses is the Non-structural protein 1 (Nsp1), known to suppress the host cells protein translation machinery, allowing the virus to produce its own proteins, propagate and invade new cells. To unveil the molecular mechanisms of SARS-CoV2 Nsp1, we have addressed its biochemical and biophysical properties in the presence of calcium, magnesium and manganese. Our findings indicate that the protein in solution is a monomer and binds to both manganese and calcium, with high affinity. Surprisingly, our results show that SARS-CoV2 Nsp1 alone displays metal-dependent endonucleolytic activity towards both RNA and DNA, regardless of the presence of host ribosome. These results show Nsp1 as new nuclease within the coronavirus family. Furthermore, the Nsp1 double variant R124A/K125A presents no nuclease activity for RNA, although it retains activity for DNA, suggesting distinct binding sites for DNA and RNA. Thus, we present for the first time, evidence that the activities of Nsp1 are modulated by the presence of different metals, which are proposed to play an important role during viral infection. This research contributes significantly to our understanding of the mechanisms of action of Coronaviruses.

Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data.

Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.

Explaining protein-protein interactions with knowledge graph-based semantic similarity.

The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of using machine learning as a tool for scientific discovery. Explainable artificial intelligence approaches help clarify algorithmic mechanisms and identify potential bias in the data. Given the complexity of the biomedical domain, explanations should be grounded in domain knowledge which can be achieved by using ontologies and knowledge graphs. These knowledge graphs express knowledge about a domain by capturing different perspectives of the representation of real-world entities. However, the most popular way to explore knowledge graphs with machine learning is through using embeddings, which are not explainable. As an alternative, knowledge graph-based semantic similarity offers the advantage of being explainable. Additionally, similarity can be computed to capture different semantic aspects within the knowledge graph and increasing the explainability of predictive approaches. We propose a novel method to generate explainable vector representations, KGsim2vec, that uses aspect-oriented semantic similarity features to represent pairs of entities in a knowledge graph. Our approach employs a set of machine learning models, including decision trees, genetic programming, random forest and eXtreme gradient boosting, to predict relations between entities. The experiments reveal that considering multiple semantic aspects when representing the similarity between two entities improves explainability and predictive performance. KGsim2vec performs better than black-box methods based on knowledge graph embeddings or graph neural networks. Moreover, KGsim2vec produces global models that can capture biological phenomena and elucidate data biases.

Antibody agonists trigger immune receptor signaling through local exclusion of receptor-type protein tyrosine phosphatases.

Antibodies can block immune receptor engagement or trigger the receptor machinery to initiate signaling. We hypothesized that antibody agonists trigger signaling by sterically excluding large receptor-type protein tyrosine phosphatases (RPTPs) such as CD45 from sites of receptor engagement. An agonist targeting the costimulatory receptor CD28 produced signals that depended on antibody immobilization and were sensitive to the sizes of the receptor, the RPTPs, and the antibody itself. Although both the agonist and a non-agonistic anti-CD28 antibody locally excluded CD45, the agonistic antibody was more effective. An anti-PD-1 antibody that bound membrane proximally excluded CD45, triggered Src homology 2 domain-containing phosphatase 2 recruitment, and suppressed systemic lupus erythematosus and delayed-type hypersensitivity in experimental models. Paradoxically, nivolumab and pembrolizumab, anti-PD-1-blocking antibodies used clinically, also excluded CD45 and were agonistic in certain settings. Reducing these agonistic effects using antibody engineering improved PD-1 blockade. These findings establish a framework for developing new and improved therapies for autoimmunity and cancer.

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system.

BACKGROUND: Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data. METHODS: Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts. RESULTS: The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (<-6/>+6) to 1.47% (<-3/>+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers. CONCLUSIONS: Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories.

Mass spectrometry-based quantification of immunostimulatory gliadin proteins and peptides in coloured wheat varieties: Implications for Celiac Disease.

Pigmented wheat varieties (Triticum aestivum spp.) are getting increasingly popular in modern nutrition and thoroughly researched for their functional and nutraceutical value. The colour of these wheat grains is caused by the expression of natural pigments, including carotenoids and anthocyanins, that can be restricted to either the endosperm, pericarp and/or aleurone layers. While contrasts in phytochemical synthesis give rise to variations among purple, blue, dark and yellow grain's antioxidant and radical scavenging capacities, little is known about their influence on gluten proteins expression, digestibility and immunogenic potential in a Celiac Disease (CD) framework. Herein, it has been found that the expression profile and immunogenic properties of gliadin proteins in pigmented wheat grains might be affected by anthocyanins and carotenoids upregulation, and that the spectra of peptide released upon simulated gastrointestinal digestion is also significantly different. Interestingly, anthocyanin accumulation, as opposed to carotenoids, correlated with a lower immunogenicity and toxicity of gliadins at both protein and peptide levels. Altogether, this study provides first-level evidence on the impact modern breeding practices, seeking higher expression levels of health promoting phytochemicals at the grain level, may have on wheat crops functionality and CD tolerability.

Beyond Words: A Case of Pure Alexia Following Posterior Cerebral Artery Occlusion.

Alexia is an acquired reading disorder known as pure alexia or alexia without agraphia when unaccompanied by other higher-level deficits. We present the case of a 40-year-old man experiencing a sudden-onset headache and blurred vision. Despite an absence of known medical history, the patient exhibited a distinctive difficulty in reading without impairing other language aspects accompanied by a right superior homonymous quadrantanopia. Through comprehensive ophthalmological and neurological evaluations, a diagnosis of pure alexia was established. An imaging scan uncovered a left posterior cerebral artery occlusion as the underlying cause. Meticulous assessments of visual acuity, perimetry, and non-visual functions played a pivotal role in decisively diagnosing this condition. This case emphasizes the indispensable role of ophthalmologists in recognizing urgent clinical conditions that extend beyond ophthalmic concerns.

Glucocorticoid receptor-dependent therapeutic efficacy of tauroursodeoxycholic acid in preclinical models of spinocerebellar ataxia type 3.

Spinocerebellar ataxia type 3 (SCA3) is an adult-onset neurodegenerative disease caused by a polyglutamine expansion in the ataxin-3 (ATXN3) gene. No effective treatment is available for this disorder, other than symptom-directed approaches. Bile acids have shown therapeutic efficacy in neurodegenerative disease models. Here, we pinpointed tauroursodeoxycholic acid (TUDCA) as an efficient therapeutic, improving the motor and neuropathological phenotype of SCA3 nematode and mouse models. Surprisingly, transcriptomic and functional in vivo data showed that TUDCA acts in neuronal tissue through the glucocorticoid receptor (GR), but independently of its canonical receptor, the farnesoid X receptor (FXR). TUDCA was predicted to bind to the GR, in a similar fashion to corticosteroid molecules. GR levels were decreased in disease-affected brain regions, likely due to increased protein degradation as a consequence of ATXN3 dysfunction being restored by TUDCA treatment. Analysis of a SCA3 clinical cohort showed intriguing correlations between the peripheral expression of GR and the predicted age at disease onset in presymptomatic subjects and FKBP5 expression with disease progression, suggesting this pathway as a potential source of biomarkers for future study. We have established a novel in vivo mechanism for the neuroprotective effects of TUDCA in SCA3 and propose this readily available drug for clinical trials in SCA3 patients.

Intrathecal baclofen for the management of hereditary spastic paraparesis: a systematic review.

This systematic review aims to evaluate the use of intrathecal baclofen (ITB) for hereditary spastic paraparesis (HSP) treatment. An extensive search in two electronical databases was performed. We identified articles published between 1990 and 2022 (PubMed, Scopus), and applied the following inclusion criteria: diagnosis of HSP at the time of the intervention, either familial or sporadic; report on the effect of ITB in patients with HSP; test trial via either bolus injections or continuous infusion tests; and ITB pump implantation. A data extraction sheet based on the Cochrane Consumers and Communication Review Group's data extraction template was created and adapted to collect relevant data. A qualitative analysis was performed to present the results in narrative summary fashion. A total of 6 studies met our inclusion criteria. 51 patients with HSP had a pre-implantation ITB trial. The time since the diagnosis until the pump implantation ranged from 5 to 30 years. The initial bolus ranged from 20 to 50 µg and the mean doses used at steady state ranged from 65 to 705 µg. An improvement in spasticity was observed on the modified Ashworth Scale in patients treated with ITB. Although all studies reported a subjective gait improvement, not all found an objective improvement in gait. The most common side effect reported was catheter-related problems. The findings of this review support the use of ITB as an effective and a viable option for the treatment of spasticity in HSP refractory to conservative therapies.

Purple Carrot Extract Exhibits A Neuroprotective Profile In The Nigrostriatal Pathway In The Reserpine-Induced Model Of Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the nigrostriatal pathway. Even with scientific and technological advances, the therapeutic approaches used for the treatment of PD have shown to be largely ineffective in controlling the progression of symptoms in the long term. There is a growing demand for the development of novel therapeutic strategies for PD treatment. Different herbs and supplements have been considered as adjuvant to treat the symptoms of Parkinsonism. The carrot is one of the most consumed vegetable species worldwide, and its root is known for its content of anthocyanins, which possess antioxidant and antiinflammatory properties. This study evaluated the neuroprotective effect of purple carrot extract (CAR) in rats on the reserpine (RES)-induced progressive parkinsonism model. METHODS: Male rats (6-month-old) received orally the CAR (400 mg/kg) or vehicle and subcutaneously RES (0.01 mg/kg) or vehicle for 28 days (Preventive Phase). From the 29th day, rats received CAR or vehicle daily and RES (0.1 mg/kg) or vehicle every other day (for 23 days, Protective phase). Behavioral tests were conducted throughout the treatment. Upon completion, the animals' brain were processed for tyrosine hydroxylase (TH) immunohistochemical assessment. RESULTS: Our results showed that the chronic treatment of CAR protected against motor disabilities, reducing the time of catalepsy behavior and decreasing the frequency of oral movements, possibly by preserving TH levels in the Ventral Tegmental Area (VTA) and SNpc. CONCLUSION: CAR extract is effective to attenuate motor symptoms in rats associated with increased TH+ levels in the Ventral Tegmental Area (VTA) and SNpc, indicating the potential nutraceutical benefits of CAR extract in a progressive parkinsonism model induced by RES.

Automated statistical analysis of raman spectra of nanomaterials.

Both at the academic and the industrial level, material scientists are exploring routes for mass production and functionalization of graphene, carbon nanotubes (CNT), carbon dots, 2D materials, and heterostructures of these. Proper application of the novel materials requires fast and thorough characterization of the samples. Raman spectroscopy stands out as a standard non-invasive technique capable of giving key information on the structure and electronic properties of nanomaterials, including the presence of defects, degree of functionalization, diameter (in the case of CNT), different polytypes, doping, etc. Here, we present a computational tool to automatically analyze the Raman spectral features of nanomaterials, which we illustrate with the example of CNT and graphene. The algorithm manages hundreds of spectra simultaneously and provides statistical information (distribution of Raman shifts, average values of shifts and relative intensities, standard deviations, correlation between different peaks, etc.) of the main spectral features defining the structure and electronic properties of the samples, as well as publication-ready graphical material.