Coronary Intravascular Lithotripsy Effectiveness and Safety in a Real-World Cohort.

Background: Coronary artery calcification is a predictor of adverse outcomes after percutaneous coronary intervention (PCI). Intravascular lithotripsy (IVL) is a promising tool for the treatment of calcified lesions. The aim of this study was to assess the effectiveness and safety of IVL. Methods: A single-center observational study of PCI procedure, with assessment of the outcomes of patients undergoing PCI using IVL, was performed. Angiographic procedural success was used as the primary effectiveness endpoint. The primary safety endpoint was defined as a composite of cardiac death, myocardial infarction and target vessel revascularization within 30 days. Results: A total of 111 patients were included. Indications for PCI spanned the spectrum of chronic (53.2%) and acute coronary syndromes (43%). Lesion preparation before IVL was performed with non-compliant (42%), cutting or OPN (14.4%) balloons and with atherectomy techniques in 11% of procedures. Intravascular imaging was used in 21.6% of procedures. The primary effectiveness endpoint was achieved in 100% and the primary safety endpoint in 3.6% of procedures. Peri-procedural complications were minimal and successfully resolved. Conclusions: IVL was an effective and safe technique for the treatment of calcified coronary lesions. These findings contribute to the growing body of evidence supporting the use of IVL in the management of these challenging scenarios.

Coronary physiology instantaneous wave-free ratio (iFR) derived from x-ray angiography using artificial intelligence deep learning models: a pilot study.

OBJECTIVES: Coronary angiography (CAG)-derived physiology methods have been developed in an attempt to simplify and increase the usage of coronary physiology, based mostly on dynamic fluid computational algorithms. We aimed to develop a different approach based on artificial intelligence methods, which has seldom been explored. METHODS: Consecutive patients undergoing invasive instantaneous free-wave ratio (iFR) measurements were included. We developed artificial intelligence (AI) models capable of classifying target lesions as positive (iFR ≤ 0.89) or negative (iFR > 0.89). The predictions were then compared to the true measurements. RESULTS: Two hundred-fifty measurements were included, and 3 models were developed. Model 3 had the best overall performance: accuracy, negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity were 69%, 88%, 44%, 74%, and 67%, respectively. Performance differed per target vessel. For the left anterior descending artery (LAD), model 3 had the highest accuracy (66%), while model 2 the highest NPV (86%) and sensitivity (91%). PPV was always low/modest. Model 1 had the highest specificity (68%). For the right coronary artery, model 1's accuracy was 86%, NPV was 97%, and specificity was 87%, but all models had low PPV (maximum 25%) and low/modest sensitivity (maximum 60%). For the circumflex, model 1 performed best: accuracy, NPV, PPV, sensitivity, and specificity were 69%, 96%, 24%, 80%, and 68%, respectively. CONCLUSIONS: We developed 3 AI models capable of binary iFR estimation from CAG images. Despite modest accuracy, the consistently high NPV is of potential clinical significance, as it would enable avoiding further invasive maneuvers after CAG. This pivotal study offers proof of concept for further development.

Insulin signaling and mitochondrial phenotype of skeletal muscle are programmed in utero by maternal diabetes.

Maternal diabetes may influence glucose metabolism in adult offspring, an area with limited research on underlying mechanisms. Our study explored the impact of maternal hyperglycemia during pregnancy on insulin resistance development. Adult female Sprague-Dawley rats from control and diabetic mothers were mated, and their female offspring were monitored for 150 days. The rats were euthanized for blood and muscle samples. Maternal diabetes led to heightened insulin levels, increased HOMA-IR, elevated triglycerides, and a raised TyG index in adult offspring. Muscle samples showed a decreased protein expression of AMPK, PI3K, MAPK, DRP1, and MFF. These changes induced intergenerational metabolic programming in female pups, resulting in insulin resistance, dyslipidemia, and glucose intolerance by day 150. Findings highlight the offspring's adaptation to maternal hyperglycemia, involving insulin resistance, metabolic alterations, the downregulation of insulin signaling sensors, and disturbed mitochondrial morphology. Maintaining maternal glycemic control emerges as crucial in mitigating diabetes-associated disorders in adult offspring.

Targeting autophagy by antipsychotic phenothiazines: potential drug repurposing for cancer therapy.

Cancer is recognized as the major cause of death worldwide and the most challenging public health issues. Tumor cells exhibit molecular adaptations and metabolic reprograming to sustain their high proliferative rate and autophagy plays a pivotal role to supply the high demand for metabolic substrates and for recycling cellular components, which has attracted the attention of the researchers. The modulation of the autophagic process sensitizes tumor cells to chemotherapy-induced cell death and reverts drug resistance. In this regard, many in vitro and in vivo studies having shown the anticancer activity of phenothiazine (PTZ) derivatives due to their potent cytotoxicity in tumor cells. Interestingly, PTZ have been used as antiemetics in antitumor chemotherapy-induced vomiting, maybe exerting a combined antitumor effect. Among the mechanisms of cytotoxicity, the modulation of autophagy by these drugs has been highlighted. Therefore, the use of PTZ derivatives can be considered as a repurposing strategy in antitumor chemotherapy. Here, we provided an overview of the effects of antipsychotic PTZ on autophagy in tumor cells, evidencing the molecular targets and discussing the underlying mechanisms. The modulation of autophagy by PTZ in tumor cells have been consistently related to their cytotoxic action. These effects depend on the derivative, their concentration, and also the type of cancer. Most data have shown the impairment of autophagic flux by PTZ, probably due to the blockade of lysosome-autophagosome fusion, but some studies have also suggested the induction of autophagy. These data highlight the therapeutic potential of targeting autophagy by PTZ in cancer chemotherapy.

Clinical and molecular profiling of human visceral adipose tissue reveals impairment of vascular architecture and remodeling as an early hallmark of dysfunction.

Adipose tissue dysfunction is more related to insulin resistance than body mass index itself and an alteration in adipose tissue function is thought to underlie the shift from metabolically healthy to unhealthy obesity. Herein, we performed a clustering analysis that revealed distinct visceral adipose tissue gene expression patterns in patients with obesity at distinct stages of metabolic dysregulation. We have built a cross-sectional cohort that aims at reflecting the evolution of the metabolic sequelae of obesity with the main objective to map the sequential events that play a role in adipose tissue dysfunction from the metabolically healthy (insulin-sensitive) state to several incremental degrees of metabolic dysregulation, encompassing insulin resistance establishment, pre-diabetes, and type 2 diabetes. We found that insulin resistance is mainly marked by the downregulation of adipose tissue vasculature remodeling-associated gene expression, suggesting that processes like angiogenesis and adaptative expansion/retraction ability suffer early dysregulation. Prediabetes was characterized by compensatory growth factor-dependent signaling and increased response to hypoxia, while type 2 diabetes was associated with loss of cellular response to insulin and hypoxia and concomitant upregulation of inflammatory markers. Our findings suggest a putative sequence of dysregulation of biological processes that is not linear and has multiple distinct phases across the metabolic dysregulation process, ultimately culminating in the climax of adipose tissue dysfunction in type 2 diabetes. Several studies have addressed the transcriptomic changes in adipose tissue of patients with obesity. However, to the best of our knowledge, this is the first study unraveling the potential molecular mechanisms associated with the multi-step evolution of adipose tissue dysfunction along the metabolic sequelae of obesity.

Diagnostic and prognostic contribution of DPD scintigraphy in transthyretin V30M cardiac amyloidosis.

BACKGROUND: Early diagnosis and prognostic stratification of cardiac transthyretin amyloidosis are crucial. Although 99mTc 3,3-diphosphono-1,2-propanedicarboxylic acid (DPD) scintigraphy is the preferred method for the non-invasive diagnosis, its accuracy appears to be limited in transthyretin amyloidosis protein (ATTR) V30M mutation. Furthermore, its prognostic value in this mutation is unknown. This study investigated the diagnostic value of DPD scintigraphy to detect ATTR cardiomyopathy in V30M mutation and explored its prognostic value regarding mortality. METHODS: A total of 288 ATTR V30M mutation carriers (median age: 46 years; 49% males) without myocardial thickening (defined as septal thickness ≥13mm) attributable to other causes and who underwent DPD scintigraphy were enrolled. ATTR cardiomyopathy was defined by septal thickness ≥13mm and at least one of the criteria: late heart-to-mediastinum (H/M) 123I-metaiodobenzylguanidine (MIBG) uptake ratio <1.60; electrical heart disease or biopsy-documented amyloidosis. RESULTS: ATTR cardiomyopathy was identified in 41 (14.2%) patients and cardiac DPD uptake in 34 (11.8%). During a mean follow-up of 33.6 ± 1.2 months, 16 patients died (5.6%). Mortality was 14 times higher in patients with ATTR cardiomyopathy, 13 times higher in those with DPD uptake and 10 times higher in those with late H/M MIBG <1.60. The combined assessment of septal thickness and cardiac DPD uptake improved risk stratification: patients without septal thickening and without DPD retention had an excellent prognosis while those who presented either or both of them had a significantly worse prognosis, with 5-year mortality rates ranging from 39.9 to 53.3%. CONCLUSIONS: DPD scintigraphy is useful for prognostic stratification of ATTR V30M mutation carriers. Patients without septal thickening and no DPD uptake present the best prognosis compared to those with any signs of cardiac involvement.

Astaxanthin induces autophagy and apoptosis in murine melanoma B16F10-Nex2 cells and exhibits antitumor activity in vivo.

Countless efforts have been made to prevent and suppress the formation and spread of melanoma. Natural astaxanthin (AST; extracted from the alga Haematococcus pluvialis) showed an antitumor effect on various cancer cell lines due to its interaction with the cell membrane. This study aimed to characterize the antitumor effect of AST against B16F10-Nex2 murine melanoma cells using cell viability assay and evaluate its mechanism of action using electron microscopy, western blotting analysis, terminal deoxynucleotidyl transferase dUTP nick-end labelling (TUNEL) assay, and mitochondrial membrane potential determination. Astaxanthin exhibited a significant cytotoxic effect in murine melanoma cells with features of apoptosis and autophagy. Astaxanthin also decreased cell migration and invasion in vitro assays at subtoxic concentrations. In addition, assays were conducted in metastatic cancer models in mice where AST significantly decreased the development of pulmonary nodules. In conclusion, AST has cytotoxic effect in melanoma cells and inhibits cell migration and invasion, indicating a promising use in cancer treatment.

Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms.

Machine learning (ML) models have made inroads into chemical sciences, with optimization of chemical reactions and prediction of biologically active molecules being prime examples thereof. These models excel where physical experiments are expensive or time-consuming, for example, due to large scales or the need for materials that are difficult to obtain. Studies of natural products suffer from these issues─this class of small molecules is known for its wealth of structural diversity and wide-ranging biological activities, but their investigation is hindered by poor synthetic accessibility and lack of scalability. To facilitate the evaluation of these molecules, we designed ML models that predict which natural products can interact with a particular target or a relevant pathway. Here, we focused on discovering natural products that are capable of modulating the 5-lipoxygenase (5-LO) pathway that plays key roles in lipid signaling and inflammation. These computational approaches led to the identification of nine natural products that either directly inhibit the activity of the 5-LO enzyme or affect the cellular 5-LO pathway. Further investigation of one of these molecules, deltonin, led us to discover a new cell-type-selective mechanism of action. Our ML approach helped deorphanize natural products as well as shed light on their mechanisms and can be broadly applied to other use cases in chemical biology.

The rise of automated curiosity-driven discoveries in chemistry.

The quest for generating novel chemistry knowledge is critical in scientific advancement, and machine learning (ML) has emerged as an asset in this pursuit. Through interpolation among learned patterns, ML can tackle tasks that were previously deemed demanding to machines. This distinctive capacity of ML provides invaluable aid to bench chemists in their daily work. However, current ML tools are typically designed to prioritize experiments with the highest likelihood of success, i.e., higher predictive confidence. In this perspective, we build on current trends that suggest a future in which ML could be just as beneficial in exploring uncharted search spaces through simulated curiosity. We discuss how low and 'negative' data can catalyse one-/few-shot learning, and how the broader use of curious ML and novelty detection algorithms can propel the next wave of chemical discoveries. We anticipate that ML for curiosity-driven research will help the community overcome potentially biased assumptions and uncover unexpected findings in the chemical sciences at an accelerated pace.

Merging the Isonitrile-Tetrazine (4+1) Cycloaddition and the Ugi Four-Component Reaction into a Single Multicomponent Process.

Multicomponent reactions are of utmost importance at generating a unique, wide, and complex chemical space. Herein we describe a novel multicomponent approach based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction to generate pyrazole amide derivatives. The scope of the reaction as well as mechanistic insights governing the 4H-pyrazol-4-imine tautomerization are provided. This multicomponent process provides access to a new chemical space of pyrazole amide derivatives and offers a tool for peptide modification and stapling.

Segmentation of X-ray coronary angiography with an artificial intelligence deep learning model: Impact in operator visual assessment of coronary stenosis severity.

BACKGROUND: Visual assessment of the percentage diameter stenosis (%DSVE ) of lesions is essential in coronary angiography (CAG) interpretation. We have previously developed an artificial intelligence (AI) model capable of accurate CAG segmentation. We aim to compare operators' %DSVE in angiography versus AI-segmented images. METHODS: Quantitative coronary analysis (QCA) %DS (%DSQCA ) was previously performed in our published validation dataset. Operators were asked to estimate %DSVE of lesions in angiography versus AI-segmented images in separate sessions and differences were assessed using angiography %DSQCA as reference. RESULTS: A total of 123 lesions were included. %DSVE was significantly higher in both the angiography (77% ± 20% vs. 56% ± 13%, p < 0.001) and segmentation groups (59% ± 20% vs. 56% ± 13%, p < 0.001), with a much smaller absolute %DS difference in the latter. For lesions with %DSQCA of 50%-70% (60% ± 5%), an even higher discrepancy was found (angiography: 83% ± 13% vs. 60% ± 5%, p < 0.001; segmentation: 63% ± 15% vs. 60% ± 5%, p < 0.001). Similar, less pronounced, findings were observed for %DSQCA < 50% lesions, but not %DSQCA > 70% lesions. Agreement between %DSQCA /%DSVE across %DSQCA strata (<50%, 50%-70%, >70%) was approximately twice in the segmentation group (60.4% vs. 30.1%; p < 0.001). %DSVE inter-operator differences were smaller with segmentation. CONCLUSION: %DSVE was much less discrepant with segmentation versus angiography. Overestimation of %DSQCA < 70% lesions with angiography was especially common. Segmentation may reduce %DSVE overestimation and thus unwarranted revascularization.

State-of-the-Art Molecular Oncology in Brazil.

Over the past few decades, the life expectancy of humankind has increased significantly due to advancements in life sciences and medical research, particularly given our increasing success in the epidemiological and pharmacological management of bacterial, fungi, and viral infections [...].

The value of multiparametric prediction scores in heart failure varies with the type of follow-up after discharge: a comparative analysis.

AIMS: Multiple prediction score models have been validated to predict major adverse events in patients with heart failure. However, these scores do not include variables related to the type of follow-up. This study aimed to evaluate the impact of a protocol-based follow-up programme of patients with heart failure regarding scores accuracy for predicting hospitalizations and mortality occurring during the first year after hospital discharge. METHODS AND RESULTS: Data from two heart failure populations were collected: one composed of patients included in a protocol-based follow-up programme after an index hospitalization for acute heart failure and a second one-the control group-composed of patients not included in a multidisciplinary HF management programme after discharge. For each patient, the risk of hospitalization and/or mortality within a period of 12 months after discharge was calculated using four different scores: BCN Bio-HF Calculator, COACH Risk Engine, MAGGIC Risk Calculator, and Seattle Heart Failure Model. The accuracy of each score was established using the area under the receiver operating characteristic curve (AUC), calibration graphs, and discordance calculation. AUC comparison was established by the DeLong method. The protocol-based follow-up programme group included 56 patients, and the control group, 106 patients, with no significant differences between groups (median age: 67 years vs. 68.4 years; male sex: 58% vs. 55%; median ejection fraction: 28.2% vs. 30.5%; functional class II: 60.7% vs. 56.2%, I: 30.4% vs. 31.9%; P = not significant). Hospitalization and mortality rates were significantly lower in the protocol-based follow-up programme group (21.4% vs. 54.7%; P < 0.001 and 5.4% vs. 17.9%; P < 0.001, respectively). When applied to the control group, COACH Risk Engine and BCN Bio-HF Calculator had, respectively, good (AUC: 0.835) and reasonable (AUC: 0.712) accuracy to predict hospitalization. There was a significant reduction of COACH Risk Engine accuracy (AUC: 0.572; P = 0.011) and a non-significant accuracy reduction of BCN Bio-HF Calculator (AUC: 0.536; P = 0.1) when applied to the protocol-based follow-up programme group. All scores showed good accuracy to predict 1 year mortality (AUC: 0.863, 0.87, 0.818, and 0.82, respectively) when applied to the control group. However, when applied to the protocol-based follow-up programme group, a significant predictive accuracy reduction of COACH Risk Engine, BCN Bio-HF Calculator, and MAGGIC Risk Calculator (AUC: 0.366, 0.642, and 0.277, P < 0.001, 0.002, and <0.001, respectively) was observed. Seattle Heart Failure Model had non-significant reduction in its acuity (AUC: 0.597; P = 0.24). CONCLUSIONS: The accuracy of the aforementioned scores to predict major events in patients with heart failure is significantly reduced when they are applied to patients included in a multidisciplinary heart failure management programme.

A tribute to Sebastián Cerdán and his key contributions to brain metabolism.

This is a tribute to Sebastián Cerdán, a brilliant and innovative NMR spectroscopist whose studies contributed greatly to the fundamental information to the understanding of brain metabolism, particularly in regard to multinuclear magnetic resonance spectroscopy (MRS) techniques. Sebastián Cerdán sadly passed away in May 2022. He was a wonderful mentor and colleague who will be greatly missed.

A focus on the use of real-world datasets for yield prediction.

The prediction of reaction yields remains a challenging task for machine learning (ML), given the vast search spaces and absence of robust training data. Wiest, Chawla et al. (https://doi.org/10.1039/D2SC06041H) show that a deep learning algorithm performs well on high-throughput experimentation data but surprisingly poorly on real-world, historical data from a pharmaceutical company. The result suggests that there is considerable room for improvement when coupling ML to electronic laboratory notebook data.