The explicit bonding reaction ensemble Monte Carlo method.

We present the explicit bonding Reaction ensemble Monte Carlo (eb-RxMC) method, designed to sample reversible bonding reactions in macromolecular systems in thermodynamic equilibrium. Our eb-RxMC method is based on the reaction ensemble method; however, its implementation differs from the latter by the representation of the reaction. In the eb-RxMC implementation, we are adding or deleting bonds between existing particles, instead of inserting or deleting particles with different chemical identities. This new implementation makes the eb-RxMC method suitable for simulating the formation of reversible linkages between macromolecules, which would not be feasible with the original implementation. To enable coupling of our eb-RxMC algorithm with molecular dynamics algorithm for the sampling of the configuration space, we biased the sampling of reactions only within a certain inclusion radius. We validated our algorithm using a set of ideally behaving systems undergoing dimerization and polycondensation reactions, for which analytical results are available. For dimerization reactions with various equilibrium constants and initial compositions, the degree of conversion measured in our simulations perfectly matched the reference values given by the analytical equations. We also showed that this agreement is not affected by the arbitrary choice of the inclusion radius or the stiffness of the harmonic bond potential. Next, we showed that our simulations can correctly match the analytical results for the distribution of the degree of polymerization and end-to-end distance of ideal chains in polycondensation reactions. Altogether, we demonstrated that our eb-RxMC simulations correctly sample both reaction and configuration spaces of these reference systems, opening the door to future simulations of more complex interacting macromolecular systems.

Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance.

Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods.

pyMBE: The Python-based molecule builder for ESPResSo.

We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.

Closed-loop baroreflex model with biophysically detailed afferent pathway.

In this work, we couple a lumped-parameter closed-loop model of the cardiovascular system with a physiologically-detailed mathematical description of the baroreflex afferent pathway. The model features a classical Hodgkin-Huxley current-type model for the baroreflex afferent limb (primary neuron) and for the second-order neuron in the central nervous system. The pulsatile arterial wall distension triggers a frequency-modulated sequence of action potentials at the afferent neuron. This signal is then integrated at the brainstem neuron model. The efferent limb, representing the sympathetic and parasympathetic nervous system, is described as a transfer function acting on heart and blood vessel model parameters in order to control arterial pressure. Three in silico experiments are shown here: a step increase in the aortic pressure to evaluate the functionality of the reflex arch, a hemorrhagic episode and an infusion simulation. Through this model, it is possible to study the biophysical dynamics of the ionic currents proposed for the afferent limb components of the baroreflex during the cardiac cycle, and the way in which currents dynamics affect the cardiovascular function. Moreover, this system can be further developed to study in detail each baroreflex loop component, helping to unveil the mechanisms involved in the cardiovascular afferent information processing.

Standards for quantitative assessments by coronary computed tomography angiography (CCTA): An expert consensus document of the society of cardiovascular computed tomography (SCCT).

In current clinical practice, qualitative or semi-quantitative measures are primarily used to report coronary artery disease on cardiac CT. With advancements in cardiac CT technology and automated post-processing tools, quantitative measures of coronary disease severity have become more broadly available. Quantitative coronary CT angiography has great potential value for clinical management of patients, but also for research. This document aims to provide definitions and standards for the performance and reporting of quantitative measures of coronary artery disease by cardiac CT.

Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques.

Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.

Charge Regulation Triggers Condensation of Short Oligopeptides to Polyelectrolytes.

Electrostatic interactions between charged macromolecules are ubiquitous in biological systems, and they are important also in materials design. Attraction between oppositely charged molecules is often interpreted as if the molecules had a fixed charge, which is not affected by their interaction. Less commonly, charge regulation is invoked to interpret such interactions, i.e., a change of the charge state in response to a change of the local environment. Although some theoretical and simulation studies suggest that charge regulation plays an important role in intermolecular interactions, experimental evidence supporting such a view is very scarce. In the current study, we used a model system, composed of a long polyanion interacting with cationic oligolysines, containing up to 8 lysine residues. We showed using both simulations and experiments that while these lysines are only weakly charged in the absence of the polyanion, they charge up and condense on the polycations if the pH is close to the pKa of the lysine side chains. We show that the lysines coexist in two distinct populations within the same solution: (1) practically nonionized and free in solution; (2) highly ionized and condensed on the polyanion. Using this model system, we demonstrate under what conditions charge regulation plays a significant role in the interactions of oppositely charged macromolecules and generalize our findings beyond the specific system used here.

Composition of the microbiota in patients with growth hormone deficiency before and after treatment with growth hormone.

INTRODUCTION: Growth hormone (GH) and insulin-like growth factor-1 (IGF-1) have modulatory effects on bowel function and its microbiota. Our aim was to investigate whether low levels of GH and IGF-1 in patients with GH deficiency are associated with changes in gut physiology/integrity as well as in the composition of the gut microbiota. MATERIALS AND METHODS: We conducted a case-control study in 21 patients with GH deficiency, at baseline and after 6 months of GH treatment, and in 20 healthy controls. We analysed changes in anthropometric and laboratory characteristics and bacterial translocation and studied the composition of the microbiome by means of massive 16S rRNA gene sequencing. RESULTS: Growth hormone deficiency was accompanied by a significant increase in serum levels of sCD14, a marker of bacterial translocation (P < .01). This increase was reversed by GH treatment. We did not find any differences in the composition or α- or ß-diversity of the gut microbiota after treatment or between cases and controls. CONCLUSIONS: Our work is the first to demonstrate that the presence of GH deficiency is not associated with differences in gut microbiota composition in comparison with healthy controls, and changes in microbiota composition are also not found after 6 months of treatment. However, GH deficiency and low IGF-1 levels were associated with an increase in bacterial translocation, which had reversed after treatment.

Nasal septum-derived chondroprogenitor cells control mandibular condylar resorption consequent to orthognathic surgery: a clinical trial.

Condylar resorption is an aggressive and disability form of temporomandibular joint (TMJ) degenerative disease, usually non-respondent to conservative or minimally invasive therapies and often leading to surgical intervention and prostheses implantation. This condition is also one of the most dreaded postoperative complications of orthognathic surgery, with severe cartilage erosion and loss of subchondral bone volume and mineral density, associated with a painful or not inflammatory processes. Because regenerative medicine has emerged as an alternative for orthopedic cases with advanced degenerative joint disease, we conducted a phase I/IIa clinical trial (U1111-1194-6997) to evaluate the safety and efficacy of autologous nasal septal chondroprogenitor cells. Ten participants underwent biopsy of the nasal septum cartilage during their orthognathic surgery. The harvested cells were cultured in vitro and analyzed for viability, presence of phenotype markers for mesenchymal stem and/or chondroprogenitor cells, and the potential to differentiate into chondrocytes, adipocytes, and osteoblasts. After the intra-articular injection of the cell therapy, clinical follow-up was performed using the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) and computed tomography (CT) images. No serious adverse events related to the cell therapy injection were observed during the 12-month follow-up period. It was found that autologous chondroprogenitors reduced arthralgia, promoted stabilization of mandibular function and condylar volume, and regeneration of condylar tissues. This study demonstrates that chondroprogenitor cells from the nasal septum may be a promise strategy for the treatment of temporomandibular degenerative joint disease that do not respond to other conservative therapies.

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients.

Implicancias de la inteligencia artificial en los métodos de imagen endovascular / Implications of Artificial Intelligence in Intravascular Imaging Methods

RESUMEN La angioplastia transluminal coronaria (ATC) es una de las principales estrategias de revascularización en pacientes con enfermedad coronaria aterosclerótica (ECA). Numerosos estudios respaldan la optimización de la ATC mediante métodos de imagen endovascular; sin embargo, estos métodos son subutilizados en la práctica clínica contemporánea y enfrentan desafíos en la interpretación de los datos obtenidos, por lo que la integración de la inteligencia artificial (IA) se vislumbra como una solución atractiva para promover y simplificar su uso. La IA se define como un programa computarizado que imita la capacidad del cerebro humano para recopilar y procesar datos. El aprendizaje de máquinas es una subdisciplina de la IA que implica la creación de algoritmos capaces de analizar grandes conjuntos de datos sin suposiciones previas, mientras que el aprendizaje profundo se centra en la construcción y entrenamiento de redes neuronales artificiales profundas y complejas. Así, se ha demostrado que la incorporación de sistemas de IA a los métodos de imagen endovascular incrementa la precisión de la ATC, disminuye el tiempo del procedimiento y la variabilidad interobservador en la interpretación de los datos obtenidos, promueve así una mayor adopción y facilita su utilización. El propósito de la presente revisión es destacar cómo los sistemas actuales basados en IA pueden desempeñar un papel fundamental en la interpretación de los datos generados por los métodos de imagen endovascular, lo que conduce a una mejora en la optimización de la ATC en pacientes con ECA.

ABSTRACT Percutaneous coronary intervention (PCI) is one of the primary revascularization strategies in patients with coronary artery disease (CAD). Several studies support the use of intravascular imaging methods to optimize PCI. However, these methods are underutilized in contemporary clinical practice and face challenges in data interpretation. Therefore, the incorporation of artificial intelligence (AI) is seen as an attractive solution to promote and simplify their use. AI can be defined as a computer program that mimics the human brain in its ability to collect and process data. Machine learning is a sub-discipline of AI that involves the creation of algorithms capable of analyzing large datasets without making prior assumptions, while deep learning focuses on the construction and training of deep and complex artificial neural networks. The incorporation of AI systems to intravascular imaging methods improves the accuracy of PCI, reduces procedure duration, and minimizes interobserver variability in data interpretation. This promotes their wider adoption and facilitates their use. The aim of this review is to highlight how current AI-based systems can play a key role in the interpretation of data generated by intravascular imaging methods and optimize PCI in patients with CAD.

Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence.

BACKGROUND: Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. METHODS: In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. RESULTS: Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). CONCLUSIONS: The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.

Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver tumor and is associated with high mortality rates. Approximately 80% of cases occur in cirrhotic livers, posing a significant challenge for appropriate therapeutic management. Adequate screening programs in high-risk groups are essential for early-stage detection. The extent of extrahepatic tumor spread and hepatic functional reserve are recognized as two of the most influential prognostic factors. In this retrospective multicenter study, we utilized machine learning (ML) methods to analyze predictors of mortality at the time of diagnosis in a total of 208 patients. The eXtreme gradient boosting (XGB) method achieved the highest values in identifying key prognostic factors for HCC at diagnosis. The etiology of HCC was found to be the variable most strongly associated with a poorer prognosis. The widely used Barcelona Clinic Liver Cancer (BCLC) classification in our setting demonstrated superiority over the TNM classification. Although alpha-fetoprotein (AFP) remains the most commonly used biological marker, elevated levels did not correlate with reduced survival. Our findings suggest the need to explore new prognostic biomarkers for individualized management of these patients.

Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression.

Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline's plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

Comparison of endothelial shear stress between ultrathin strut bioresorbable polymer drug-eluting stent vs durable-polymer drug-eluting stent post-stent implantation: An optical coherence tomography substudy from BIOFLOW II.

BACKGROUND: Recent clinical data indicate a different performance of biodegradable polymer (BP)-drug eluting stent (DES) compared to durable polymer (DP)-DES. Whether this can be explained by a beneficial impact of BP-DES stent design on the local hemodynamic forces distribution remains unclear. OBJECTIVES: To compare endothelial shear stress (ESS) distribution after implantation of ultrathin (us) BP-DES and DP-DES and examine the association between ESS and neointimal thickness (NIT) distribution in the two devices at 9 months follow up. METHODS AND RESULTS: We retrospectively identified patients from the BIOFLOW II trial that had undergone OCT imaging. OCT data were utilized to reconstruct the surface of the stented segment at baseline and 9 months follow-up, simulate blood flow, and measure ESS and NIT in the stented segment. The patients were divided into 3 groups depending on whether DP-DES (N = 8, n = 56,160 sectors), BP-DES with a stent diameter of >3 mm (strut thickness of 80 µm, N = 6, n = 36,504 sectors), or BP-DES with a stent diameter of ≤3 mm (strut thickness of 60 µm, N = 8, n = 50,040 sectors) were used for treatment. The ESS, and NIT distribution and the association of these two variables were estimated and compared among the 3 groups. RESULTS: In the DP-DES group mean NIT was 0.18 ± 0.17 mm and ESS 1.68 ± 1.66 Pa; for the BP-DES ≤3 mm group the NIT was 0.17 ± 0.11 mm and ESS 1.49 ± 1.24 Pa and for the BP-DES >3 mm group 0.20 ± 0.23 mm and 1.42 ± 1.24 Pa respectively (p < 0.001 for both NIT and ESS comparisons across groups). A negative correlation between NIT and baseline ESS was found, the correlation coefficient for all the stented segments was -0.33, p < 0.001. CONCLUSION: In this OCT sub-study of the BIOFLOW II trial, the NIT was statistically different between groups of patients treated with BP-DES and DP-DES. In addition, regions of low ESS were associated with increased NIT in all studied devices.

Acute Type A Aortic Dissection Diagnosed by POCUS in a 29-year-old Man.

Aortic dissection (AD) is a medical emergency with a poor prognosis if not recognized early and treated promptly. In this setting, clinical data may be equivocal, while electrocardiogram, laboratory tests, and chest radiography often show nonspecific findings. In contrast, cardiac point of care ultrasound (POCUS) has proven useful in the diagnosis and detection of complications of AD. We present the case of a 29-year-old man with marfanoid habitus presenting with chest pain and acute heart failure, in whom cardiac POCUS aided in the rapid diagnosis of type A AD and pulmonary edema. POCUS contributed to optimizing the medical treatment and allowed for early activation of the surgical team.

Ultrasonography and procedures in intensive care medicine.

The use of point-of-care ultrasonography (POCUS) is not limited to the diagnosis and/or monitoring of critically ill patients. Further, ultrasound guidance is of paramount relevance to aid in successfully and safely performing several procedures in the intensive care unit (ICU). In this article, we review the role of POCUS as a procedural guidance in the ICU. Core procedures include, but are not limited to, vascular cannulation, pericardiocentesis, thoracentesis, paracentesis, aspiration of soft-tissue collections/arthrocentesis and lumbar puncture. With time, the procedures performed by intensivists may extend beyond the core competencies depicted in this review. Ultrasound guidance should be part of the intensivist's competencies, for which appropriate training should be achieved.

Non-touch treatment to solve the persistent contamination of culture plates with Aspergillus niger in a clinical microbiology laboratory of Spain.

A pseudo-outbreak of Aspergillus caused by false positive cultures can have a high sanitary impact. We determined the effectiveness (fungal load elimination) of a non-touch disinfection system, vs conventional disinfection methods, to solve steady contamination of culture plates with Aspergillus niger at a clinical microbiology laboratory. Routine cleaning-disinfection (RCD), intensive cleaning-disinfection (ICD), and terminal airborne disinfection (TAD) were employed in stages. Air sampling was carried out before and after each procedure. The effectiveness of TAD on contact surfaces was tested by surface sampling. After RCD, ICD, and TAD, there was a mean decrease of 5.4 (95% CI = 1.8-9.0), 19.2 (95% CI = 12.4-26.0), and 4.4 (95% CI = 2.5-6.3) CFU per tested area, and 46.2%, 21.7%, and 95.5% of contaminated areas became sterile, respectively. There was a mean decrease of 30.6 CFU per tested surface (p < 0.0007) and 50% of tested surfaces became sterile. Global effectiveness of RCD, ICD, and TAD was 68.8% (95% CI = 68.5-69.1), 82.2% (95% CI = 82.1-82.3), and 99.0% (95% CI = 98.8-99.2), respectively. The effectiveness was higher with TAD (4.1 CFU/cm2 less than with ICD; p = 0.0290). No further contamination has occurred since then. When construction and renovation activities are discarded and RCD and ICD practices are insufficient, non-touch disinfection remove residual dust contamination and avoid recurrence.

Beyond the effects of HIV infection and integrase inhibitors-based therapies on oral bacteriome.

Oral microbiome is the second largest microbial community in humans after gut. Human immunodeficiency virus (HIV) infection triggers an impairment of the immune system which could favour the growth and the colonization of pathogens in the oral cavity, and this dysbiosis has been associated with oral manifestations that worsen the quality of life of these patients. Antiretroviral therapy (ART) could also drive changes in specific oral bacterial taxa associated with such periodontal diseases. Integrase strand transfer inhibitors (INSTIs), therapy of choice in the treatment of naive HIV-patients, are able to reverse the impact of HIV infection on systemic inflammation, gut permeability, and gut bacterial diversity/richness. The objective of this study was to analyse the effects of HIV infection per se and INSTIs on salivary bacteriome composition, taking into consideration other factors such as smoking, that could also have a significant impact on oral microbiome. To accomplish this objective, 26 non-HIV-infected volunteers and 30 HIV-infected patients (15 naive and 15 under INSTIs-regimen) were recruited. Salivary samples were collected to measure lysozyme levels. Oral bacteriome composition was analysed using 16S rRNA gene sequencing. Naive HIV-infected patients showed statistically higher levels of lysozyme compared to controls (p < 0.001) and INSTIs-treated patients (p < 0.05). Our study was unable to detect differences in α nor ß-diversity among the three groups analysed, although significant differences in the abundance of some bacterial taxonomical orders were detected (higher abundance in the phylum Pseudomonadota, in the order Acholeplasmatales, and in the genera Ezakiella and Acholeplasma in the naive group compared to controls; and higher abundance in the phylum Mycoplasmatota, in the order Acholeplasmatales, and in the genera Acholeplasma and uncultured Eubacteriaceae bacterium in the INTIs-treated HIV-infected patients compared to controls). These differences seem to be partially independent of smoking habit. HIV infection and INSTIs effects on oral microbiota seem not to be very potent, probably due to the modulation of other factors such as smoking and the greatest outward exposure of the oral cavity.