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.

Total Synthesis of Mannopeptimycin ß via ß-Hydroxyenduracididine Ligation.

Nonribosomal peptide synthesis in bacteria has endowed cyclic peptides with fascinating structural complexity via incorporating nonproteinogenic amino acids. These bioactive cyclic peptides provide interesting structural motifs for exploring total synthesis and medicinal chemistry studies. Cyclic glycopeptide mannopeptimycins exhibit antibacterial activity against antibiotic-resistant Gram-positive pathogens and act as the lipid II binder to stop bacterial cell wall biosynthesis. Here, we report a strategy streamlining solution phase-solid phase synthesis and chemical ligation-mediated peptide cyclization for the total synthesis of mannopeptimycin ß.

Synaptotagmin Binding to Botulinum Neurotoxins.

Botulinum neurotoxins (BoNTs) are exceptionally toxic proteins that cause paralysis but are also extensively used as treatment for various medical conditions. Most BoNTs bind two receptors on neuronal cells, namely, a ganglioside and a protein receptor. Differences in the sequence between the protein receptors from different species can impact the binding affinity and toxicity of the BoNTs. Here we have investigated how BoNT/B, /DC, and /G, all three toxins that utilize synaptotagmin I and II (Syt-I and Syt-II, respectively) as their protein receptors, bind to Syt-I and -II of mouse/rat, bovine, and human origin by isothermal titration calorimetry analysis. BoNT/G had the highest affinity for human Syt-I, and BoNT/DC had the highest affinity for bovine Syt-II. As expected, BoNT/B, /DC, and /G showed very low levels of binding to human Syt-II. Furthermore, we carried out saturation transfer difference (STD) and STD-TOCSY NMR experiments that revealed the region of the Syt peptide in direct contact with BoNT/G, which demonstrate that BoNT/G recognizes the Syt peptide in a model similar to that in the established BoNT/B-Syt-II complex. Our analyses also revealed that regions outside the Syt peptide's toxin-binding region are important for the helicity of the peptide and, therefore, the binding affinity.

Analysis of hydrophobic and hydrophilic moments of short penetrating peptides for enhancing mitochondrial localization: prediction and validation.

Pharmaceutical interest in targeting mitochondria is increasing because of their contribution in incurable diseases. However, the inner mitochondrial layer represents a major hurdle to overcome for most drugs. Penetrating peptides are a promising strategy for drug delivery, but the absence of standard principles and reliable prediction tools limits the design and discovery of sequences with improved organelle specificity. In our hypothesis, peptide local flexibility represents a valuable source to predict peptide performance. Here, a pool of short nonnatural peptides was designed with the same amino acid content but different positioning. Molecular dynamics and membrane-transfer simulations were used to generate the low-energy conformers in extra, intracellular, and membrane-inserted environments. The contributions of the hydrophobic and hydrophilic side chain-exposed surfaces revealed that the amino acid's relative position significantly affected the simulated peptide's dynamics. Based on the structural versatility, we predicted the peptides' behavior and the sequence with the most efficient membrane penetration and mitochondrial localization. The prediction and the improved performance of our peptides were experimentally confirmed and compared with a reported mitochondrial-targeting sequence. We demonstrated that an accurate understanding of the structural versatility is a valid aid for future works in designing sequences with improved mitochondrial targeting.-Pirisinu, M., Blasco, P., Tian, X., Sen, Y., Bode, A. M., Liu, K., Dong, Z. Analysis of hydrophobic and hydrophilic moments of short penetrating peptides for enhancing mitochondrial localization: prediction and validation.

WAIS-IV Performance in Patients With Schizophrenia.

Wechsler Adult Intelligence Scale (WAIS) is one of the most widely used instruments to measure cognitive functioning. The aims of this study were 1) to obtain the cognitive profile of Spanish patients with schizophrenia on the WAIS-IV; 2) to compare their profile to the profile of a healthy control group; and 3) to compare the cognitive profile of patients with schizophrenia to the performance observed in two separate previous studies in Canada and China. A sample of 99 outpatients and 99 healthy control participants, matched on age, sex, and educational level, were measured using the WAIS-IV, including 10 core subtests, 4 indices, and 2 general intelligence scores, to obtain their cognitive profile. Results showed that only the performance on the Verbal Comprehension Index and its subtests was similar in the patient and control groups. This pattern of cognitive impairment was similar to the pattern reported in the Canadian and Chinese studies.

Conformational Dynamics of the Lipopolysaccharide from Escherichia coli O91 Revealed by Nuclear Magnetic Resonance Spectroscopy and Molecular Simulations.

The outer leaflet of the outer membrane in Gram-negative bacteria contains lipopolysaccharides (LPS) as a major component, and the outer membrane provides a physical barrier and protection against hostile environments. The enterohemorrhagic Escherichia coli of serogroup O91 has an O-antigen polysaccharide (PS) with five sugar residues in the repeating unit (RU), and the herein studied O-antigen PS contains ∼10 RUs. 1H-13C HSQC-NOESY experiments on a 1-13C-labeled PS were employed to deduce 1H-1H cross-relaxation rates and transglycosidic 3JCH related to the ψ torsional angles were obtained by 1H-1H NOESY experiments. Dynamical parameters were calculated from the molecular dynamics (MD) simulations of the PS in solution and compared to those from 13C nuclear magnetic resonance (NMR) relaxation studies. Importantly, the MD simulations can reproduce the dynamical behavior of internal correlation times along the PS chain. Two-dimensional free energy surfaces of glycosidic torsion angles delineate the conformational space available to the O-antigen. Although similar with respect to populated states in solution, the O-antigen in LPS bilayers has more extended chains as a result of spatial limitations due to close packing. Calcium ions are highly abundant in the phosphate-containing core region mediating LPS-LPS association that is crucial for maintaining bilayer integrity, and the negatively charged O-antigen promotes a high concentration of counterbalancing potassium ions. The ensemble of structures present for the PS in solution is captured by the NMR experiments, and the similarities between the O-antigen on its own and as a constituent of the full LPS in a bilayer environment make it possible to realistically describe the LPS conformation and dynamics from the MD simulations.

Direct Enzymatic Branch-End Extension of Glycocluster-Presented Glycans: An Effective Strategy for Programming Glycan Bioactivity.

The sequence of a glycan and its topology of presentation team up to determine the specificity and selectivity of recognition by saccharide receptors (lectins). Structure-activity analysis would be furthered if the glycan part of a glycocluster could be efficiently elaborated in situ while keeping all other parameters constant. By using a bacterial α2,6-sialyltransferase and a small library of bi- to tetravalent glycoclusters, we illustrate the complete conversion of scaffold-presented lactoside units into two different sialylated ligands based on N-acetyl/glycolyl-neuraminic acid incorporation. We assess the ensuing effect on their bioactivity for a plant toxin, and present an analysis of the noncovalent substrate binding contacts that the added sialic acid moiety makes to the lectin. Enzymatic diversification of a scaffold-presented glycan can thus be brought to completion in situ, offering a versatile perspective for rational glycocluster engineering.

Detailed Investigation of the Immunodominant Role of O-Antigen Stoichiometric O-Acetylation as Revealed by Chemical Synthesis, Immunochemistry, Solution Conformation and STD-NMR Spectroscopy for Shigella flexneri†3a.

Shigella flexneri 3a causes bacillary dysentery. Its O-antigen has the {2)-[α-d-Glcp-(1→3)]-α-l-Rhap-(1→2)-α-l-Rhap-(1→3)-[Ac→2]-α-l-Rhap-(1→3)-[Ac→6]≈40 % -ß-d-GlcpNAc-(1→} ([(E)ABAc CAc D]) repeating unit, and the non-O-acetylated equivalent defines S. flexneri X. Propyl hepta-, octa-, and decasaccharides sharing the (E')A'BAc CD(E)A sequence, and their non-O-acetylated analogues were synthesized from a fully protected BAc CD(E)A allyl glycoside. The stepwise introduction of orthogonally protected mono- and disaccharide imidate donors was followed by a two-step deprotection process. Monoclonal antibody binding to twenty-six S. flexneri types 3a and X di- to decasaccharides was studied by an inhibition enzyme-linked immunosorbent assay (ELISA) and STD-NMR spectroscopy. Epitope mapping revealed that the 2C -acetate dominated the recognition by monoclonal IgG and IgM antibodies and that the BAc CD segment was essential for binding. The glucosyl side chain contributed to a lesser extent, albeit increasingly with the chain length. Moreover, tr-NOESY analysis also showed interaction but did not reveal any meaningful conformational change upon antibody binding.

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach.

Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir's clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB's effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.

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.

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.

Molecular recognition of complex-type biantennary N-glycans by protein receptors: a three-dimensional view on epitope selection by NMR.

The current surge in defining glycobiomarkers by applying lectins rekindles interest in definition of the sugar-binding sites of lectins at high resolution. Natural complex-type N-glycans can present more than one potential binding motif, posing the question of the actual mode of interaction when interpreting, for example, lectin array data. By strategically combining N-glycan preparation with saturation-transfer difference NMR and modeling, we illustrate that epitope recognition depends on the structural context of both the sugar and the lectin (here, wheat germ agglutinin and a single hevein domain) and cannot always be predicted from simplified model systems studied in the solid state. We also monitor branch-end substitutions by this strategy and describe a three-dimensional structure that accounts for the accommodation of the α2,6-sialylated terminus of a biantennary N-glycan by viscumin. In addition, we provide a structural explanation for the role of terminal α2,6-sialylation in precluding the interaction of natural N-glycans with lectin from Maackia amurensis . The approach described is thus capable of pinpointing lectin-binding motifs in natural N-glycans and providing detailed structural explanations for lectin selectivity.

Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves.

The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.

A Machine Learning-Based Method for Detecting Liver Fibrosis.

Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.

Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach.

Metabolic Associated Fatty Liver Disease (MASLD) is a condition that is often present in patients with a history of cholecystectomy. This is because both situations share interconnected metabolic pathways. This study aimed to establish a predictive model that allows for the identification of patients at risk of developing hepatic fibrosis following this surgery, with potential implications for surgical decision-making. A retrospective cross-sectional analysis was conducted in four hospitals using a database of 211 patients with MASLD who underwent cholecystectomy. MASLD diagnosis was established through liver biopsy or FibroScan, and non-invasive test scores were included for analysis. Various Machine Learning (ML) methods were employed, with the Adaptive Boosting (Adaboost) system selected to build the predictive model. Platelet level emerged as the most crucial variable in the predictive model, followed by dyslipidemia and type-2 diabetes mellitus. FIB-4 score proved to be the most reliable non-invasive test. The Adaboost algorithm improved the results compared to the other methods, excelling in both accuracy and area under the curve (AUC). Moreover, this system holds promise for implementation in hospitals as a valuable diagnostic support tool. In conclusion, platelet level (<150,000/dL), dyslipidemia, and type-2 diabetes mellitus were identified as primary risk factors for liver fibrosis in MASLD patients following cholecystectomy. FIB-4 score is recommended for decision-making, particularly when the indication for surgery is uncertain. This predictive model offers valuable insights into risk stratification and personalized patient management in post-cholecystectomy MASLD cases.

Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.

Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (SRF). The aim of our study was to provide evidence on predictors of poor outcome in patients with COVID-19 treated with tocilizumab, using machine learning (ML) techniques. We conducted a retrospective study, analyzing the clinical, laboratory and sociodemographic data of patients admitted for severe COVID-19 with SRF, treated with tocilizumab. The extreme gradient boost (XGB) method had the highest balanced accuracy (93.16%). The factors associated with a worse outcome of tocilizumab use in terms of mortality were: baseline situation at the start of tocilizumab treatment requiring invasive mechanical ventilation (IMV), elevated ferritin, lactate dehydrogenase (LDH) and glutamate-pyruvate transaminase (GPT), lymphopenia, and low PaFi [ratio between arterial oxygen pressure and inspired oxygen fraction (PaO2/FiO2)] values. The factors associated with a worse outcome of tocilizumab use in terms of hospital stay were: baseline situation at the start of tocilizumab treatment requiring IMV or supplemental oxygen, elevated levels of ferritin, glutamate-oxaloacetate transaminase (GOT), GPT, C-reactive protein (CRP), LDH, lymphopenia, and low PaFi values. In our study focused on patients with severe COVID-19 treated with tocilizumab, the factors that were weighted most strongly in predicting worse clinical outcome were baseline status at the start of tocilizumab treatment requiring IMV and hyperferritinemia.

eXtreme Gradient Boosting-based method to classify patients with COVID-19.

Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. The aim of this single-center observational study is to classify, based on different types of variables, adult patients with COVID-19 at increased risk of mortality. SARS-CoV-2 infection was defined by a positive reverse transcriptase PCR. A total of 203 patients were admitted between March 15 and June 15, 2020 to a tertiary hospital. Data were extracted from the electronic medical record. Four supervised ML algorithms (k-nearest neighbors (KNN), decision tree (DT), Gaussian naïve Bayes (GNB) and support vector machine (SVM)) were compared with the eXtreme Gradient Boosting (XGB) method proposed to have excellent scalability and high running speed, among other qualities. The results indicate that the XGB method has the best prediction accuracy (92%), high precision (>0.92) and high recall (>0.92). The KNN, SVM and DT approaches present moderate prediction accuracy (>80%), moderate recall (>0.80) and moderate precision (>0.80). The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.

Triazine-pyridine chemistry for protein labelling on tyrosine.

Herein, we discover the new reactivity of the 1,3,5-triazine moiety reacting with a phenol group and report the development of biocompatible and catalyst-free triazine-pyridine chemistry (TPC) for tyrosine labelling under physiological conditions and profiling in the whole proteome. TPC exhibited high tyrosine chemoselectivity in biological systems after cysteine blocking, displayed potential in tyrosine-guided protein labelling, and had bio-compatibility in live cells.

Effect of Lean Red Meat from Beef (Pirenaica Breed) Versus Lean White Meat Consumption on Diet Quality: A Randomized-Controlled Crossover Study in Healthy Young Adults.

A randomized crossover study was carried out in three University accommodation halls. Participants consumed either beef (Pirenaica breed) (PB) or conventional chicken (CC) three times per week for an 8-week periods with their usual diet, each one separated by a 5-week wash out period. Dietary variables were recollected by the Food Frequency Questionnaire (FFQ), and the Diet Quality Index (DQI) was calculated. Forty-seven healthy adults were included (19.9 ± 1.75 years). The inclusion of both types of diets did not modify the components of the DQI, such as the diversity, equilibrium, adequacy and excess. However, when only the first period was analyzed, a significant decrease in the consumption of fruits and vegetables was observed in those participants who received the PB diet (intervention group). The CC diet (control group) significantly reduced the consumption of fish and eggs, total DQI, and DQI quality component. The expected effect was observed in the significant increment of consumption of red meat after the intervention period.

An atomic perspective on improving daptomycin's activity.

BACKGROUND: Recently, through comprehensive medicinal chemistry efforts, we have found a new daptomycin analogue, termed kynomycin, showing enhanced activity against both methicillin-resistant S. aureus and vancomycin-resistant Enterococcus in vitro and in vivo, with improved pharmacokinetics and lower cytotoxicity than daptomycin. METHODS: In this study we compared the physicochemical properties of kynomycin with those of daptomycin from an atomic perspective by using Nuclear Magnetic Resonance spectroscopy and Molecular Dynamics simulations. RESULTS AND CONCLUSION: We observed that kynurenine methylation changes daptomycin's key physicochemical properties; its calcium dependent oligomerization efficiency is improved and the modified kynurenine strengths contacts with the lipid tail and tryptophan residues. In addition, it is observed that, compared to daptomycin, kynomycin tetramer is more stable and binds stronger to calcium. The combined experiments provide key clues for the improved antibacterial activity of kynomycin. GENERAL SIGNIFICANCE: We expect that this approach will help study the calcium binding and oligomerization features of new calcium dependent peptide antibiotics.