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Algorithm portfolios are multi-algorithmic schemes that combine a number of solvers into a joint framework for solving global optimization problems. A crucial part of such schemes is the resource allocation process that is responsible for assigning computational resources to the constituent algorithms. We propose a resource allocation process based on adaptive decision-making procedures. The proposed approach is incorporated in algorithm portfolios composed of three essential types of numerical optimization algorithms, namely gradient-based, direct search, and swarm intelligence algorithms. The designed algorithm portfolios are experimentally demonstrated on a challenging optimization problem for different dimensions and experimental settings. The accompanying statistical analysis offers interesting conclusions and insights on the performance of the algorithm portfolio compared to its constituent algorithms, as well as on the effect of its parameters.

Early detection and evaluation of children at risk of neurodevelopmental disorders and/or communication deficits is critical. While the current literature indicates a high prevalence of neurodevelopmental disorders, many children remain undiagnosed, resulting in missed opportunities for effective interventions that could have had a greater impact if administered earlier. Clinicians face a variety of complications during neurodevelopmental disorders’ evaluation procedures and must elevate their use of digital tools to aid in early detection efficiently. Artificial intelligence enables novelty in taking decisions, classification, and diagnosis. The current research investigates the efficacy of various machine learning approaches on the biometric SmartSpeech datasets. These datasets come from a new innovative system that includes a serious game which gathers children’s responses to specifically designed speech and language activities and their manifestations, intending to assist during the clinical evaluation of neurodevelopmental disorders. The machine learning approaches were used by utilizing the algorithms Radial Basis Function, Neural Network, Deep Learning Neural Networks, and a variation of Grammatical Evolution (GenClass). The most significant results show improved accuracy (%) when using the eye tracking dataset; more specifically: (i) for the class Disorder with GenClass (92.83%), (ii) for the class Autism Spectrum Disorders with Deep Learning Neural Networks layer 4 (86.33%), (iii) for the class Attention Deficit Hyperactivity Disorder with Deep Learning Neural Networks layer 4 (87.44%), (iv) for the class Intellectual Disability with GenClass (86.93%), (v) for the class Specific Learning Disorder with GenClass (88.88%), and (vi) for the class Communication Disorders with GenClass (88.70%). Overall, the results indicated GenClass to be nearly the top competitor, opening up additional probes for future studies toward automatically classifying and assisting clinical assessments for children with neurodevelopmental disorders.

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders.

Gonorrhoea is a prevalent sexually transmitted infection caused by the bacterial pathogen Neisseria gonorrhoeae . N. gonorrhoeae has demonstrated a remarkable capacity to evolve antibiotic resistance, with emerging strains that show resistance to all standard treatment options. The development of new antibiotics for gonorrhoea, especially those with novel targets and no pre-existing resistance, is critical. One such untapped antibacterial target in N. gonorrhoeae is FabI, an enoyl-acyl carrier protein reductase enzyme that is essential for fatty acid biosynthesis in this pathogen. In the current report, structure-based drug design using novel N. gonorrhoeae FabI inhibitor co-crystals guides medicinal chemistry toward increasing potency in the sub-nanomolar range and drives the discovery of Debio 1453. Debio 1453 is optimized for activity against N. gonorrhoeae and is highly active in vitro against diverse N. gonorrhoeae isolates including those resistant to the last remaining treatment options. Additionally, the compound presents a low propensity for selection of mutants with reduced susceptibility. Debio 1453 is efficacious in vivo against N. gonorrhoeae isolates with clinically relevant multi-drug resistance phenotypes in a murine vaginal gonorrhoea infection model underscoring Debio 1453 as a promising candidate for the treatment of gonorrhoea. Debio 1453, a FabI inhibitor, shows potent activity against antibiotic- resistant Neisseria gonorrhoeae , rapidly killing bacteria in vitro and clearing infection in mice, supporting its potential for treating drug-resistant gonorrhoea.

The reduction of human intervention in tuning metaheuristic optimization algorithms has been an ongoing research pursuit. Differential Evolution is a very popular algorithm that counts a large number of variants. However, its efficiency has been shown to depend on the type of its crossover operators (binomial or exponential), mutation operators, as well as on the two parameters that dominate these procedures. Making proper decisions on these parameters has proved to be a laborious, problem-dependent task. We propose a parameter adaptation technique that allows the algorithm to dynamically determine the most suitable crossover type and parameter values during its execution. The technique is based on a search procedure in the discretized parameter search space, using estimations of the algorithm’s performance. The proposed approach is tested and statistically validated on an established high-dimensional test suite. Also, comparisons with other algorithms are reported, verifying the competitiveness of the proposed approach.

Gallbladder cancer (GBC), while infrequent, is the predominant malignancy of the biliary tract. The absence of clinical symptoms in conjunction with aggressive behavior contributes to delayed diagnosis and unfavorable prognosis. This condition typically remains asymptomatic in its initial stages, making detection challenging. Herein, we report a case of adenosquamous carcinoma of the gallbladder in a 74-year-old Caucasian woman who was admitted due to anemia, gastrointestinal bleeding, and abdominal pain. Computed tomography (CT) of the abdomen demonstrated a formation adjacent to the neck of the gallbladder. A lesion with unclear boundaries in the IVb liver segment was also observed. CT-guided percutaneous biopsies were conducted on the thickened wall of the gallbladder and the suspicious area of the liver. The histological report from liver biopsy indicated carcinoma of the gallbladder characterized by squamous differentiation. Τhe case was discussed in an oncology board meeting, and the patient underwent exploratory laparotomy. Intraoperative observations revealed an invasive gallbladder neoplasm extending to the hepatoduodenal junction and the second part of the duodenum. The surgical team performed cholecystostomy and gastrojejunostomy with palliative intent. Gallbladder wall biopsies were obtained intraoperatively and histopathology results confirmed the initial diagnosis of adenosquamous carcinoma. The patient was referred to an oncology board meeting, and it was decided to be a combination of chemotherapy and chemoradiation according to the National Comprehensive Cancer Network (NCCN) guidelines.

Neutrophil extracellular traps (NETs) are a neutrophil-generated extracellular network of chromatin and chromatin-bound molecules with antimicrobial potency. Recent data suggest that NETs are associated with cancer progression and cancer-associated hypercoagulability. Pancreatic adenocarcinoma (PDAC) is a lethal type of cancer in which hypercoagulability and cancer-related thrombosis are among the main complications. In the current report, we summarize the available data on the interplay between NET formation and PDAC development. We conclude that NETs support a dual role during PDAC progression and metastasis. Their formation is on the one hand an important event that shapes the cancer microenvironment to support cancer cell proliferation, invasion and metastasis. On the other hand, NETs may lead to cancer-associated thrombosis. Both mechanisms seem to be dependent on distinct molecular mechanisms that link inflammation to cancer progression. Collectively, NET formation may contribute to the pathogenesis of PDAC, while during cancer development, the proinflammatory environment enables the induction of new NETs and thrombi, forming a vicious cycle. We suggest that targeting NET formation may be an effective mechanism to inhibit both PDAC development and the accompanying hypercoagulability.

The role of immune system components in the development of myocardial remodeling in chronic kidney disease (CKD) and kidney transplantation remains an open question. Our aim was to investigate the associations between immune cell subpopulations in the circulation of CKD patients and kidney transplant recipients (KTRs) with subclinical indices of myocardial performance. We enrolled 44 CKD patients and 38 KTRs without established cardiovascular disease. A selected panel of immune cells was measured by flow cytometry. Classical and novel strain-related indices of ventricular function were measured by speckle-tracking echocardiography at baseline and following dipyridamole infusion. In CKD patients, the left ventricular (LV) relative wall thickness correlated with the CD14++CD16− monocytes (β = 0.447, p = 0.004), while the CD14++CD16+ monocytes were independent correlates of the global radial strain (β = 0.351, p = 0.04). In KTRs, dipyridamole induced changes in global longitudinal strain correlated with CD14++CD16+ monocytes (β = 0.423, p = 0.009) and CD4+ T-cells (β = 0.403, p = 0.01). LV twist and untwist were independently correlated with the CD8+ T-cells (β = 0.405, p = 0.02 and β = −0.367, p = 0.03, respectively) in CKD patients, whereas the CD14++CD16+ monocytes were independent correlates of LV twist and untwist in KTRs (β = 0.405, p = 0.02 and β = −0.367, p = 0.03, respectively). Immune cell subsets independently correlate with left ventricular strain and torsion-related indices in CKD patients and KTRs without established CVD.

Background: Anastomotic leakage (AL) still remains a common complication after colorectal anastomosis that leads to increased morbidity and mortality. The gut microbiota has been hypothesized as one of the risk factors associated with anastomotic leakage. The aim of the present study was to summarize all existing clinical and experimental studies that evaluate the impact of intestinal microbiota on anastomotic leakage after colorectal resection. Methods: The present scoping review was designed according to PRISMA recommendations and a systematic search in Medline, Scopus, EMBASE, Clinicaltrials.gov, Google Scholar, and CENTRAL was conducted until September 2024. Results: Overall, 7 clinical and 5 experimental studies were included. A diminished α-diversity of the gut microbiota in patients suffering from AL was demonstrated. Specific microbe genera, such as Lachnospiraceae, Bacteroidaceae, Bifidobacterium, Acinetobacter, Fusobacterium, Dielma, Elusimicronium, Prevotella, and Faecalibacterium, seem to be associated with AL. However, specific genera, like Prevotella, Streptococcus, Eubacterium, Enterobacteriaceae, Klebsiella, Actinobacteria, Gordonibacter, Phocaeicola, and Ruminococcus2, seem to be protective against AL. Experimental studies highlighted that the Western diet seems to affect microbiota diversity and increases the AL rate, whereas anastomotic healing seems to be impaired by high metalloproteinase production and increased collagenase activity. Conclusions: The intestinal microbiota seems to play an important role in anastomotic leakage after colorectal resection. Specific interventions targeting the microbiota’s composition and the pathophysiological mechanisms by which it impairs anastomotic healing could diminish the risk for anastomotic leakage and improve clinical outcomes. However, future studies should be based on prospective design and eliminate heterogeneity.

Background: Longitudinal surveillance of peritoneal membrane function is crucial in defining patients with a risk of ultrafiltration failure. Long PD is associated with increased low molecular weight solute transport and decreased ultrafiltration and free water transport. Classic PET test only provides information about low molecular solute transport, and the vast majority of longitudinal studies are based on this test and include patients using conventional dialysates. Our aim was to prospectively analyze longitudinal data on peritoneal function in patients on biocompatible solutions using a novel test. Methods: Membrane function data were collected based on uni-PET (a combination of modified and mini PET). A total of 85 patients (age 61.1 ± 15.1 years) with at least one test/year were included. Results: The median follow up was 36 months (21.3, 67.2). A total of 219 PETs were performed. One-way repeated measures ANOVA showed that there were no statistically significant differences over time in ultrafiltration, free water transport, ultrafiltration through small pores, sodium removal, D/D0 and D/PCre in repeated PET-tests. Twenty-three tests revealed ultrafiltration failure in 16 (18.8%) patients. Those patients were longer on PD, had higher D/P creatinine ratios, lower ultrafiltration at one hour with lower free water transport and higher urine volume at baseline. Multivariate analysis revealed that the variation of ultrafiltration over repeated PET-tests independently correlated only with D/Pcreatinine, free water transport and ultrafiltration through small pores. Conclusions. Uni-PET is a combination of two tests that provides more information on the function of the membrane compared with PET. Our study on a PD cohort using only biocompatible solutions revealed that function membrane parameters remained stable over a long time. Ultrafiltration failure was correlated with increased D/P creatinine and decreased free water transport and ultrafiltration through small pores.