Artificial Intelligence (AI) has come a long way in healthcare, having played significant roles in data and information storage and management—such as in patients' medical histories, medicine stocks, sale records, and so on; automated machines; and software and computer applications like diagnostic tools, including MRI radiation technology, CT diagnosis, and many more—all of which have been created to facilitate and simplify healthcare measures. Without a doubt, artificial intelligence (AI) has transformed healthcare over the past few decades to become more effective and efficient, and the pharmaceutical industry is not an exception. AI has had several implications for the pharmaceutical industry. The first sector is Drug Development and Discovery: businesses such as Atomwise accelerate the early phases of drug discovery by using AI for virtual screening, which predicts the behavior of various compounds. The second sector is clinical studies; by identifying suitable participants, forecasting results, and continuously monitoring patient data, artificial intelligence (AI) assists in the design of more effective clinical studies. AI is used, for instance, by IBM Watson Health to match patients with suitable clinical trials by analyzing patient data. The third sector is personalized medicines; AI is used in personalized medicine to customize care based on each patient’s unique genetic profile. Businesses like Tempus help doctors tailor cancer treatment regimens by using AI to evaluate clinical and molecular data. The fourth sector is Supply Chain Management; AI makes the supply chain more efficient by forecasting demand, controlling inventories, and guaranteeing that medications are delivered on time. This lowers expenses and boosts productivity in the pharmaceutical sector. This analysis highlights the advantages and disadvantages of the many AI-based techniques used in pharmaceutical technology.

5G networks have had a major impact on the communication industry in recent years. With its high data transfer rates and improved latency, 5G networks have enabled a range of services such as autonomous vehicles, virtual reality, and the Internet of Things (IoT). As a result of these applications, a massive amount of data is generated every minute. This has created significant issues and considerably impacted network slicing performance. To provide end users with customized network services and improved user experience of 5G network slicing technology, an efficient prediction model is needed for the classification of different types of 5G network slicing. In this paper, we propose a model for predicting the different classes of 5G network slicing. The model utilizes machine algorithms to classify the 5G network into four different slices, eMBB (enhanced mobile broadband), mMTC (massive machine type communications), URLLC (ultra-reliable low-latency communication) and V2X (vehicle-to-everything) slicing. The results obtained indicate that the proposed model is capable of accurately classifying the 5G network slices with an average precision of 0.94% and an average recall of 0.96%. This showcases the effectiveness of our approach. The experimental results indicated that the proposed model could significantly influence the delivery of accurate 5G network slicing services.

Astrophysics is an ideal field in which to take advantage of machine learning (ML) techniques due to the great amount of astronomical data and its peculiar characteristics. The space satellite WISE is considered the current best infrared all-sky survey in both quality and coverage. In contrast, the space satellite Spitzer, with smaller coverage, has better spatial resolution (3x-2x, depending on the band) and sensitivity in the same spectral region. Some studies have claimed to find some kind of noise or contamination in WISE, resulting in discrepancies when comparing the measurements of both satellites. In this communication [1], we intend to overcome these discrepancies and report an ML approach to predict mid-infrared fluxes at two specific bands from WISE variables.

We have tested several ML regression models in a large sample of confirmed members (stars) from open clusters (groups of stars physically related) with both WISE and high-quality Spitzer data. In our particular case, Extremely Randomized Trees gave the best performance with values of R²>0.95 in both bands. Importantly, we have been able to improve the results at lower fluxes (the ones with the largest discrepancies) and to prove the good correspondence between the predicted fluxes and the real Spitzer ones when available.

The use of ML allows us to bring the best characteristics of both satellites together without the loss of data other approaches could cause. We believe a similar strategy could be useful in other studies when dealing with similar discrepancies.

[1] Fonseca-Bonilla et al. 2024, under review.

Microorganisms belonging to the Mycobacterium tuberculosis (MTB) complex cause tuberculosis (TB), a contagious respiratory illness. While MTB is mainly associated with lung infections, it has the potential to cause illness in several other organs and tissues. MTB infection can progress from a state of containment within the host, where the bacteria remain confined to granulomas (latent TB infection), to a communicable stage, marked by the manifestation of symptoms. Recently, there have been worries over novel strains of MTB and multidrug-resistant tuberculosis. Consequently, experts have expressed worries regarding efforts to suppress MTB as a means to enhance healthcare administration and avert TB. This study aims to characterize the uncharacterized HTH-type transcriptional regulator Rv0681 protein, examine its physicochemical properties, investigate protein--protein interactions, document functional annotations, anticipate its structure, and design a computational drug to prevent potential protein infections. The instability index has identified this protein as stable. The protein is predicted to be involved in the transcriptional regulation of the TetR family. It has an HTH-type TetR domain. Gene ontology studies demonstrated that this protein is involved in both molecular and biological processes. The enzyme and pathway databases indicate that this protein participates in a reaction that phosphorylates Rv0681, resulting in the production of phosphorylated Rv0681 and ADP. To predict the 3D structure of the protein, three different servers were employed and were used to compare the outcomes, with AlphaFold being documented as the best structure-predicting server. Maestro software was used to perform molecular docking between the drug molecule (proflavine) and the selected protein, resulting in a docking energy of -8.151 kcal/mol.

Levodopa, or L-3,4-dihydroxyphenylalanine, is an amino acid directly obtained from phenylalanine and a medicine widely used for the effective treatment of Parkinson's disease. The main aim of this work was to clearly highlight and develop two quantitative synthesis methods of two new azo dyes possessing chromophore groups, which were obtained by the color reactions of Levodopa with two main chromogenic agents—alpha-naphthol 0,1% and beta-naphthol 0,1 % alcoholic alkalinized solutions—in the presence of NaNO₂ 5% and HCl 15%-20%. Following the first color reaction of Levodopa with alpha-naphthol, an intensely bright yellow azo dye was quantitatively obtained and showed a maximum absorption at λ = 418 nm. In the second case, as a result of the second quantitative reaction of Levodopa with beta-naphthol, an intense orange azo dye with a reddish tint at λ = 478 nm was synthesized. Both azo dyes quantitatively obtained from Levodopa had the azo- -N=N- reactive groups as color-generating chromophores directly assigned to the signaling moieties of two chemical sensors. In the first reaction, the azo group was directly linked to alpha-naphthol, and in the second reaction, the azo group was linked to the beta-naphthol aromatic cycle. An important objective of this research was to accurately quantitatively analyze Levodopa in a pharmaceutical following these two new color reactions of L-Dopa with alpha-naphthol 0,1% and beta-naphthol 0,1 %. According to these reactions, two new azo dyes were quantitatively obtained from Levodopa and could be spectrophotometrically dosed at λ = 418 nm for the intensely bright yellow azo dye and λ = 478 nm for the intense orange azo dye with a reddish tint. Through the spectrophotometric quantitative analysis of the two azo dyes formed at λ = 418 nm and λ = 478 nm, it was possible to successfully and accurately quantify Levodopa in a pharmaceutical.

Background: Alzheimer's disease is a progressive neurological disorder that primarily affects memory and cognitive function, mostly affecting the elderly population. Although there are currently no disease-modifying treatments for such neurological disorders, there are a number of ways to reduce the risk of Alzheimer’s through appropriate diagnosis and by using of natural plant products.

Aim(s): The goal of this research study was to see how the main plant phytoconstituents (Acteoside) overcome Alzheimer’s type dementia in rodents by activating the cholinergic system, anti-oxidants, and the protection of neuronal death in the hippocampus region of the brain.

Methods: Investigating the extraction method initially, followed by an in vitro and in vivo investigation in rodent models, and a phytoconstituents analysis using a variety of analytical techniques. Numerous criteria, including behavioral, biochemical, and histological examination, are examined during rodent modeling, using different groups. Subsequently, a standard group including a marketed formulation was used to assess each group.

Results: The hot continuous percolation (Soxhlet) method is used in the preliminary evaluation to determine the percentage yield, which measures 14.10%. Strong antioxidant properties are also shown by the plant extract in the early stages.

Conclusion: The current study suggested that the plant extract in in vivo experiments to prevent related oxidative stress-mediated problems. Further studies are needed to explore the potential medicinal applications of this plant.

The emergence of bispecific antibodies (BsAbs) represents a groundbreaking advancement in oncological therapeutics, offering innovative approaches for precision cancer immunotherapy. These specialized antibodies are engineered to bind simultaneously to two distinct antigens, thereby facilitating dual-targeting strategies that enhance therapeutic precision and efficacy. This dual engagement not only improves the targeting of malignant cells but also addresses limitations observed with conventional monoclonal antibodies. One of the most compelling features of BsAbs is their capacity to modulate the immune system in a more sophisticated manner. By simultaneously interacting with tumor-associated antigens and immune cell receptors, BsAbs orchestrate a more potent and directed immune response against cancer cells. This immune modulation addresses the challenge of immune evasion employed by tumors, potentially leading to improved therapeutic outcomes. Enhanced tumor selectivity is another significant advantage of bispecific antibodies. By directing therapeutic activity towards specific tumor antigens, BsAbs minimize off-target effects and reduce collateral damage to normal tissues, thereby improving the safety and efficacy profiles of cancer treatments. This selectivity is further refined by cutting-edge linker technologies that optimize the stability, pharmacokinetics, and overall performance of BsAbs. The dynamic nature of the adaptive immune engagement enabled by BsAbs facilitates interactions with various immune system components, including T cells and natural killer (NK) cells. This review delves into the innovative landscape of bispecific antibodies in oncology, emphasizing their dual-targeting capabilities, immune modulation, and tumor selectivity, and the role of advanced linker technologies. By examining these advancements, this review aims to illuminate the transformative potential of BsAbs in advancing precision cancer immunotherapy and improving patient outcomes.

Diabetic ulcers are often worsened by elevated levels of human neutrophil elastase (HNE) and bacterial infections, both of which impede healing. To address these challenges, polycaprolactone (PCL)/polyethylene glycol (PEG) electrospun fibers infused with elastase-targeting peptides, AAPV and WAAPV, were engineered. The fibers were designed to exert multiple actions, including the inhibition of HNE. WAAPV's effectiveness in regulating proteolytic enzymes was verified by its ability in inhibiting HNE activity. The incorporation of PEG into the fibers enhanced their wettability, although it also accelerated degradation. However, the inclusion of WAAPV mitigated this effect, resulting in a sustained release of peptides over 24 hours.

Peptide loading within the fibers was confirmed through thermal stability and hydration capacity analyses, and the peptide concentrations were determined by mass/dimension ratios in approximately 51.1 μg/cm² and 46.0 μg/cm² for AAPV, and 48.5 μg/cm² and 51.3 μg/cm² for WAAPV, within PCL and PCL/PEG matrices, respectively. Both peptides effectively inhibited HNE, with PEG showing potential to enhance this inhibition by interacting with the peptides and forming peptide-PEG complexes. The fibers containing PCL and peptides achieved approximately 10% HNE inhibition after 6 hours of incubation, while PCL/PEG fibers showed ≈ 20% inhibition after 4 hours testing.

Peptide-loaded fibers demonstrated significant antibacterial activity, inhibiting the growth of Staphylococcus aureus by up to 78% and Escherichia coli by up to 66%, with peak efficacy observed after 4 and 2 hours of incubation, respectively. These findings suggest that WAAPV-loaded fibers hold promise for inhibiting HNE and bacterial activity, and thus for treating diabetic ulcers.

Microalgae are cultivated for a broad range of applications, from food to cosmetics, and from biofuel to biotechnology. While usually grown in suspension in bioreactors, this technique poses several challenges regarding processing, especially with respect to illumination, and harvesting. Alternatively, some microalgae can be cultivated on suitable substrates in the form of a biofilm to solve the problem of harvesting. In this case, however, a constant and sufficient light intensity on the microalgae without overheating the medium is still problematic. A previous project has thus investigated the possibility of letting green microalgae grow on a suitable substrate outside a bioreactor, i.e., in the air, under regular wetting, which would improve illumination and ease harvesting. Here, we report the growth of the green microalgae Chlorella vulgaris, which is often used as dietary supplement or for cosmetics on textile substrates outside a bioreactor. We show how the first setup was improved in terms of fixing the textile, ensuring regular watering and increasing the available light on the substrates. Our comparison between C. vulgaris grown on identical textile fabrics in a bioreactor, in a petri dish, and in the air show that the latter leads to a significantly higher microalgae growth than both the more common methods, and that cultivation of microalgae at the air should thus be optimized further in future studies.

Background:
Ibrutinib, a small-molecule drug inhibiting Bruton’s tyrosine kinase, is widely used for treating B-cell lymphoma. However, its potential cardiac toxicity is not fully understood. This study aims to examine how different concentrations of Ibrutinib affect cardiac electrophysiological properties.

Methods:
Using an in-silico electrophysiological model of the sinoatrial node (SAN), we analyzed the effects of Ibrutinib (ranging from 0.1 µmol/L to 10 µmol/L) on the conductance of voltage-gated sodium channels (Nav1.5) over a 200 ms period. Electrophysiological activities were recorded using both current-clamp and voltage-clamp techniques.

Results:
Application of varying current stimuli (0.1-0.10 nA) and durations (10-50 ms) generated action potentials (AP) in the SAN. The current-voltage (I-V) relationship of Nav1.5 under different Ibrutinib concentrations demonstrated a significant reduction in inward current, with a 26% decrease at 10 µmol/L. The I-V curve shifted positively by 20%, and the half-activation potential increased by 28%. This change in inward current was then integrated into a whole-cell model, revealing prolonged AP repolarization and decreased firing frequency at 10 µmol/L Ibrutinib.

Conclusions:
Our findings indicate that high concentrations of Ibrutinib reduce the frequency of spontaneous AP firing by inhibiting Nav1.5 currents, suggesting a risk of cardiac toxicity. Careful dosage management of Ibrutinib is recommended, and further clinical trials are needed to explore its detailed subcellular mechanisms.