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The cultural and professional growth of a physician is a long process, spanning over more than 10 years [...]

In the last decades, the development of PET/CT radiopharmaceuticals, targeting the Prostate-Specific Membrane Antigen (PSMA), changed the management of prostate cancer (PCa) patients thanks to its higher diagnostic accuracy in comparison with conventional imaging both in staging and in recurrence. Alongside molecular imaging, PSMA was studied as a therapeutic agent targeted with various isotopes. In 2021, results from the VISION trial led to the Food and Drug Administration (FDA) approval of [177Lu]Lu-PSMA-617 as a novel therapy for metastatic castration-resistant prostate cancer (mCRPC) and set the basis for a radical change in the future perspectives of PCa treatment and the history of Nuclear Medicine. Despite these promising results, primary resistance in patients treated with single-agent [177Lu]Lu-PSMA-617 remains a real issue. Emerging trials are investigating the use of [177Lu]Lu-PSMA-617 in combination with other PCa therapies in order to cover the multiple oncologic resistance pathways and to overcome tumor heterogeneity. In this review, our aim is to retrace the history of PSMA-targeted therapy from the first preclinical studies to its future applications in PCa.

Radical enactivism supports radical embodied cognition (REC), which is the idea that basic or fundamental cognition (perception and action) does not need to be understood in representational, contentful terms. REC departs from the idea that the mind can be naturalized through biological functions, but rejects the idea that mental content, which is understood as having a representational nature, can be naturalized. For REC, the natural origins of content (or NOC) is a program based on the following hypothesis: first, we depart from basic cognitive processes that are target-based and guided by an Ur-intentionality or directedness toward the world, and then sociality enters in the picture when language appears into the scene, allowing for establishing full-blown semantic content in which that content is about worldly states of affairs. Here, I am going to focus on the phenomenon of directedness since there are blind spots in this picture: as many authors claim, REC takes Ur-intentionality as the starting point, but there is simply no explanation to date of how this directedness or Ur-intentionality is established. Therefore, how could we account for Ur-intentionality? How does this kind of intentionality emerge? We believe that we can answer this question if we invoke the best scientific evidence from ecological perceptual learning especially in regard to the role of the environment and the information for perceiving affordances in our learning processes. This allows us to offer an answer to the question of how the most basic form of cognition (Ur-intentionality or directedness) emerges in nature.

Mangrove ecosystems are crucial for biodiversity and coastal protection but face threats from climate change and human activities. This review assesses the productivity of the Matang Mangrove Forest Reserve (MMFR) in Malaysia, which is recognised as one of the best-managed mangrove forests, while also addressing challenges such as deforestation and climate change-induced factors. This review explores the concept of productivity in mangrove forests, highlighting their role in carbon sequestration and discussing litterfall measurements as fundamental metrics for assessing primary productivity. An analysis of historical changes in MMFR’s biomass and productivity revealed fluctuations influenced by logging, reforestation, and climatic conditions. Trends in MMFR productivity indicate a concerning decline attributed to anthropogenic activities such as aquaculture and industrial projects. A regression analysis conducted on Rhizophora apiculata data with age as the predictor and AGB as the response variable indicated a positive trend (slope = 3.61, R-squared = 0.686), suggesting a quantitative increase in AGB with age. Further analysis revealed a significant negative trend in MMFR’s overall productivity over years (coefficient = −3.974, p < 0.05) with a strong inverse relationship (rho = −0.818, p < 0.05), indicating declining AGB trends. Despite these challenges, this review underscores the significance of sustainable management practices, effective conservation efforts, and community engagement in maintaining mangrove ecosystem health and productivity. In conclusion, sharing management lessons from MMFR can contribute to global conservation and sustainable mangrove forest management efforts, fostering resilience in these vital ecosystems.

Success in integrating artificial intelligence (AI) in anesthesia depends on collaboration with anesthesiologists, respecting their expertise, and understanding their opinions. The aim of this study was to illustrate the confidence in AI integration in perioperative anesthetic care among Jordanian anesthesiologists and anesthesia residents working at tertiary teaching hospitals. This cross-sectional study was conducted via self-administered online questionnaire and includes 118 responses from 44 anesthesiologists and 74 anesthesia residents. We used a five-point Likert scale to investigate the confidence in AI’s role in different aspects of the perioperative period. A significant difference was found between anesthesiologists and anesthesia residents in confidence in the role of AI in operating room logistics and management, with an average score of 3.6 ± 1.3 among residents compared to 2.9 ± 1.4 among specialists (p = 0.012). The role of AI in event prediction under anesthesia scored 3.5 ± 1.4 among residents compared to 2.9 ± 1.4 among specialists (p = 0.032) and the role of AI in decision-making in anesthetic complications 3.3 ± 1.4 among residents and 2.8 ± 1.4 among specialists (p = 0.034). Also, 65 (55.1%) were concerned that the integration of AI will lead to less human–human interaction, while 81 (68.6%) believed that AI-based technology will lead to more adherence to guidelines. In conclusion, AI has the potential to be a revolutionary tool in anesthesia, and hesitancy towards increased dependency on this technology is decreasing with newer generations of practitioners.

The research used polyethersulfone (PES) as a membrane material, polyvinylpyrrolidone (PVP) k30 and polyethylene glycol 400 (PEG 400) as water-soluble additives, and dimethylacetamide (DMAc) as a solvent to prepare hollow-fiber ultrafiltration membranes through a nonsolvent-induced phase separation (NIPS) process. The hydrophilic nature of PVP-k30 and PEG caused them to accumulate on the membrane surface during phase separation. The morphology, chemical composition, surface charge, and pore size of the PES membranes were evaluated by SEM, FTIR, zeta potential, and dextran filtration experiments. The paper also investigated how different spinning solution compositions affected membrane morphology and performance. The separation efficiency of membranes with four different morphologies was tested in single-protein and double-protein mixed solutions. The protein separation effectiveness of the membrane was studied through molecular weight cutoff, zeta potential, and static protein adsorption tests. In addition, the operating pressure and pH value were adjusted to improve ultrafiltration process conditions. The PES membrane with an intact sponge-like structure showed the highest separation factor of 11, making it a prime candidate membrane for the separation of bovine serum albumin (BSA) and lysozyme (LYS). The membrane had a minimal static protein adsorption capacity of 48 mg/cm2 and had excellent anti-fouling properties. When pH = 4, the BSA retention rate was 93% and the LYS retention rate was 23%. Furthermore, it exhibited excellent stability over a pH range of 1–13, confirming its suitability for protein separation applications.

Impaired neuronal plasticity and cognitive decline are cardinal features of Alzheimer’s disease and related Tauopathies. Aberrantly modified Tau protein and neurotransmitter imbalance, predominantly involving acetylcholine, have been linked to these symptoms. In Drosophila, we have shown that dTau loss specifically enhances associative long-term olfactory memory, impairs foot shock habituation, and deregulates proteins involved in the regulation of neurotransmitter levels, particularly acetylcholine. Interestingly, upon choline treatment, the habituation and memory performance of mutants are restored to that of control flies. Based on these surprising results, we decided to use our well-established genetic model to understand how habituation deficits and memory performance correlate with different aspects of choline physiology as an essential component of the neurotransmitter acetylcholine, the lipid phosphatidylcholine, and the osmoregulator betaine. The results revealed that the two observed phenotypes are reversed by different choline metabolites, implying that they are governed by different underlying mechanisms. This work can contribute to a broader knowledge about the physiologic function of Tau, which may be translated into understanding the mechanisms of Tauopathies.

The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Dense Neural Network (DNN), for classifying DDoS attacks. The CICDDoS2019 dataset was used, which underwent data preprocessing to remove outliers, and 22 features were selected using the Pearson correlation coefficient. The RF classifier achieved the best accuracy rate (99.97%), outperforming other classifiers and even previously published neural network-based techniques. These findings underscore the feasibility and effectiveness of machine learning algorithms in the field of DDoS attack detection, reaffirming their relevance as a valuable tool in advanced cyber defense.

The figure of the child is one that, at least in the Westernised imagination, is entangled with notions of innocence, naivety, and freedom. But what of the child who is unfree, who has been stripped of innocence, and for whom naivety is a danger? One expression of this iteration of the figure of the child is the child soldier, which has been a centralising figure in various narratives set during and concerned with African experiences of warfare. This paper is concerned with the figure of the child soldier as it is staged in both Edward Zwick’s Blood Diamond (2006) and Cary Joji Fukunaga’s filmic adaptation of Uzodinma Iweala’s novel, Beasts of No Nation (2015). In turning to Ashis Nandy’s articulation of the tension held within “the child” as being both emblematic of a fantasy of childhood produced by adult nostalgia—hopeful, joyous and free—and always potentially dangerous, this paper pivots the notions of soldiering and slaving on and around the child as a figure. In doing so, the paper asks what it might mean to think of the condition of being a child soldier as being akin to that of being a child slave, weaponised for political and economic ends.

Eddy current inspection has been extensively employed in non-destructive testing of various conductive materials. The coil probe, as a mainstream sensor in the eddy current detection system, inevitably encounters interference from external signals while transmitting its own signal. Therefore, developing techniques to extract valuable signals from noisy ones is crucial for ensuring accurate detection. Carbon fiber composites not only possess significantly lower electrical conductivity compared to conventional metallic materials but also exhibit notable anisotropy. To address this issue, we designed an ‘8’ coil probe set where the excitation coil does not electromagnetically interfere with the detection coil. However, practical applications that require portability and miniaturization pose challenges when utilizing this coil probe set to identify carbon content or defects due to the typically weak output signal. To address this issue, this paper proposes a design that combines the ‘8’ structure of the planar coil probe with the principle of phase-locked amplification to create a dual-phase sensitive phase-locked amplification detection circuit. These specific design ideas were tested using a weak signal, which passed through the preamplifier, secondary amplifier, and band-pass filter comprising the target channel for signal amplification and noise filtering. The effective signal amplitude is proportional to the inverse phase difference between the direct current (DC) signal and inversely proportional to the amplitude of the signal. Finally, the DC signal was passed through an analog-to-digital converter (ADC). The analog-to-digital converter (A/D) is used to collect and calculate the DC signal, enabling the detection of weak electrical signals. Simulation experiments demonstrated that the signal detection circuit has an amplitude error below 0.2% and a phase error below 0.5%. The phase-locked amplification circuit designed in this paper can effectively extract the tiny impedance change signals of the planar coil sensor probe with high sensitivity and good robustness.