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In this work, the mechanical behaviour of cancellous bone tissues used for the manufacturing of commercial heterologous bone grafts was investigated both at small and at large strains. Three different tissues were examined. They were obtained from equine bones after a specific proprietary enzymatic-based treatment aimed at achieving perfect biocompatibility while preserving the collagenous part. Two tissues had the mineral content of the bones of origin (femur, humerus), whereas the third was markedly demineralized (femur). The response at small strains and at failure was studied by means of monotonic compression tests in quasi-static conditions. Stiffness and strength turned out to be governed by the apparent density of the tissue, with specimen size and geometry, and tissue macro-structural characteristics, only playing a secondary role. A ductile- and a brittle-like failure was exhibited by the demineralized tissue and the non-demineralized ones, respectively. The large deformation behaviour was studied by means of cyclic compression tests, which clearly highlighted the occurrence of plasticity (for strains higher than ≈1.9%), and allowed to study the strain accumulation processes, at the macromechanical level. More in-depth microstructural analyses were carried out on the demineralized tissue. Flexural tests were also performed.

In the present study, polymeric micelles were developed to improve the intestinal permeability of an extract of Olea europaea L. leaf with a high content of total polyphenols (49% w/w), with 41% w/w corresponding to the oleuropein amount. A pre-formulation study was conducted to obtain a stable formulation with a high loading capacity for extract. The freeze-drying process was considered to improve the stability of the formulation during storage. Micelles were characterized in terms of physical and chemical properties, encapsulation efficiency, stability, and in vitro release. The optimized system consisted of 15 mg/mL of extract, 20 mg/mL of Pluronic L121, 20 mg/mL of Pluronic F68, and 10 mg/mL of D-α-tocopheryl polyethylene glycol succinate (TPGS), with dimensions of 14.21 ± 0.14 nm, a polydisersity index (PdI) of 0.19 ± 0.05 and an encapsulation efficiency of 66.21 ± 1.11%. The influence of the micelles on polyphenol permeability was evaluated using both Parallel Artificial Membrane Permeability Assay (PAMPA) and the Caco-2 cell monolayer. In both assays, the polymeric micelles improved the permeation of polyphenols, as demonstrated by the increase in Pe and Papp values.

In areas with high levels of solar radiation, decreasing the amount of solar energy absorbed by the building envelope is useful to reduce the need of air conditioning and "heat island" effects. Most high-reflective products, however, suffer from low durability. The COOL-IT project is developing an innovative high-reflective cement based mortar for precast products to be used as outer layer in buildings for both vertical and horizontal surfaces, or for road pavement. The mix design is aimed at increasing the durability of this cement-based component while retaining high reflectance to solar radiation. This paper presents the preliminary results of the project, based on the simulation of the energy demand of a residential building, intended as a support to optimize the proposed mixes. The model is analysed in three different locations in Italy, for one year of operation. This allows evaluating the trade-off of the energy demand between the winter increase and the summer reduction.

Retentive drug delivery systems (DDSs) are intended for prolonged residence and release inside hollow muscular organs, to achieve either local or systemic therapeutic goals. Recently, formulations based on shape memory polymers (SMPs) have gained attention in view of their special ability to recover a shape with greater spatial encumbrance at the target organ (e.g., urinary bladder or stomach), triggered by contact with biological fluids at body temperature. In this work, poly(vinyl alcohol) (PVA), a pharmaceutical-grade SMP previously shown to be an interesting 4D printing candidate, was employed to fabricate expandable organ-retentive prototypes by hot melt extrusion. With the aim of improving the mechanical resistance of the expandable DDS and slowing down relevant drug release, the application of insoluble permeable coatings based on either Eudragit® RS/RL or Eudragit® NE was evaluated using simple I-shaped specimens. The impact of the composition and thickness of the coating on the shape memory, swelling, and release behavior as well as on the mechanical properties of these specimens was thoroughly investigated and the effectiveness of the proposed strategy was demonstrated by the results obtained.

The expected increase of the penetration of distributed renewable energy technologies into the electricity grid is expected to lead to major challenges. As a main stakeholder, authorities often lack the appropriate tools to frame and encourage the transition and monitor the impact of energy transition policies. This paper aims at combining relatively detailed modeling of the PV generation potential on the building’s envelope while retaining the energy system optimization approach. The problem is addressed as a multiobjective, mixed-integer linear programming problem. Compared to the existing literature in the field, the proposed approach combines advanced modeling of the energy generation potential from PV panels with detailed representation of the district energy systems, thus allowing an accurate representation of the interaction between the energy generation from PV and the rest of the system. The proposed approach was applied to a typical residential district in Switzerland. The results of the application of the proposed method show that the district can achieve carbon neutrality based on PV energy alone, but this requires covering all the available district’s rooftops and part of the district’s facades. Whereas facades are generally disregarded due to their lower generation potential, the results also allow concluding that facade PV can be economically convenient for a wide range of electricity prices, including those currently used by the Swiss grid operators. Achieving self-sufficiency at district scale is challenging: it can be achieved by covering approximately 42–100% of the available surface when the round-trip efficiency decreases from 100 to 50%. The results underline the importance of storage for achieving self-sufficiency: even with 100%, round-trip efficiency for the storage, very large capacities are required. However, energy demand reduction through renovation would allow reaching self-sufficiency with half of the PV and storage capacity required.

In recent years, a rapid increase in the adoption of renewable energy sources and in the transition from a centralized electricity generation system to an increasingly distributed one has occurred. Within this scenario, in line with the European directives for achieving the objectives in the field of energy transition and climate change, energy communities are seen as potential contributors. The purpose of this work is to analyze the application potential of the energy community concept associated with district heating networks, leading to better overall energy-economic performance. This was demonstrated for a specific energy community in Italy, and it can be achieved by maximizing internal energy sharing—resulting from the electricity surplus generated by the photovoltaic system—and adopting different strategies that include heat pumps in order to maximize self-consumption and self-sufficiency, as well as to evaluate the most efficient investment in economic terms by exploiting the incentive tariff on shared energy. The results show that the performance of the system can be improved with the proposed design, achieving a significant reduction in the system’s energy demand, emissions and costs: compared to the reference case, the use of photovoltaics reduces primary energy demand by approximately 11%, while the addition of the energy community configuration allows emissions to be reduced by nearly 12%, with no additional investment.

In the context of increasing concern for anthropogenic CO2 emissions, the residential building sector still represents a major contributor to energy demand. The integration of renewable energy sources, and particularly of photovoltaic (PV) panels, is becoming an increasingly widespread solution for reducing the carbon footprint of building energy systems (BES). However, the volatility of the energy generation and its mismatch with the typical demand patterns are cause for concern, particularly from the viewpoint of the management of the power grid. This paper aims to show the influence of the orientation of photovoltaic panels in designing new BES and to provide support to the decision making process of optimal PV placing. The subject is addressed with a mixed integer linear optimization problem, with costs as objectives and the installation, tilt, and azimuth of PV panels as the main decision variables. Compared with existing BES optimization approaches reported in literature, the contribution of PV panels is modeled in more detail, including a more accurate solar irradiation model and the shading effect among panels. Compared with existing studies in PV modeling, the interaction between the PV panels and the remaining units of the BES, including the effects of optimal, scheduling is considered. The study is based on data from a residential district with 40 buildings in western Switzerland. The results confirm the relevant influence of PV panels’ azimuth and tilt on the performance of BES. Whereas south-orientation remains the most preferred choice, west-orientationed panels better match the demand when compared with east-orientationed panels. Apart from the benefits for individual buildings, an appropriate choice of orientation was shown to benefit the grid: rotating the panels 20° westwards can, together with an appropriate scheduling of the BES, reduce the peak power of the exchange with the power grid by 50% while increasing total cost by only 8.3%. Including the more detailed modeling of the PV energy generation demonstrated that assuming horizontal surfaces can lead to inaccuracies of up to 20% when calculating operating expenses and electricity generated, particularly for high levels of PV penetration.

This work reports on a novel method to synthesize hydrophobically-modified hydrogels by curing epoxy monomers with amines. The resulting networks contain hydrophilic poly(ethylene glycol) (PEG) segments, poly(propylene glycol) (PPG) segments, and C18 alkyl segments. By varying the content of C18 segments, networks with different hydrophilic-lipophilic balance (HLB) are obtained. All networks show an amphiphilic behavior, swelling considerably both in organic solvents and in aqueous media. In the latter they display a thermosensitive behavior, which is highly affected by the network HLB and the pH of the solution. A decrease in HLB results in an increment of the polymer weight content (wp) due to hydrophobic association. Furthermore, a reduction in HLB induces a remarkable increase in initial modulus, elongation at break and tensile strength, especially when wp becomes greater than about 10%. Low field nuclear magnetic resonance (LF-NMR) experiments evidence that, when HLB decreases, a sudden and considerable increase in hydrogel heterogeneity takes place due to occurrence of extensive physical crosslinking. Available data suggest that in systems with wp ≳ 10% a continuous physical network superimposes to the pre-existing chemical network and leads to a sort of double network capable of considerably improving hydrogel toughness.

Solid oxide fuel cells (SOFC) have developed to a mature technology, able to achieve electrical efficiencies beyond 60%. This makes them particularly suitable for off-grid applications, where SOFCs can supply both electricity and heat at high efficiency. Concerns related to lifetime, particularly when operated dynamically, and the high investment cost are however still the main obstacles toward a widespread adoption of this technology. In this paper, we propose a hybrid cogeneration system that attempts to overcome these limitations, in which the SOFC mainly provides the baseload of the system. Introducing a purification unit allows the production and storage of pure hydrogen from the SOFC anode off-gas. The hydrogen can be stored, and used in a proton exchange membrane fuel cell (PEMFC) during peak demands. The SOFC system is completed with a battery, used during periods of high electricity production. We propose the use of a mixed integer-linear optimization framework for the sizing of the different components of the system, and particularly for identifying the optimal trade-off between round-trip efficiency and investment cost of the battery-based and hydrogen-based storage systems. The proposed system is applied and optimized to two case studies: an off-grid dwelling, and a cruise ship. The results show that, if the SOFC is used as the main energy conversion technology of the system, the use of hydrogen storage in combination with a PEMFC and a battery is more economically convenient compared to the use of the SOFC in stand-alone mode, or of pure battery storage. The results show that the proposed hybrid storage solution makes it possible to reduce the investment cost of the system, while maintaining the use of the SOFC as the main energy source of the system.

Fracture mechanics are of high interest for the engineering design and structural integrity assessment of polymeric materials; however, regarding highly ductile polymers, many open questions still remain in terms of fully understanding deformation and fracture behaviors. For example, the influence of the constraint and specimen size on the fracture behavior of polymeric materials is still not clear. In this study, a polymeric material with an elastic plastic deformation behavior (ABS, acrylonitrile butadiene styrene) is investigated with regard to the influence of constraint and specimen size. Different single-edge notched bending (SENB) specimen sizes with constant geometrical ratios were tested. The material key curve was used to investigate differences in the constraint, where changes for small and large specimen sizes were found. Based on a size-independent crack resistance curve (J–R curve), two apparent initiation parameters (J0.2 and Jbl) were determined, namely, the initiation parameter Jini (based on the crack propagation kinetics curve) and the initiation parameter JI,lim (based on an ESIS TC 4 draft protocol). It was found that J0.2 and Jbl could be used as crack initiation parameters whereby Jini and JI,lim are indicative of the onset of stable crack growth.

The increased concerns about the effect of human activities on the climate have pushed natural gas among the most obvious solutions for the transition to a low-carbon economy. The growing importance and volumes of liquefied natural gas for transportation over long distances come as a consequence of this tendency. The liquefaction of natural gas requires a high amount of energy that can be recovered during the re-gasification phase. In this paper, a novel approach for this purpose is presented, where the main feature is the use of a combination of Rankine and Brayton cycles while retaining natural gas as the only working fluid of the system. The proposed system is optimized for cost and exergy efficiency using a bi-level multi-objective optimization procedure, where the master level is setup as a nonlinear optimization problem and solved using an evolutionary algorithm, while the slave level as a mixed integer-linear programming problem. The results of the optimization show that such system can potentially achieve high efficiencies (up to 60 % exergy efficiency for the power cycle and above 65 % plant thermal efficiency), at the cost of a significant capital investment for the heat exchanger network. By allowing a lower level of integration in the system a profitability of up to 98 kUSD/y can be achieved, while retaining significantly high performance.

The selection of a proper machinery system is one of the primary decisions to be taken during the ship-design phase. Nonetheless, this selection is made challenging by the presence of a variety of alternatives, and by the limited data availability at the early stages of the design phase. An optimization framework is presented in this paper, supporting decision making at the earliest stages of the ship-design process. The framework is suitable to perform the screening and the selection of optimal machinery configurations for a predefined ship operational profile, and it includes both linear and non-linear optimization routines. The results of the linear and the non-linear approaches are compared, and indications on what conditions are the most suitable for the application of one or the other approach are provided. Both approaches are tested for two case studies, a bulk carrier and a small cruise ship. The results indicate that both optimization approaches lead to the same layout of the machinery system, but to slightly different unit scheduling. This suggests that the use of the linear approach is suitable for design purposes, but less appropriate for operational optimization. In addition, the findings of the work suggest that the trade-off between fuel consumption and volume of the engines should be considered when selecting the machinery system for a ship.

In recent years, the International Maritime Organization agreed on aiming to reduce shipping’s greenhouse gas emissions by 50% with respect to 2009 levels. Meanwhile, cruise ship tourism is growing at a fast pace, making the challenge of achieving this goal even harder. The complexity of the energy system of these ships makes them of particular interest from an energy systems perspective. To illustrate this, we analyzed the energy and exergy flow rates of a cruise ship sailing in the Baltic Sea based on measurements from one year of the ship’s operations. The energy analysis allows identifying propulsion as the main energy user (46% of the total) followed by heat (27%) and electric power (27%) generation; the exergy analysis allowed instead identifying the main inefficiencies of the system: while exergy is primarily destroyed in all processes involving combustion (76% of the total), the other main causes of exergy destruction are the turbochargers, the heat recovery steam generators, the steam heaters, the preheater in the accommodation heating systems, the sea water coolers, and the electric generators; the main exergy losses take place in the exhaust gas of the engines not equipped with heat recovery devices. The application of clustering of the ship’s operations based on the concept of typical operational days suggests that the use of five typical days provides a good approximation of the yearly ship’s operations and can hence be used for the design and optimization of the energy systems of the ship.

Abstract Background Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions. Methods Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania. Results The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients. Conclusion These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Abstract Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60–100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.

Abstract Background Anopheles funestus is a leading vector of malaria in most parts of East and Southern Africa, yet its ecology and responses to vector control remain poorly understood compared with other vectors such as Anopheles gambiae and Anopheles arabiensis. This study presents the first large-scale survey of the genetic and phenotypic expression of insecticide resistance in An. funestus populations in Tanzania. Methods We performed insecticide susceptibility bioassays on An. funestus mosquitoes in nine regions with moderate-to-high malaria prevalence in Tanzania, followed by genotyping for resistance-associated mutations (CYP6P9a, CYP6P9b, L119F-GSTe2) and structural variants (SV4.3 kb, SV6.5 kb). Generalized linear models were used to assess relationships between genetic markers and phenotypic resistance. An interactive R Shiny tool was created to visualize the data and support evidence-based interventions. Results Pyrethroid resistance was universal but reversible by piperonyl-butoxide (PBO). However, carbamate resistance was observed in only five of the nine districts, and dichloro-diphenyl-trichloroethane (DDT) resistance was found only in the Kilombero valley, south-eastern Tanzania. Conversely, there was universal susceptibility to the organophosphate pirimiphos-methyl in all sites. Genetic markers of resistance had distinct geographical patterns, with CYP6P9a-R and CYP6P9b-R alleles, and the SV6.5 kb structural variant absent or undetectable in the north-west but prevalent in all other sites, while SV4.3 kb was prevalent in the north-western and western regions but absent elsewhere. Emergent L119F-GSTe2, associated with deltamethrin resistance, was detected in heterozygous form in districts bordering Mozambique, Malawi and the Democratic Republic of Congo. The resistance landscape was most complex in western Tanzania, in Tanganyika district, where all five genetic markers were detected. There was a notable south-to-north spread of resistance genes, especially CYP6P9a-R, though this appears to be interrupted, possibly by the Rift Valley. Conclusions This study underscores the need to expand resistance monitoring to include An. funestus alongside other vector species, and to screen for both the genetic and phenotypic signatures of resistance. The findings can be visualized online via an interactive user interface and could inform data-driven decision-making for resistance management and vector control. Since this was the first large-scale survey of resistance in Tanzania’s An. funestus, we recommend regular updates with greater geographical and temporal coverage. Graphical Abstract

Abstract Background The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. Methods and results Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the ‘ground truth’ for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%–90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. Conclusions This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

Abstract Background Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. Methods Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1–16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection–Fourier transform infrared (ATR–FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1–9 days (young, non-infectious) and 10–16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. Results The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. Conclusions This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations. Graphical Abstract

Abstract Background Anopheles funestus is a major malaria vector in Eastern and Southern Africa and is currently the dominant malaria-transmitting vector in many parts of Tanzania. Previous research has identified its preference for specific aquatic habitats, especially those that persist in dry months. This observation suggests the potential for targeted control through precise habitat mapping and characterization. In this study, we investigated the influence of habitat characteristics, land cover and human population densities on An. funestus distribution during dry seasons. Based on the results, we developed a habitat suitability model for this vector species in south-eastern Tanzania. Methods Eighteen villages in south-eastern Tanzania were surveyed during the dry season from September-December 2021. Water bodies were systematically inspected for mosquito larvae and characterized by their physico-chemical characteristics and surrounding environmental features. A generalized linear model was used to assess the presence of An. funestus larvae as a function of the physico-chemical characteristics, land use and human population densities. The results obtained from this model were used to generate spatially explicit predictions of habitat suitability in the study districts. Results Of the 1466 aquatic habitats surveyed, 440 were positive for An. funestus, with river streams having the highest positivity (74%; n = 322) followed by ground pools (15%; n = 67). The final model had an 83% accuracy in predicting positive An. funestus habitats, with the most important characteristics being permanent waters, clear waters with or without vegetation or movement and shading over the habitats. There was also a positive association of An. funestus presence with forested areas and a negative association with built-up areas. Human population densities had no influence on An. funestus distribution. Conclusions The results of this study underscore the crucial role of both the specific habitat characteristics and key environmental factors, notably land cover, in the distribution of An. funestus. In this study area, An. funestus predominantly inhabits river streams and ground pools, with a preference for clear, perennial waters with shading. The strong positive association with more pristine environments with tree covers and the negative association with built-up areas underscore the importance of ecological transitions in vector distribution and malaria transmission risk. Such spatially explicit predictions could enable more precise interventions, particularly larval source management, to accelerate malaria control. Graphical Abstract

During the 2022-outbreak, peculiar clinical presentations of Mpox have been described, some of which can make the diagnosis of the disease extremely challenging. Here we report a case series of fourteen patients with Mpox pharynogotonsillar involvement (PTI) seen at National Institute for Infectious Diseases, “Lazzaro Spallanzani”, in Rome, Italy from May to September 2022. All included patients were men who have sex with men (median age 38 years) reporting unprotected sex within three weeks from symptoms onset. Seven out of fourteen patients needed hospitalization due to uncontrolled pain, reduced airspace and difficulty swallowing, of whom five were effectively treated with tecovirimat or cidofovir. The remaining two patients were treated with symptomatic drugs. The typical Mpox muco-cutaneous manifestations were not observed simultaneously with PTI in three patients, two of whom developed the lesions after several days, while one never manifested them. Polymerase Chain Reaction (PCR) for Mpox virus was positive in oropharyngeal swab, saliva and serum. Although PTI occurs in only a small percentage of Mpox cases, its diagnosis is of utmost importance. In fact, this localization, if not identified, could lead to serious complications in the absence of early antiviral treatment and to missed diagnosis with an increased risk of disease transmission.

Abstract Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements—and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

Abstract Background Local strains of the entomopathogenic fungus Metarhizium pingshaense in Burkina Faso have demonstrated remarkable virulence against malaria vectors, positioning them as promising candidates for inclusion in the future arsenal of malaria control strategies. However, the underlying mechanisms responsible for this virulence remain unknown. To comprehend the fungal infection process, it is crucial to investigate the attachment mechanisms of fungal spores to the mosquito cuticle and explore the relationship between virulence and attachment kinetics. This study aims to assess the adhesion and virulence properties of native Metarhizium fungal strains from Burkina Faso for controlling malaria vectors. Methods Fungal strains were isolated from 201 insects and 1399 rhizosphere samples, and four strains of Metarhizium fungi were selected. Fungal suspensions were used to infect 3-day-old female Anopheles coluzzii mosquitoes at three different concentrations (106, 107, 108 conidia/ml). The survival of the mosquitoes was measured over 14 days, and fungal growth was quantified after 1 and 24 h to assess adhesion of the fungal strains onto the mosquito cuticle. Results All four fungi strains increased mosquito mortality compared to control (Chi-square test, χ2 = 286.55, df = 4, P