Tunga penetrans (Linnaeus, 1758) (Siphonaptera: Tungidae) is an ectoparasitic flea whose adult females cause tungiasis by embedding into the host's skin. Environmental, social and financial challenges in tungiasis endemic areas have left this disease neglected and lacking locally available control measures. One possible approach for controlling T. penetrans targets its off-host stages; however, surveillance tools for adult fleas are still lacking. Moreover, host-seeking cues and behaviour of adult T. penetrans remain largely unknown. In this study, we show that visual cues are attractive to adult T. penetrans. Newly emerged T. penetrans responded to light stimuli across a broad spectrum, ranging from 375 nm to 690 nm. Within this range, significantly stronger attraction was observed at 430 nm over 375 nm, at 490 nm over 610 nm, and at 610 nm over 690 nm. Attraction did not differ significantly between 430 nm and 490 nm or between 535 nm and 490 nm. Overall, wavelengths between 430-535 nm induced stronger attraction in both sexes under constant electrical current conditions. In contrast, although host odor and body temperature attracted more than half of the tested adult fleas on average, the observed responses were not statistically significant. These findings suggest that visual cues in this spectral range could serve as promising attractants for capturing adult fleas. Incorporating these insights into trap strategies could support improved monitoring, surveillance, and potentially contribute to control efforts.

The information age, fueled by globalization and technological advancements, has transformed the data landscape, impacting decision-making, management, governance, and policy intervention across various sectors. The health sectors are no exception, experiencing a data explosion driven by ongoing research initiatives and advancements. This transformation necessitates a shift towards evidence-based practices, emphasizing the importance of high-quality, timely, accessible data at all levels. Data science offers a compelling solution, particularly in Africa, where resource scarcity demands prudent allocation. By leveraging the abundance and diversity of data, data-driven decision-making and policymaking can be fostered. This approach holds immense potential to develop accurate, effective, measurable policies, addressing Africa's healthcare challenges. In this paper, we delve into the need for leveraging data on health decision-making and policy implementation in Africa. We further explore the intricate relationship between the burgeoning field of data science, Africa's persistent infrastructural deficiencies, and the continent's ongoing healthcare transformation challenges. We use available data, publications, and information resources across Africa to highlight the challenges, and opportunities and provide a roadmap and recommendation involving data-driven and policy-making decisions in the healthcare sector. We conclude by making proposals, and recommendations, and advocating for global and local approaches to data sharing and capacity-building initiatives by policymakers in collaboration with researchers to foster a system of data-driven decisions in health care. We emphasize the need for data-sharing partnership model, turning African biomedical data into treasures and valuable assets, and have highlighted different data elements that can contribute to data-driven decisions in healthcare.

Determining formaldehyde emissions is a key component of source control for wood-based panels. The non-linear WKI_2 equation, a modified form of the Andersen equation, is frequently used to deduce material-related emission parameters from measured test chamber concentrations and boundary conditions such as temperature (T), relative humidity (RH), air exchange (AC), and loading (L). The coefficients of the WKI_2 equation are determined using non-linear least-squares algorithms to ensure that the calculated steady-state concentrations correspond as closely as possible to the experimental data. Since the WKI_2 equation had previously only been verified for particleboard, the question arose whether it could also be used to describe the formaldehyde emissions of other wood-based panels. Therefore, a supplementary experimental dataset with medium-density fiberboard and plywood was provided and the statistical analysis showed very good agreement with the dataset for particleboard. Non-linear regression analysis is often sophisticated, as the six coefficients of the WKI_2 equation are partially correlated and the fitting procedure can be sensitive to the initial values. An alternative approach is neural network regression. In general, the advantage of machine learning lies in the recognition of non-linear patterns, which are difficult to represent in classical models without explicit physical assumptions, and in its greater robustness against measurement errors. However, neural networks require training to identify the optimal number/combination of hidden layers and neurons for the specific problem. The comparison conducted in this work has shown that both approaches, model function and machine learning, offer great potential for more precise and reliable emission predictions.

Tungiasis is a neglected tropical disease (NTD) caused by the jigger flea (Tunga penetrans) which typically invades the sole of the foot causing serious morbidity and sometimes mortality through secondary infections such as tetanus. Although tungiasis is found throughout tropical regions of the Americas and Africa, it has not yet become established in tropical Asia. Due to the large human population and extensive poverty in tropical Asia, an introduction of T. penetrans would pose a serious public health threat. Therefore, to estimate the extent of suitable habitat in tropical Asia and to identify areas at high risk of tungiasis outbreaks, niche modelling using climate, soil, and poverty data was performed. This allowed identification of zones where high poverty overlap with high environmental suitability for T. penetrans. Models projected that the most environmentally suitable regions in South Asia included the west coast of India, costal Sri Lanka, the Meghna delta and catchment, Andaman Islands, and coastal Myanmar. The areas predicted to have the highest risk of tungiasis emergence were the Meghna delta and catchment and the Ayeyarwady region (Myanmar). In Southeast Asia, the areas with the highest environmental suitability included Indochina, central and northern Philippines, the eastern coast of Peninsula Malaysia, the Lesser Sunda islands and the lowlands of West Papua. The areas predicted to have the highest risk of tungiasis outbreaks were Laos, Cambodia, Vietnam, the central Philippines, and the Lesser Sunda islands. With increased trade and travel between tropical Asia and tungiasis-endemic regions, public health safeguards such as enhanced monitoring in high-risk areas and travel health education are needed to prevent the spread of tungiasis to Asia.

Tungiasis is a cutaneous ectoparasitosis caused by the penetration of gravid female Tunga penetrans fleas into the epidermis. Although endemic in tropical and subtropical regions, it remains rare in Europe, where most cases are travel-associated and secondary household transmission is seldom documented. This study describes imported tungiasis in Greece and investigates possible secondary household transmission in a non-endemic setting. Seven Greek men residing in Attica developed tungiasis following occupational exposure in Tanzania, together with one secondary case in a non-travelling household contact who had never travelled outside Greece. Diagnosis was based on clinical and dermoscopic findings and confirmed by amplification and sequencing of the mitochondrial cytochrome oxidase I (COI) gene. Household investigations were also performed. Eight male patients presented with painful plantar and/or subungual nodular lesions. Sequence analysis of COI demonstrated 657/662 bp (99%) identity with the Tunga penetrans reference sequence, and identical sequences were identified in all samples. A representative sequence was deposited in GenBank (accession no. PZ336383). All patients exhibited mild-to-moderate elevations of hepatocellular and cholestatic liver enzymes, which resolved within two weeks following treatment. One probable secondary household case was identified, and no infestation was detected among additional cohabitants or companion animals. This report documents imported tungiasis with probable secondary household transmission in Greece and highlights the importance of clinical awareness and environmental assessment in non-endemic settings.

Nonvolatile flip-flops and latches have been proposed as key building blocks for improving the reliability and energy efficiency of computing systems operating under aggressive power gating and intermittent power availability. Ferroelectric field-effect transistors (FeFETs) based on van der Waals (vdW) heterostructures provide a promising approach for nonvolatile sequential logic by enabling direct integration of memory functionality into logic devices while minimizing interface-related degradation. In this work, CuInP2S6-based vdW FeFETs are fabricated in both metal-ferroelectric-metal-insulator-semiconductor and metal-ferroelectric-semiconductor configurations, exhibiting robust ferroelectric switching and stable nonvolatile behavior. Building on these devices, ferroelectric nonvolatile inverters are realized, followed by a nonvolatile ferroelectric latch that reliably restores its logic state after complete power loss. The proposed latch leverages ambipolar MoTe2 channels to inherently drive the FeFET gate-source and gate-drain voltages to full rail-to-rail values (VGS = VGD = ±VDD) in a state-dependent manner, thereby ensuring reliable polarization switching without auxiliary sensing circuitry or additional bias-management schemes. Owing to the clean vdW interfaces and intrinsic circuit operation, the latch restores its state upon power recovery using only simple control signals. These results demonstrate the strong performance of 2D vdW FeFETs and establish nonvolatile latch operation as a viable experimental platform for nonvolatile sequential logic and energy-efficient computing architectures.

Retinal ganglion cell (RGC) degeneration underlies glaucomatous optic neuropathy and remains a leading cause of irreversible vision loss worldwide. Although elevated intraocular pressure (IOP) is the primary modifiable risk factor, RGC death reflects converging mechanisms including mechanical stress, vascular insufficiency, metabolic dysfunction, and neuroinflammation. We conducted a PRISMA-guided systematic review with PICOS-defined eligibility criteria, searching PubMed, Cochrane Library, ScienceDirect, Scopus, Google Scholar, and ProQuest for studies through January 2026 on RGC degeneration and neuroprotective or regenerative therapies in glaucoma. Included studies supported OCT-based structural assessment and imaging biomarkers as essential tools for early detection, risk stratification, and monitoring of progression and treatment response. Continued RGC loss despite IOP control in many patients highlights the need for mechanism-based interventions; neuroprotective strategies targeting excitotoxicity, oxidative stress, mitochondrial dysfunction, and neurotrophic insufficiency are emerging, while stem cell and gene-based regenerative therapies remain under active investigation. Integrating molecular insights with advanced imaging and biomarker-guided endpoints may enable earlier, more individualized intervention and help explain progression despite adequate pressure control.

Data Science can revolutionize biomedical sciences, and ultimately, health. However, this relies on core elements, including computing infrastructure, data science skills, and well-curated contextually relevant data, which are limited in low- and middle-income countries. Many big data biomedical projects that have emerged over the last decade have been instrumental in development of datasets, computing infrastructure and skills; yet large disparities remain. Here, we review the data science development within Africa over recent years and identify key gaps and challenges that still hinder its implementation for health. We provide recommendations for establishing a sustainable data science ecosystem within Africa.

Mining activities significantly contribute to the release of hazardous heavy metals (HHMs) into agricultural soils, especially where stone extraction is intensive. This study assessed metal content, source identification, and possible health risks associated with HHMs (Cd, Pb, Cr, Cu, Ni) in stone mines affected agricultural soils of India. The results revealed that Zone 1 exhibited higher concentrations of Cr (308.95 ± 23.03 mg/kg), Ni (117.11 ± 16.21 mg/kg), and Cd (4.21 ± 1.26 mg/kg), surpasses the threshold limit set by WHO/FAO for agricultural soils. In contrast, Zone 2 had considerably lower HHMs concentrations for Cr (60.24 ± 10.68 mg/kg), Ni (31.61 ± 7.52 mg/kg) and Cd (0.66 ± 0.13 mg/kg). Geospatial distribution and self-organizing map (SOM) indicated spatial clustering of HHMs, with elevated concentrations centered in the southern part of the study area. Positive matrix factorization showed that geogenic sources and mining activities affect significantly to HHMs pollution. Monte Carlo simulation revealed that children are more prone to exposure to HHMs than adults, with ingestion identified as the primary exposure route. Hazard quotient values calculated from free ion activity model exceeded the threshold limit for Pb (1.59E+00) and Cd (6.32E-01). Severity adjustment margin of exposure highlighted severe human health risk for Cr and Ni. Random forest analysis indicated that bioavailable HHMs significantly influence the accumulation of metals in rice tissues which elevating dietary risk. The integration of robust statistical tools offers insights into the dynamics of HHMs contamination and provide policymakers and environmental scientists with useful information to mitigate the potential risks caused by mining on nearby agricultural lands.

Tungiasis, caused by the sand flea Tunga penetrans, results in itching and pain. Effective treatments, such as dimeticones, are often unaffordable. A 3% phenothrin lotion has shown safety and efficacy in Kenyan trials. This study compared the cure rate and safety of 3% phenothrin lotion (as the intervention) and 0.05% potassium permanganate (KMnO4; as the standard-care comparator) over 14 days. This parallel-group, three-arm, non-blinded, randomised comparative trial was conducted in Vihiga County, Kenya. Participants aged ≥2 years with ≥1 viable flea on each foot were allocated (2:1:1) to KMnO4, single-dose 3% phenothrin, or two-dose 3% phenothrin groups. Overall, 415 fleas from 79 participants were followed up to day 14 (KMnO4, 213; single-dose, 129; two-dose, 73). On days 4 and 7, the single-dose phenothrin showed significantly higher cure rates (11.6% and 21.7%) than KMnO4 (0.9% and 11.7%) (p < 0.001 and p = 0.013). The differences diminished by days 10 and 14 because of spontaneous flea death. The cure rate of the two-dose group on day-7 (8.2%) was lower than that of the single-dose group. Single-dose 3% phenothrin improved early cure rates compared to KMnO4, but not by days 10-14; two-dose phenothrin showed no benefit compared with single dose from day 7 onwards.