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Abstract In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.

Nowadays, the surge in energy demand due to economic growth and extreme weather conditions has put immense pressure on the usage of fossil fuels in Kuwait. As a result, scheduled load-shedding is performed in some regions during the summer season to meet the energy demand. To address this issue, this paper proposes a photovoltaic-based street lighting system as an alternative solution to meet the rising energy demand in Kuwait during the daytime. This study initially investigates the existing street lighting systems in the state of Kuwait. Subsequently, three different configurations of photovoltaic panels are proposed based on the existing streetlight pole structures. The simulation models are then developed and evaluated using physical security information management and PVSyst simulation platforms, aiming to validate their performance against conventional power generation models in Kuwait. The proposed photovoltaic system is designed based on feedback information collected from the existing installed capacity. Finally, an overall energy model is presented to demonstrate how solar potential can offset energy consumption during peak demand hours. Practical testbed data from the Al-Jahra residential area of Kuwait is used for validation. The results indicate that the proposed photovoltaic street lighting system can generate a maximum power output of 18.8 GWh in August and a minimum of 11.8 GWh in December, compared to the monthly consumption of 30.45 GWh. The study showcases the economic viability of the solution, with an average degradation ratio of 13% of the total cost. Moreover, the proposed system contributes to a reduction in CO 2 emissions from traditional power plants.

Abstract Background Effective family planning is associated with substantial benefits, including reductions in maternal and neonatal mortality due to the avoidance of unintended pregnancies, and contributions to spacing, timing, and limiting births. However, in Zanzibar, Tanzania, the utilization of modern contraceptive methods is low. This study therefore aimed to identify barriers to using postpartum family planning among women in Zanzibar. Methods Five focus group discussions were conducted with 24 women who gave birth in the maternity unit at a reference hospital in Zanzibar during the first quarter of 2022. The discussions took place in Swahili, were performed with the assistance of an interview guide, and were audio recorded, transcribed in Swahili, and translated to English. Data were analysed with qualitative content analysis using an inductive approach. Results Barriers to using postpartum family planning in Zanzibar could be summarized in three generic categories. Inadequate knowledge about postpartum family planning is expressed in the subcategories: inadequate knowledge about contraceptive methods and their mode of action, insufficient quality of family planning services, and belief in traditional and natural medicine for family planning. Perceived risks of modern contraceptive methods are described in the subcategories: fear of being harmed, and fear of irregular bleeding. Limited power in one’s own decision about contraceptive use consist of the subcategories: the need to involve the husband, and opposition and lack of interest from the husband. Conclusions The participants’ current knowledge of postpartum family planning was insufficient to either overcome the fear of side-effects or to understand which side-effects were real and likely to happen. The woman’s power in her own decision-making around her sexual reproductive rights is of critical importance. Given the barriers identified in this study, the findings call for increased knowledge about family planning methods and their mode of action, and involvement of the husband throughout pregnancy, childbirth, and the postpartum period in postpartum family planning education and counselling, in Zanzibar and in similar settings.

This article presents a numerical simulation model using COMSOL software to study breast thermophysical properties. It analyzes tumor heat at different locations within the breast, records breast surface temperatures, investigates the effects of factors such as blood perfusion, size, depth, and thermal conductivity on breast size, and applies Pennes’ bioheat formula to illustrate thermal distribution on the breast skin surface. An analysis was conducted to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect breast skin surface temperature. The simulation model results showed that the highest variations in skin temperatures for breasts with tumors and without tumors can range from 2.58 °C to 0.274 °C. Further, large breast size with a large surface area consistently reduces the temperature variations on the skin and might have difficulty in yielding observable temperature contrast. For small breast sizes, however, heat from tumor sizes below 0.5 cm might be quite difficult to detect, while tumors located deep within the breast layers could not produce observable temperature variations. Motivated by the above interesting results, an emulation experiment was conducted to enhance the observable heat and temperature background contrast, using situ-cooling gel applied to silicon breasts, while the tumor source was emulated using LEDs. The experiment was used to evaluate the effectiveness of adding situ-cooling to the breast surface area, and to study the modulated effect of tumor size and depth. Experimental results showed that situ-cooling enhances thermal contrast in breast thermal images. For example, for a tumor location at a depth of 10 cm, a difference of 6 °C can still be achieved with situ-cooling gel applied, a feat that was not possible in the simulation model. Furthermore, changes in tumor size and location depth significantly impacted surface temperature distribution.

Background: Parasitic infectious agents rarely occur in isolation. Epidemiological evidence is mostly lacking, and little is known on how the two common parasites Plasmodium and soil transmitted helminths (STH) interact. There are contradictory findings in different studies. Synergism, antagonism and neutral effect have been documented between Plasmodium and STH. This study investigated the impact of STH on clinical malaria presentation and treatment outcome. Methods: A matched case control study with a semi longitudinal follow up according to World Health Organization (WHO) antimalarial surveillance guideline was done among children aged 2 months to 9 years inclusively living in western rural areas of Bagamoyo, coastal region of Tanzania. Cases were children with uncomplicated and severe malaria enrolled from the health facilities while controls were children with asymptomatic Plasmodium parasitemia enrolled from the same community. Results: In simple conditional regression analysis there was a tendency for a protective effect of STH on the development of clinical malaria [OR = 0.6, 95% CI of 0.3–1.3] which was more marked for Enterobius vermicularis species [OR = 0.2, 95% CI of 0.0–0.9]. On the contrary, hookworm species tended to be associated with increased risk of clinical malaria [OR = 3.0, 95% CI of 0.9–9.5]. In multiple conditional regression analysis, the overall protective effect was lower for all helminth infection [OR = 0.8, 95% CI of 0.3–1.9] but remained significantly protective for E. vermicularis species [OR = 0.1, 95% CI of 0.0–1.0] and borderline significant for hookworm species [OR = 3.6, 95% CI of 0.9–14.3]. Using ordinal logistic regression which better reflects the progression of asymptomatic Plasmodium parasitemia to severe malaria, there was a 50% significant protective effect with overall helminths [OR = 0.5, 95% CI of 0.3–0.9]. On the contrary, hookworm species was highly predictive of uncomplicated and severe malaria [OR = 7.8, 95% (CI of 1.8–33.9) and 49.7 (95% CI of 1.9–1298.9) respectively]. Generally, children infected with STH had higher geometric mean time to first clearance of parasitemia. Conclusion: The findings of a protective effect of E. vermicularis and an enhancing effect of hookworms may explain the contradictory results found in the literature about impact of helminths on clinical malaria. More insight should be gained on possible mechanisms for these opposite effects. These results should not deter at this stage deworming programs but rather foster implementation of integrated control program for these two common parasites. Author summary: Contradictory results have been published on the impact of intestinal worm infections on malaria and there is an ongoing debate on whether general anti-helminthic treatment is desirable. For the first time, we provide highlights for the case of Plasmodium and Enterobius vermicularis co-infection in Tanzanian children. In our findings, helminth infection was shown to be associated with a protective effect on the development of clinical malaria. However, when looking at the specific effect of different helminth species, E. vermicularis was indeed protective against clinical malaria but co-infection with hookworm was associated with uncomplicated and even more so severe malaria. We speculate that specific effects of STH species tend to be obscured when analysis is carried out with all helminths aggregated. The opposite effects on malaria of all helminths reported in previous studies could be due to the individual effect of the most prevalent helminth species within the studied population. Further studies are needed to understand the impact of helminth species in low and high intensity areas in different age- and at-risk groups to better advise helminth and malaria control programs. [ABSTRACT FROM AUTHOR]

Breast cancer plays a significant role in affecting female mortality. Researchers are actively seeking to develop early detection methods of breast cancer. Several technologies contributed to the reduction in mortality rate from this disease, but early detection contributes most to preventing disease spread, breast amputation and death. Thermography is a promising technology for early diagnosis where thermal cameras employed are of high resolution and sensitivity. The combination of Artificial Intelligence (AI) with thermal images is an effective tool to detect early stage breast cancer and is foreseen to provide impressive predictability levels. This paper reviews systematically the related works employing thermography with AI highlighting their contributions and drawbacks and proposing open issues for research. Several different types of Artificial Neural Networks (ANNs) and deep learning models were used in the literature to process thermographic images of breast cancer, such as Radial Basis Function Network (RBFN), K-Nearest Neighbors (KNN), Probability Neural Network (PNN), Support Vector Machine (SVM), ResNet50, SeResNet50, V Net, Bayes Net, Convolutional Neural Networks (CNN), Convolutional and DeConvolutional Neural Networks (C-DCNN), VGG-16, Hybrid (ResNet-50 and V-Net), ResNet101, DenseNet and InceptionV3. Previous studies were found limited to varying the numbers of thermal images used mostly from DMR-IR database. In addition, analysis of the literature indicate that several factors do affect the performance of the Neural Network used, such as Database, optimization method, Network model and extracted features. However, due to small sample size used, most of the studies achieved a classification accuracy of 80% to 100%.

Nowadays, the surge in energy demand due to economic growth and extreme weather conditions has put immense pressure on the usage of fossil fuels in Kuwait. As a result, scheduled load-shedding is performed in some regions during the summer season to meet the energy demand. To address this issue, this paper proposes a photovoltaic-based street lighting system as an alternative solution to meet the rising energy demand in Kuwait during the daytime. This study initially investigates the existing street lighting systems in the state of Kuwait. Subsequently, three different configurations of photovoltaic panels are proposed based on the existing streetlight pole structures. The simulation models are then developed and evaluated using physical security information management and PVSyst simulation platforms, aiming to validate their performance against conventional power generation models in Kuwait. The proposed photovoltaic system is designed based on feedback information collected from the existing installed capacity. Finally, an overall energy model is presented to demonstrate how solar potential can offset energy consumption during peak demand hours. Practical testbed data from the Al-Jahra residential area of Kuwait is used for validation. The results indicate that the proposed photovoltaic street lighting system can generate a maximum power output of 18.8 GWh in August and a minimum of 11.8 GWh in December, compared to the monthly consumption of 30.45 GWh. The study showcases the economic viability of the solution, with an average degradation ratio of 13% of the total cost. Moreover, the proposed system contributes to a reduction in CO2 emissions from traditional power plants. [ABSTRACT FROM AUTHOR]

Agile malaria surveillance and response systems that can be sustained over time are needed for the optimal design of control programs (1,2). Rates of Plasmodium falciparum infection among pregnant women [...]

Immunization with attenuated Plasmodium falciparum sporozoites (PfSPZs) has been shown to be protective against malaria, but the features of the antibody response induced by this treatment remain unclear. To investigate this response in detail, we isolated IgM and IgG monoclonal antibodies from Tanzanian volunteers who were immunized with repeated injection of Sanaria PfSPZ Vaccine and who were found to be protected from controlled human malaria infection with infectious homologous PfSPZs. All isolated IgG monoclonal antibodies bound to P. falciparum circumsporozoite protein (PfCSP) and recognized distinct epitopes in its N terminus, NANP-repeat region, and C terminus. Strikingly, the most effective antibodies, as determined in a humanized mouse model, bound not only to the repeat region, but also to a minimal peptide at the PfCSP N-terminal junction that is not in the RTS,S vaccine. These dual-specific antibodies were isolated from different donors and were encoded by VH3-30 or VH3-33 alleles that encode tryptophan or arginine at position 52. Using structural and mutational data, we describe the elements required for germline recognition and affinity maturation. Our study provides potent neutralizing antibodies and relevant information for lineage-targeted vaccine design and immunization strategies., Author(s): Joshua Tan [1, 2]; Brandon K Sack [3]; David Oyen [4]; Isabelle Zenklusen [5, 6]; Luca Piccoli [1]; Sonia Barbieri [1]; Mathilde Foglierini [1, 7]; Chiara Silacci Fregni [1]; [...]