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Microalgae exhibit remarkable potential as a feedstock for biofuel production compared with other sources, owing to their high areal productivity, low environmental effect, and negligible influence on food security. However, the primary obstacle to the commercialization of algae-based biofuels is the high economic cost due to the low-yield lipid content in the microalgae biomass. Maximizing biomass and lipid production is crucial to improve the economic viability of microalgae for biofuels. Identifying appropriate algal strains, particularly from indigenous environments, and developing those ‘platform strains’ using mutagenesis and genetic-engineering techniques is preferable. The provided discussion of conventional methods to increase microalgae’s biomass and lipid productivity mostly entailed adjusting environmental (such as temperature, light, and salinity) and nutritional (such as nitrogen and phosphorus) parameters. This review illustrated a comprehensive overview of biotechnological approaches and the recent strategies to enhance the lipid productivity of microalgae. The research also emphasized the need to streamline engineering strategies with the aid of recent advancements in DNA-manipulation techniques to hinder the existing biological intricacies in lipogenesis. This review also discussed the current economic and commercialization of this algal biorefinery along with the drawbacks.

Malaria continues to pose a significant global health challenge, with its persistent transmission creating major difficulties for healthcare systems worldwide. Tackling this problem calls for innovative and effective methods to enhance understanding and control of the disease. In this work, we proposed a fractional-order mathematical model to study the dynamics of malaria transmission, integrating essential control measures such as treatment of humans and management of mosquito populations. The model employed three different types of non-integer order differential operators: the Caputo operator, the Caputo–Fabrizio operator with exponential decay, and the Atangana–Baleanu operator with an extended Mittag–Leffler kernel. Using fixed-point theory, we proved the existence and uniqueness of solutions for the proposed model. Numerical simulations are carried out to assess the impact of varying fractional orders on the progression of the disease. The results revealed that increasing the fractional order slows down the spread of malaria, reduces the peak number of infections, and prolongs the duration of outbreaks highlighting the memory-dependent nature of fractional systems. Our findings demonstrated that fractional-order models offer a more accurate and flexible approach to capturing the complex dynamics of malaria transmission. The study underscores the importance of integrating both therapeutic interventions and vector control strategies in reducing disease burden. Based on the findings of this study, we recommended the integration of fractional order modeling into malaria control strategies, as it captures the memory effects and long-term dynamics of disease transmission more accurately than classical models. Public health programs should adopt combined intervention approaches incorporating both effective treatment and vector control measures to significantly reduce infection rates. Furthermore, control efforts should be sustained over time, as fractional models reveal that short-term interventions may not be sufficient in curbing prolonged outbreaks. Policymakers are encouraged to use insights from these models to design adaptive, data-driven strategies that enhance the efficiency and sustainability of malaria control programs.

Malaria, a life-threatening disease responsible for millions of deaths worldwide, remains a major public health issue, especially in under-resourced regions. It is caused by Plasmodium parasites, transmitted through mosquito bites, and disproportionately affects vulnerable groups like children and pregnant women. To improve understanding and management of malaria transmission, we investigated different mathematical models, traditionally based on integer-order derivatives. In this study, we introduced a novel approach using a fractional-order mathematical model to evaluate how treatment strategies impact malaria’s spread. Initially, we modeled limited treatment scenarios with integer-order nonlinear differential equations. However, recognizing the complexity of malaria dynamics, we enhanced the model with fractional-order derivatives and power laws to capture a more detailed picture of disease behavior. The research established conditions for solution existence and uniqueness within the fractional framework and assessed the stability of the endemic equilibrium using the Lyapunov function technique. A sensitivity analysis of the basic reproduction number identified key factors influencing malaria transmission. Using the fractional Adams–Bashforth–Moulton method, we simulated various scenarios to explore the effects of model parameters and fractional-order values. Visual tools like surface and contour plots helped illustrate the findings. The results showed that improving treatment strategies and implementing preventive measures, such as mosquito control and timely medication, significantly reduced malaria cases. On the other hand, factors like increased mosquito contact and ineffective treatments aggravated the disease’s impact. This study provided valuable insights into malaria dynamics, highlighting the critical need for sustained efforts in treatment and prevention to mitigate its devastating effects on communities.

Tuberculosis (TB) remains one of the top infectious disease killers worldwide, with an estimated 10.6 million new cases and 1.3 million deaths reported in 2022 alone (WHO, 2023). The COVID-19 pandemic has further disrupted TB control efforts by limiting access to healthcare services, interrupting treatment regimens, and delaying diagnoses and leading to a resurgence in TB transmission. Tuberculosis is caused by Mycobacterium tuberculosis and spread through the air, TB posing a serious threat to vulnerable populations, especially those with weakened immune systems such as individuals living with HIV. These challenges emphasize the need for more robust and realistic modeling approaches to inform policy and intervention. In this study, We incorporated fractional-order derivatives and applied the Adams–Bashforth method to better understand how TB spreads and how it can be controlled. The model divides the population into six key groups: those susceptible to infection, exposed individuals, people with acute TB, those with chronic TB, individuals undergoing treatment, and those who have recovered. To capture the complexities of TB transmission, we incorporated fractional-order derivatives along with the Adams–Bashforth method, allowing us to account for memory effects and more accurately reflect real-world dynamics. Sensitivity analysis revealed that increasing treatment rates significantly improves recovery outcomes. In addition, we conducted a quantitative analysis of the model, deriving the basic reproduction number ( R ₀) using the next-generation matrix method. The results show that the endemic equilibrium is globally and asymptotically stable when R ₀ > 1, indicating that TB will persist in the population under these conditions. Conversely, when R ₀ < 1, the disease will die out over time, highlighting the critical role of reducing transmission and increasing treatment to achieve effective TB control. The simulations also explored various intervention strategies, including improved treatment access, faster diagnosis, and recognition of nonlinear transmission dynamicsThese results emphsize the importance of timely intervention and provide actionable insights for strengthening TB control policies.

The effect of salt stress on pigment synthesis and antioxidant enzyme activity as well as in the genes involved in the biosynthetic pathway of bixin was studied. The 14-day germinated seedlings of Bixa orellana were induced into the various NaCl concentration (0, 25, 50, 75, 100 mM). After 45 days, leaves were taken for pigment analysis, antioxidant assays, and gene expression analysis to study the response of salt stress. The pigment content such as chlorophyll level was increased upon salt stress with a reduction in total carotenoid clearly indicating the adaptability of plants towards the stressed state. The level of β-carotene was increased in the highest concentration of salt stress treatment. The secondary metabolites such as bixin and abscisic acid (ABA) content were also high in elevated concentration of salt-treated seedling than control. The antioxidant enzyme activity was increased with the highest dose of salt stress suggesting the antioxidant enzymes to protect the plant from the deleterious effects. The mRNA transcript gene of the carotenoid biosynthetic pathway such as phytoene synthase (PSY), 1-deoxyxylulose-5-phosphate synthase (DXS), phytoene desaturase (PDS), beta-lycopene cyclase (LCY-β), epsilon lycopene cyclase (LCY-ε), carboxyl methyl transferase (CMT), aldehyde dehydrogenase (ADH), lycopene cleavage dioxygenase (LCD), and carotenoid cleavage dioxygenase (CCD) showed differential expression pattern under salt stress. In addendum, we studied the co-expression network analysis of gene to assess the co-related genes associated in the biosynthesis pathway of carotenoid. From the co-expression analysis result showed, the LCY, PDS, and PSY genes were closely correlated with other genes. These finding may provide insight to the plants to exist in the stress condition and to improve the industrially important pigment production.

This work is aimed at formulating a mathematical model for the control of mosquito population using sterile insect technology (SIT). SIT is an environmental friendly method, which depends on the release of sterile male mosquitoes that compete with wild male mosquitoes and mate with wild female mosquitoes, which leads to the production of no offspring. The basic offspring number of the mosquitoes’ population was computed, after which we investigated the existence of two equilibrium points of the model. When the basic offspring number of the model M0, is less than or equal to 1, a mosquito extinction equilibrium point E2, which is often biologically unattainable, was shown to exits. On the other hand, if M0>1, we have the nonnegative equilibrium point E1 which is shown to be both locally and globally asymptotically stable whenever M0>1. Local sensitivity analysis was then performed to know the parameters that should be targeted by control intervention strategies and result shows that female mating probability to be with the sterile male mosquitoes ρS, mating rate of the sterile mosquito β2, and natural death rates of both aquatic and female mosquitoesμA+μF have greater impacts on the reduction and elimination of mosquitoes from a population. Simulation of the model shows that enough release of sterile male mosquitoes into the population of the wild mosquitoes controls the mosquito population and as such can reduce the spread of mosquito borne disease such as Zika.

Lassa fever is an animal-borne acute viral illness caused by the Lassa virus. This disease is endemic in parts of West Africa including Benin, Ghana, Guinea, Liberia, Mali, Sierra Leone, and Nigeria. We formulate a mathematical model for Lassa fever disease transmission under the assumption of a homogeneously mixed population. We highlighted the basic factors influencing the transmission of Lassa fever and also determined and analyzed the important mathematical features of the model. We extended the model by introducing various control intervention measures, like external protection, isolation, treatment, and rodent control. The extended model was analyzed and compared with the basic model by appropriate qualitative analysis and numerical simulation approach. We invoked the optimal control theory so as to determine how to reduce the spread of the disease with minimum cost.

Co‐infections such as malaria and tuberculosis pose significant public health challenges, particularly in regions where both diseases are endemic. Despite the global burden of these infections, their combined transmission dynamics remain poorly understood, highlighting the importance of this study. We develop a comprehensive mathematical model that captures the complex interactions between malaria and tuberculosis within a human population. By decomposing the system into disease‐specific sub‐models, we conduct a rigorous theoretical analysis of their individual and joint behaviors. A key result of this study is the identification of backward bifurcation in the co‐infection model an—important finding that departs from traditional models which assume that reducing the basic reproduction number (R0) below one ensures disease eradication. Our analysis reveals that co‐infection introduces nonlinear dynamics that make disease control more challenging, necessitating more nuanced and aggressive intervention strategies. Additionally, a sensitivity analysis pinpoints the most influential parameters driving transmission, such as contact rates and treatment effectiveness, providing valuable insights for public health decision‐making. It was concluded that malaria‐tuberculosis co‐infection requires integrated control strategies that account for their interactions rather than addressing each disease in isolation. The study offers a robust mathematical framework that not only advances theoretical understanding but also supports evidence‐based policymaking in the fight against these deadly diseases.

Introduction: Tuberculosis (TB) continues to be a public health problem globally. The burden is further exacerbated in developing countries like Nigeria, by poor diagnosis, management and treatment, as well as rapid emergence of drug-resistant TB. This study was conducted to evaluate the prevalence of drug-resistant TB, determine the rpoB gene mutation patterns of Mycobacterium tuberculosis (MTB) and model the dynamics of multidrug resistant TB (MDR-TB) in Enugu, Nigeria. Methodology: A total of 868 samples, from patients accessing DOTS services in designated centres within the zone, were screened by sputum-smear microscopy, while 207 samples were screened by Nucleic Acid Amplification (Xpert® MTB/Rif) Test (NAAT). A deterministic model was formulated to study the transmission dynamics of TB and MDR-TB, using live data generated through epidemiological study. Results: The results showed TB prevalence values of 22.1% and 21.3% by sputum-smear and NAAT assays, respectively. Analysis of the rifampicin resistance patterns showed the highest occurrence of mutations (50%) along codons 523 – 527. Factors such as combination therapy, multiple therapy and compliance to treatment had influence on both prevalence and development of TB drug resistance in the population. Conclusions: This first documentation of Rifampicin resistance patterns in MTB from Nigeria shows that a majority of rpoB gene mutations occurred along codons 523 to 527, contrary to the widely reported codon 531 mutation and that multiple interventions such as combination therapy, with good compliance to treatment are needed to reduce both prevalence and development of TB drug resistance in the population.

Tuberculosis (TB), caused by Mycobacterium tuberculosis , remains a leading cause of death from a single infectious agent, particularly in low-resource settings. Classical models often fail to capture the memory-dependent and complex dynamics of TB transmission. This study introduces a novel fractional-order mathematical model using the Atangana-Baleanu-Caputo (ABC) derivative to incorporate vaccination, reinfection, and environmental interventions. The existence and uniqueness of solutions are established using Schauder and Banach fixed-point theorems. Numerical simulations showed that decreasing the fractional order significantly reduces the rate of disease spread, lowers the peak incidence, and extends the duration of the epidemic curve, reflecting the hereditary and temporal properties captured by fractional calculus, while higher fractional orders increase the basic reproduction number ( ) due to enhanced memory effects. Sensitivity analysis revealed that vaccination, timely treatment, and environmental hygiene are the most effective measures for reducing both ( ) and cumulative incidence. The study showed that fractional-order models provide more realistic epidemic trajectories than classical integer-order models and offer a robust framework for guiding TB control strategies and policy planning.