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INTRODUCTION: Several studies have assessed and validated the impact of exoskeletons on back and shoulder muscle activation; however, limited research has explored the role that exoskeletons could play in mitigating lower arm-related disorders. This study assessed the impact of Ironhand, an active hand exoskeleton (H-EXO) designed to reduce grip force exertion, on worker exertion levels using a two-phase experimental design. METHOD: Ten male participants performed a controlled, simulated drilling activity, while three male participants completed an uncontrolled concrete demolition activity. The impact of the exoskeleton was assessed in terms of muscle activity across three different muscles using electromyography (EMG), perceived exertion, and perceived effectiveness. RESULTS: Results indicate that peak muscle activation decreased across the target muscle group when the H-EXO was used, with the greatest reduction (27%) observed in the Extensor Carpi Radialis (ECR). Using the exoskeleton in controlled conditions did not significantly influence perceived exertion levels. Users indicated that the H-EXO was a valuable technology and expressed willingness to use it for future tasks. PRACTICAL APPLICATIONS: This study showcases how glove-based exoskeletons can potentially reduce wrist-related disorders, thereby improving safety and productivity among workers. Future work should assess the impact of the H-EXO in various tasks, different work environments and configurations, and among diverse user groups.

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The construction industry is witnessing a transformative shift towards sustainable and intelligent housing solutions driven by advancements in 3D printing, Artificial Intelligence (AI), and the Internet of Things (IoT). Several architectural and construction firms have adopted innovative technologies to make construction easier, sustainable, efficient, cheap, fast, low generation of waste etc. This explorative review critically examines the integration of these technologies in the construction of eco-friendly intelligent homes. Drawing on a comprehensive analysis of literature spanning from 2010 to 2024, the review explores the synergistic potential and challenges associated with amalgamating 3D printing, AI, and IoT in construction processes. The increase need of smart homes equipped with sensors that can sense and regulate temperature, prevent or control fire, sense gas leakage, motion detectors and alarms for security and other application is in high demand. These types of smart homes can only be achieved by integrating different technologies together which include 3D printing (3DP), AI and Internet of Things (IoT). Despite the growing research in the field of automated construction, there are few articles that attempt to integrate these technologies together for futuristic smart homes and potential of smart cities. This study is aim at providing up-to-date advancement in technological innovation within the construction sector with regards to applications of 3DP, IoT, and AI. Key findings highlight how 3D printing enables rapid prototyping and customization of building components, AI enhances energy efficiency and occupant comfort through predictive analytics and automation, while IoT facilitates real-time monitoring and control of building systems. Furthermore, the review discusses the environmental benefits, cost-effectiveness, and societal implications of adopting such integrated approaches. However, challenges such as regulatory barriers, technological limitations, and the need for skilled labor are identified as critical barriers to widespread implementation. Future research directions are proposed to address these challenges and further optimize the integration of 3D printing, AI, and IoT for the construction of sustainable intelligent homes. In this review article, the need for 3DP in construction, advantage and disadvantage of 3DP, (AI) and IoT and the application of these technologies in addressing challenges regarding 3DP and promoting sustainability in the construction industries were comprehensively explored.

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Background: The COVID-19 pandemic's first wave and subsequent lockdowns disrupted global healthcare systems, significantly impacting essential services including tuberculosis (TB) care. Non-adherence to anti-TB drugs is a critical concern, leading to treatment failure, drug resistance, and increased morbidity and mortality. This study assessed the rate and determinants of non-adherence to TB treatment among patients at TB centers during the first wave of the pandemic. Material and Methods: A cross-sectional study was conducted from June 15 to July 30, 2020, involving 255 TB patients at three centers in Mogadishu. Data were gathered using the Morisky Medication Adherence Scale-8 (MMAS-8) through structured interviews and analyzed using descriptive statistics and binary logistic regression. Results: The study found a 34.5% non-adherence rate during the pandemic. Key reasons for non-adherence included forgetting to take medication (33%), feeling well (29%), experiencing side effects (18%), and fear of contracting COVID-19 (16%). Significant factors associated with non-adherence were age groups 25-34 years (OR = 2.96, p = 0.024) and 35-44 years (OR = 4.55, p = 0.005), unemployment (OR = 2.57, p = 0.037), smoking (OR = 3.49, p = 0.029), tobacco use (OR = 4.15, p = 0.034), proximity to a health facility (OR = 0.44, p = 0.033), perception of healthcare providers as very friendly (OR = 0.24, p = 0.031) or friendly (OR = 0.45, p = 0.023), being in the continuous treatment phase (OR = 3.2, p < 0.001), and experiencing adverse treatment effects (OR = 2.42, p = 0.003). Conclusion: Non-adherence to anti-tuberculosis treatment was notably high in Mogadishu during the first wave of the pandemic, necessitating targeted interventions to improve adherence.

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Finding optimal trajectories for multiple traffic demands in a congested network is a challenging task. Optimal transport theory is a principled approach that has been used successfully to study various transportation problems. Its usage is limited by the lack of principled and flexible ways to incorporate realistic constraints. We propose a principled physics-based approach to impose constraints flexibly in optimal transport problems. Constraints are included in mirror descent dynamics using the D'Alembert-Lagrange principle from classical mechanics. This results in a sparse, local and linear approximation of the feasible set leading in many cases to closed-form updates.

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It has been realized throughout the years that an ideal combination of high toughness, hardness and strength is required in many engineering applications that need load-bearing capabilities. Ceramics and related materials have significant constraints for structural and particular non-structural applications due to their low toughness and limited strength while having substantially superior hardness than typical metallic materials. For example, hydroxyapatite (HAp) has gained attention for applications in orthopaedic implants, dental materials, drug delivery, etc. Researchers have continued to strive to produce HAp materials with reliable properties within the acceptable Weibull modulus (m) for load bearing. The Weibull analysis (WA) is a statistical analysis adopted widely in reliability applications to detect failure periods. Researchers have confirmed it to be an effective technique to get results on the reliability of materials at a moderately low rate with assured reliability of the material or component. This review summarizes the WA and the steps in the Weibull method for its reliability analysis to predict the failure rate of ceramics like HAp and other related materials. Also, the applications of WA for these materials were reviewed. From the review, it was discovered that Weibull distribution is proven to confer to the feeblest-link concept. For brittle materials, it was revealed that the Weibull Modulus ranges from 2 to 40, and environment, production processes, and comparative factors are well-thought-out contributing factors for reliability. In addition, the confidence interval can be up to 95 %. The frequently used technique for reliability valuation is to syndicate the Weibull statistics. Also, a very narrow distribution is desirable to offer the expected likelihood. Furthermore, when paired with trials, Monte Carlo simulations prove to be a very helpful tool for forecasting the dependability of different estimate techniques and their optimization. Finally, if the equivalent m is anticipated to be high, it signifies that the material has a high degree of homogeneity of properties and high reliability. WA can find application in predicting the dependability and lifetime of materials, making it widely utilized in engineering and other disciplines. It is especially useful for analysing data in which the likelihood of failure per unit of time varies over time.

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Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interactions. Identifying and addressing adverse effects caused by polypharmacy is crucial to ensure patient safety and improve healthcare results. This paper introduces a new method using Graph Convolutional Networks (GCN) to identify polypharmacy side effects. Our strategy involves developing a medicine interaction graph in which edges signify drug-drug intuitive predicated on pharmacological properties and hubs symbolize drugs. GCN is a well-suited profound learning procedure for graph-based representations of social information. It can be used to anticipate the probability of medicate unfavorable impacts and to memorize important representations of sedate intuitive. Tests were conducted on a huge dataset of patients' pharmaceutical records commented on with watched medicate unfavorable impacts in arrange to approve our strategy. Execution of the GCN show, which was prepared on a subset of this dataset, was evaluated through a disarray framework. The perplexity network shows the precision with which the show categories occasions. Our discoveries demonstrate empowering advance within the recognizable proof of antagonistic responses related with polypharmaceuticals. For cardiovascular system target drugs, GCN technique achieved an accuracy of 94.12%, precision of 86.56%, F1-Score of 88.56%, AUC of 89.74% and recall of 87.92%. For respiratory system target drugs, GCN technique achieved an accuracy of 93.38%, precision of 85.64%, F1-Score of 89.79%, AUC of 91.85% and recall of 86.35%. And for nervous system target drugs, GCN technique achieved an accuracy of 95.27%, precision of 88.36%, F1-Score of 86.49%, AUC of 88.83% and recall of 84.73%. This research provides a significant contribution to pharmacovigilance by proposing a data-driven method to detect and reduce polypharmacy side effects, thereby increasing patient safety and healthcare decision-making.

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BACKGROUND: Physical exercise helps to mitigate cardiovascular risks in people with diabetes mellitus (DM), but there are limited data in Nigeria. This study aimed to assess cardiovascular risk awareness, exercise practices and metabolic outcomes among Nigerians with diabetes. MATERIALS AND METHODS: We conducted a cross-sectional study at five tertiary hospitals using questionnaire interviews and clinical assessments. Participants' knowledge of cardiovascular risk factors and knowledge of exercise were assessed on 12- and 5-item scores, while exercise practices were classed as adequate if performed regularly on 3 or more days weekly for a total of 150 min or more based on the American Diabetes Association recommendations. Mean body mass index (BMI), blood pressure (BP), fasting blood glucose, serum haemoglobin A1C (HbA1c), lipid profile, urea, creatinine and uric acid were then compared among participant groups. RESULTS: We studied 426 participants with DM, 58.7% females. The mean age was 52.9 ± 13.1 years, with males significantly older than females (54.6 ± 12.2 vs. 51.8 ± 13.5 years; 95% confidence interval: 0.27-5.28, P = 0.03). The mean age at diabetes diagnosis was 44.8 ± 11.7 years, and the median duration of diabetes was 84 months. There was low knowledge of cardiovascular risk factors and low knowledge of exercise (mean scores of 2.94 and 2.31, respectively). Forty-three per cent of participants reported adequate exercise, which was significantly associated with younger age (P = 0.007), male gender (P = 0.001) and formal education (P = 0.021). Participants with adequate exercise had lower systolic BP and serum urea compared to those with inadequate exercise, but there were no significant differences in BMI, fasting glucose, HbA1c, serum lipids, creatinine or uric acid. CONCLUSION: Participants had low knowledge of cardiovascular risks and the appropriate exercise practices for diabetes patients. There is a need for better patient education on diabetes self-care and exercise at clinic visits.

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One of predominant contributors to global mortality is tuberculosis (TB), an infection caused by Mycobacterium tuberculosis (MTB). Inappropriate and ineffectual treatment can lead to the development of drug-resistant TB. One of the most common forms of drug-resistant TB is multidrug-resistant tuberculosis (MDR-TB), caused by mutations in the rpoB and katG genes that lead to resistance to anti-TB drugs, rifampicin (RIF) and isoniazid (INH), respectively. Although culturing remains the gold standard, it is not rapid thereby delaying potential treatment and potentially increasing the incidence of MDR-TB. In contrast, molecular techniques provide a highly sensitive and specific alternative. This review discusses the classification of biomarkers used to detect MDR-TB, some of the commonly used anti-TB drugs, and DNA mutations in MTB that lead to anti-TB resistance. The objective of this review is to increase awareness of the need for rapid and precise detection of MDR-TB cases to decrease morbidity and mortality of this infectious disease worldwide.

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INTRODUCTION: This study examined the effectiveness of mobile phone reminders in improving the completeness and timeliness of childhood immunization. METHOD: We conducted a parallel arm cluster randomized controlled trial in four primary health care facilities in Nigeria. Reminders were sent to eligible participants in the intervention group at specific intervals when their children were scheduled to receive the vaccines administered at the sixth, 10, and 14 weeks after birth. Immunization records of all participants' children were then tracked to assess their immunization status. RESULTS: The immunization status of the intervention (n = 275) and control (n = 261) arms was analyzed. Completeness and timeliness of the vaccine series were significantly higher (p < .001) among children of participants in the intervention (n = 169, 61.5% and n = 138, 50.2%) than those in the control group (n = 35, 13.4% and n = 13, 5%) arm. DISCUSSION: Mobile phone reminders were established to be effective in increasing the completeness and timeliness of childhood immunization.

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Helicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for diagnosing H. pylori involves labour-intensive examination of endoscopic biopsies by trained pathologists. However, this process can be time-consuming and may occasionally result in the oversight of small bacterial quantities. Our study explored the potential of five pre-trained models for binary classification of 204 histopathological images, distinguishing between H. pylori-positive and H. pylori-negative cases. These models include EfficientNet-b0, DenseNet-201, ResNet-101, MobileNet-v2, and Xception. To evaluate the models' performance, we conducted a five-fold cross-validation, ensuring the models' reliability across different subsets of the dataset. After extensive evaluation and comparison of the models, ResNet101 emerged as the most promising. It achieved an average accuracy of 0.920, with impressive scores for sensitivity, specificity, positive predictive value, negative predictive value, F1 score, Matthews's correlation coefficient, and Cohen's kappa coefficient. Our study achieved these robust results using a smaller dataset compared to previous studies, highlighting the efficacy of deep learning models even with limited data. These findings underscore the potential of deep learning models, particularly ResNet101, to support pathologists in achieving precise and dependable diagnostic procedures for H. pylori. This is particularly valuable in scenarios where swift and accurate diagnoses are essential.

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Background: Intracerebral hemorrhage (ICH) is a serious condition characterized by bleeding within the brain tissue. Although the use of sildenafil, a vasodilator agent for erectile dysfunction, has been associated with rare cases of ICH, the combination of sildenafil usage and smoking as risk factors for ICH has not yet been reported. This case report describes the occurrence of ICH in a patient with a history of both sildenafil usage and heavy smoking. Case Presentation: A 53-year-old male, with a history of smoking and regular sildenafil use, was brought to the emergency department due to loss of consciousness with right-side weakness, he initially experienced with nausea, vomiting and dizziness after taking sildenafil 100mg tablet once. The Glasgow Coma Score (GCS) was 10 with side hemiparesis. Non-contrast CT revealed left thalamic acute hemorrhage with ventricular extension. Furthermore, a head CT angiography ruled out any vascular anomalies after that the patient was admitted to the intensive care unit (ICU) for conservative management. After three days on clinical and neurological improvement, the patient was transferred to the inpatient ward for further management, monitoring and physiotherapy. On day 6, the patient was discharged and planned for flow up. Conclusion: This rare case highlights the need for further research and awareness regarding the potential risks associated with the combination of sildenafil and heavy smoking. Healthcare professionals should carefully evaluate the individual risk factors of patients, educate them about potential complications, and consider alternative treatments if necessary. Additionally, patients should be encouraged to quit smoking and adopt a healthy lifestyle to minimize the risk of cerebrovascular events.

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Background: Visceral leishmaniasis (VL) is fatal neglected parasitic illness caused by Leishmania donovani. The diagnosis remains a challenge due to the non-specific clinical symptoms, especially in areas where infections like malaria and limited access to diagnostic tools coexist. Here, we describe a case of late diagnosis of visceral leishmaniasis using tru-cut biopsy of the spleen and malaria co-infection. Case Presentation: Here case report, a 24-year-old patient from an endemic region of Somalia presented with fever, headache, abdominal pain, nausea, vomiting, and weight loss for two months. Initially, the patient received symptomatic treatment and a blood transfusion but showed no improvement. Physical examination revealed fever, pallor, and hepatosplenomegaly. Laboratory tests showed pancytopenia and positive rapid diagnostic test for plasmodium parasite antigen. Despite three days of anti-malarial treatment, the symptoms persisted, and hepatosplenomegaly worsened. Further investigations, including infectious disease tests, were conducted, ruling out HIV, viral hepatitis, Brucella, and Leishmania antibodies. Peripheral blood smear showed pancytopenia and bone marrow aspiration revealed no evidence of infection or malignancy. A tru-cut biopsy of the spleen was performed, confirming the diagnosis of visceral leishmaniasis. The patient received a combination therapy of sodium stibogluconate and paromomycin, leading to significant improvement. After completing treatment, the patient was discharged with normal spleen biopsy results. Conclusion: Visceral leishmaniasis (VL) is a challenging disease to diagnose, especially in areas where it coexists with other infectious diseases, such as malaria. Co-infection with malaria should also be considered in patients with fever and hepatosplenomegaly. A high index of suspicion is necessary for the timely diagnosis of VL, and a tru-cut biopsy of the spleen can be conducted in cases where other investigations are inconclusive in endemic areas. Early diagnosis and prompt treatment of visceral leishmaniasis are crucial to prevent complications and reduce mortality.

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The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10-6), Nanomolar nM (10-9), Picomolar pM (10-12), femtomolar fM (10-15), and attomolar aM (10-18).

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Introduction: Cerebral venous thrombosis (CVT) remains one of the most frequent cause of stroke in young adults although it is a rare disorder. It has a serious emergency manifestation during pregnancy and postpartum. It is frequently under diagnosed and limited publications in developing countries especially sub-Saharan Africa. Case Presentation: Here, we report the case of a 35-year-old female, para 8 and gravida 8, brought to emergency department on her ninth day postpartum, having severe headaches and confusion; during her admission to emergency, she suffered two episodes of generalized seizure. On admission, the patient's blood pressure was 200/120 mmHg. The patient was firstly diagnosed with postpartum eclampsia and managed with magnesium sulfate for seizures and hydralazine for blood pressure control. On ward admission, she continued having seizures and her level of consciousness decreased with left side weakness. An urgent MRI of head and MR venography performed immediately revealed acute thrombosis involving the anterior aspect of superior sagittal sinus and the left transverse sinus. The patient was moved to the ICU. Anti-coagulant LMW enoxaparin and anticonvulsant were started. After improving on clinical and biochemical tests, the patient was transferred to the inpatient ward on fifth day. Another two days later in the ward, she improved significantly, and then a head CT (contrast) angiography/venography was performed before she was discharge and showed no evidence of filling defect in cortical/dural venous sinuses. Conclusion: Cerebral venous sinus thrombosis (CVST) and postpartum eclampsia may have similar manifestations in young women of reproductive age. Early prompt diagnosing and therapeutic intervention of CVST can prevent further neurological deterioration and immediately improve the patient.

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A novel antenna structure is constructed from cascading multi-stage metamaterial (MTM) unit cells-based printed monopole antenna for 5G mobile communication networks. The proposed antenna is constructed from a printed conductive trace that fetches four MTM unit cells through four T-Resonators (TR) structures. Such a combination is introduced to enhance the antenna gain-bandwidth products at sub-6GHz bands after exiting the antenna with a coplanar waveguide (CPW) feed. The antenna circuitry is fabricated by etching a copper layer that is mounted on Taconic RF-43 substrate. Therefore, the proposed antenna occupies an effective area of 51 × 24 mm2. The proposed antenna provides an acceptable matching impedance with S11 ≤ -10 dB at 3.7 GHz, 4.6 GHz, 5.2 GHz, and 5.9 GHz. The antenna radiation patterns are evaluated at the frequency bands of interest with a gain average of 9.1-11.6 dBi. Later, to control the antenna performance, four optical switches based on LDR resistors are applied to control the antenna gain at 5.85 GHz, which is found to vary from 2 dBi to 11.6 dBi after varying the value of the LDR resistance from 700 Ω to 0 Ω, in descending manner. It is found that the proposed antenna provides an acceptable bit error rate (BER) with varying the antenna gain in a very acceptable manner in comparison to the ideal performance. Finally, the proposed antenna is fabricated to be tested experimentally in in free space and in close to the human body for portable applications.

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A novel design of a reconfigurable MIMO antenna array of a 3D geometry-based solar cell integration that is operating at sub-6 GHz for self-power applications in a 5G modern wireless communication network. The proposed antenna array provides three main frequency bands around 3.6 GHz, 3.9 GHz, and 4.9 GHz, with excellent matching impedance of S11 ≤ -10 dB. The proposed MIMO array is constructed from four antenna elements arranged on a cubical structure to provide a low mutual coupling, below -20 dB, over all frequency bands of interest. Each antenna element is excited with a coplanar waveguide (CPW). The proposed radiation patterns are controlled with two optical switches of Light Dependent Resistors (LDRs). The proposed antenna array is fabricated and tested experimentally in terms of S-parameters, gain and radiation patterns. The maximum gain is found to be 3.6 dBi, 6.9 dBi, and 3.5 dBi at 3.6 GHz, 3.9 GHz, and 4.9 GHz, respectively. It is realized that the proposed array realizes a significant beam forming by splitting the antenna beam and changing the main lobe direction at 3.9 GHz after changing LDR switching statuses. Such an antenna array is found to be very applicable for femtocell wireless communication networks in the 5G systems.

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Detecting communities in networks is important in various domains of applications. While a variety of methods exist to perform this task, recent efforts propose Optimal Transport (OT) principles combined with the geometric notion of Ollivier-Ricci curvature to classify nodes into groups by rigorously comparing the information encoded into nodes' neighborhoods. We present an OT-based approach that exploits recent advances in OT theory to allow tuning between different transportation regimes. This allows for better control of the information shared between nodes' neighborhoods. As a result, our model can flexibly capture different types of network structures and thus increase performance accuracy in recovering communities, compared to standard OT-based formulations. We test the performance of our algorithm on both synthetic and real networks, achieving a comparable or better performance than other OT-based methods in the former case, while finding communities that better represent node metadata in real data. This pushes further our understanding of geometric approaches in their ability to capture patterns in complex networks.

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Purpose: The recent increase in aluminum exposure and its effect on the development of the brain call for serious attention. The study investigated the behavioral and immunohistochemical changes in the cerebral cortex of Wistar rats following prenatal co-administration of ethyl acetate leaf fraction of Tamarindus indica (EATI) and aluminum chloride (AlCl3). Methods: Pregnant Wistar rats were divided into 5 groups (n=4). Group I (negative control), Group II-V were experimental groups treated with 200 mg/kg of AlCl3 s/c. Group III and IV received an additional 400 mg/kg and 800 mg/kg of EATI respectively, while Group V received an additional 300 mg/kg of Vitamin E for 14 days (prenatal days 7-21) via the oral route. The pups were then exposed to cliff avoidance, negative geotaxis, and elevated plus maze (EPM) test on the post-natal day (PoND) 4-6, 7-10, and 18 respectively. On PoND 21 pups were sacrificed, and the skull dissected to remove the brain. The harvested brain tissues were processed for Cresyl fast (CF) and glial fibrillary acid protein (GFAP). Results: The study showed that EATI administration during AlCl3 exposure was associated with significant improvement in sensory-motor development. The EPM, CF, and GFAP results revealed significant improvement in anxiety-like behavior, motor activities, GFAP expression, pyramidal cell count, and Nissl staining following prenatal EATI administration during AlCl3 exposure. Conclusion: The present study concludes that EATI was associated with some protective potential during prenatal AlCl3 exposure in Wistar rats.

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Tropical diseases (TDs) are among the leading cause of mortality and fatality globally. The emergence and reemergence of TDs continue to challenge healthcare system. Several tropical diseases such as yellow fever, tuberculosis, cholera, Ebola, HIV, rotavirus, dengue, and malaria outbreaks have led to endemics and epidemics around the world, resulting in millions of deaths. The increase in climate change, migration and urbanization, overcrowding, and other factors continue to increase the spread of TDs. More cases of TDs are recorded as a result of substandard health care systems and lack of access to clean water and food. Early diagnosis of these diseases is crucial for treatment and control. Despite the advancement and development of numerous diagnosis assays, the healthcare system is still hindered by many challenges which include low sensitivity, specificity, the need of trained pathologists, the use of chemicals and a lack of point of care (POC) diagnostic. In order to address these issues, scientists have adopted the use of CRISPR/Cas systems which are gene editing technologies that mimic bacterial immune pathways. Recent advances in CRISPR-based biotechnology have significantly expanded the development of biomolecular sensors for diagnosing diseases and understanding cellular signaling pathways. The CRISPR/Cas strategy plays an excellent role in the field of biosensors. The latest developments are evolving with the specific use of CRISPR, which aims for a fast and accurate sensor system. Thus, the aim of this review is to provide concise knowledge on TDs associated with mosquitoes in terms of pathology and epidemiology as well as background knowledge on CRISPR in prokaryotes and eukaryotes. Moreover, the study overviews the application of the CRISPR/Cas system for detection of TDs associated with mosquitoes.

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Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.

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