Cardiovascular Risk Awareness, Exercise Practices and Metabolic Outcomes among Patients with Diabetes Mellitus in Northern Nigeria: A Cross-sectional, Multicentre Study.

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.

Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review.

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).

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.

Effect of mobile phone text message and call reminders in the completeness of pentavalent vaccines in Kano state, Nigeria.

PURPOSE: This study assessed the effect of mobile phone text message and call reminders in the completeness of the pentavalent vaccine administered against diphtheria, pertussis, tetanus, hepatitis B, and Haemophilus influenza. DESIGN AND METHODS: The population for this quasi-experimental study were recent mothers of children not more than three weeks old selected from six local government areas of Kano State, Northern Nigeria. Three local government areas were each allocated to the intervention (reminder) and control groups of the study. Mobile phone reminders (SMS and follow-up calls) were sent to mothers in the reminder group three days to and on the due date of their child's schedule for the 1st, 2nd and 3rd doses of the pentavalent vaccine. All statistical data were entered into the Statistical Package for Social Sciences (SPSS) version 23.0 and analysed using descriptive and inferential statistics. RESULTS: A total of 541 mothers (271 in the intervention group and 270 in the control group) participated in the study. Completion rates for the three doses of the pentavalent vaccine were observed to be higher for children in the reminder group (n = 161, 59.4%) compared to those in the control group (n = 92, 34.1%). CONCLUSION: Mobile phone reminders were effective and improved the rate of completeness of the pentavalent vaccine in the studied population. In line with the findings of the study, it is recommended that future studies should focus on identifying the best approach to remind parents either through SMS, phone calls, or voice messages or a combination of any of the approaches. PRACTICE IMPLICATION: Nurses and other health care providers will have empirical evidence on the use of mobile phone technology to improve the health and wellbeing of children by protecting them from vaccine-preventable diseases.

Development of paediatric critical care in northwestern Nigeria: Initial implementation with a needs assessment model.

INTRODUCTION: There is high paediatric morbidity and mortality in northwestern Nigeria, attributable in part to vaccine-preventable illnesses and lack of comprehensive training of medical and nursing staff in the healthcare delivery of paediatric critical care. Pediatric Universal Life-Saving Effort Inc. (PULSE), a New York-based nonprofit organisation with a mission to develop paediatric critical care in resource-limited settings, collaborated with Aminu Kano University Teaching Hospital to decrease the gaps in knowledge and skills of medical and nursing personnel. The joint effort also aims to address and remove barriers to the delivery of paediatric critical care in northwestern Nigeria. The primary objective was to perform a needs assessment for paediatric intensive care resources in northwestern Nigeria, identify barriers to delivering these services, and designate a hub for the development of paediatric critical care educational programs for healthcare professionals. METHODS: An anonymous survey was designed and distributed using SurveyMonkey® online software to medical and nursing staff from nine healthcare institutions in northwestern Nigeria. RESULTS: Analysis from 67 responses revealed that care delivered to critically ill paediatric patients was by anaesthesiologists (77%), pediatricians (26%), and adult intensive care specialists (10%). The acquisition of clinical skills was perceived to be an essential need (65%), followed by adequate staffing of critical care units (19%), continuing medical and nursing education (13%), and availability of medical equipment (3%). DISCUSSION: There is an identified need for paediatric critical care training and resources in northwestern Nigeria. CONCLUSION: The needs assessment conducted has provided important results that will form the basis for building staff capacity and training programs for paediatric critical care in northwestern Nigeria.

Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.

Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

Classification of H. pylori Infection from Histopathological Images Using Deep Learning.

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.

Multidrug-resistant tuberculosis.

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.

Utilization of Mobile Reminders in Improving the Completeness and Timeliness of Routine Childhood Immunization in Kano Metropolis, Nigeria: A Randomized Controlled Trial.

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.

Sildenafil Use and Cigarette Smoking Associated with Intracerebral Hemorrhage: A Rare Case Report.

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.

A Printed Reconfigurable Monopole Antenna Based on a Novel Metamaterial Structures for 5G Applications.

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.

Postpartum Cerebral Venous Sinus Thrombosis: A Case Report.

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.

A Late Diagnosis of Visceral Leishmaniasis Using Tru-Cut Biopsy of the Spleen and Malaria Co-Infection - A Diagnostic Challenge: A Case Report in Somalia.

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.

Nigerian lawyers and reproductive health rights: a survey of knowledge, practices and opinions on law reforms among the bar and bench in north eastern Nigeria.

The objective of this study was to determine the knowledge and attitudes of practicing Nigerian lawyers towards issues relating to reproductive health and reproductive rights, and their opinions about abortion law reform. It was a population- based study which consisted of interviews with practicing lawyers in north-east Nigeria. The results showed poor knowledge of issues related to reproductive health and reproductive rights among the lawyers. However, the majority (56.9%) disagreed that a woman can practice family planning without the consent of her husband. The prevalence of contraceptive use among the lawyers was low and attitude to abortion law not satisfactory. Only few lawyers (22.4%) supported safe abortion in cases of failed contraception. We conclude that reproductive health advocates must target legal professionals with a view to educating them on issues relating to sexual and reproductive health and rights. Lawyers in Nigeria should undergo capacity building in reproductive health laws and be encouraged to specialize in reproductive rights protection as obtainable in other developed countries.

Sustainable optimal transport in multilayer networks.

Traffic congestion is one of the major challenges faced by the transportation industry. While this problem carries a high economic and environmental cost, the need for an efficient design of optimal paths for passengers in multilayer network infrastructures is imperative. We consider an approach based on optimal transport theory to route passengers preferably along layers that are more carbon-efficient than the road, e.g., rails. By analyzing the impact of this choice on performance, we find that this approach reduces carbon emissions considerably compared to shortest-path minimization. Similarly, we find that this approach distributes traffic more homogeneously, thus alleviating the risk of traffic congestion. Our results shed light on the impact of distributing traffic flexibly across layers guided by optimal transport theory.

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview.

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10-18 M), Femtomolar (10-15 M) and Picomolar (10-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10-9 M and micromolar 10-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

Computer aided detection of tuberculosis using two classifiers.

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.

Optically Remote Control of Miniaturized 3D Reconfigurable CRLH Printed Self-Powered MIMO Antenna Array for 5G Applications.

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.

Community detection in networks by dynamical optimal transport formulation.

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.