Advancing the frontiers of virology in Africa: The African Virologists Network's Mission and Progress.

Africa has long been a hotspot for emerging and re-emerging viral infections such as Ebola, Mpox, Lassa fever, HIV, and arboviruses. Despite the continent's critical role in virology, African virologists have historically lacked a dedicated platform for collaboration and influence in global health policies. The African Virologists Network (AVN) was established in 2019 to address these gaps, fostering research collaboration, knowledge exchange, and capacity building.

Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET) and 17,649 ECG-single-photon emission computed tomography (SPECT) data pairs, the ML model is trained with a swarm intelligence approach and support vector regression (SVR). The model achieves a receiver-operator curve (ROC) area under the curve (AUC) of 0.

The prognostic effect of infiltrating immune cells is shaped by proximal M2 macrophages in lung adenocarcinoma.

The spatial arrangement of immune cells within the tumor microenvironment (TME) and their interactions play critical roles in the initiation and development of cancer. Several advanced technologies such as imaging mass cytometry (IMC) providing the immunological landscape of the TME with single-cell resolution. In this study, we develop a new method to quantify the spatial proximity between different cell types based on single-cell spatial data.

Domestic violence against women with infertility attending gynaecological clinics in Northern Nigeria: A multi-centre study.

Background: Due to the high premium placed on childbearing, infertility puts a lot of stress on the family which may result in aggressive and irrational behaviour if not properly managed. Domestic violence (DV) against infertile women is a public health issue but under-reported especially in northern Nigeria.

Objectives: This study sought to identify the prevalence, pattern, and response to DV.

GIS-based multi-criteria decision models for barite exploration in Nigeria's Benue Trough.

Spatial predictive mapping using geographic information system (GIS) is considered an invaluable tool for reconnaissance-scale exploration of mineral resources. In this study, geospatial data on geophysics, remote sensing, and structural and lithological attributes were systematically integrated to prospect barite potential zones within the Mid-Nigerian Benue Trough (MBT). Correlation attribute evaluation was used to establish the relationship between mineral deposit occurrences and geospatial data, while data integration was implemented using the Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Additive Ratio Assessment (ARAS) multi-criteria models.

CoCo-ST: Comparing and Contrasting Spatial Transcriptomics data sets using graph contrastive learning.

Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures.

Joint multi-omics discriminant analysis with consistent representation learning using PANDA.

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

[F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478).

Experience with colostomy and colostomy reversal in children in Bauchi.

Background: Colostomy is one of the common surgical procedures performed in pediatric surgical practice. The aim of this study was to retrospectively review our experience with colostomy and closure (reversal) in children.

Patients And Methods: A retrospective review of the data of all children aged 15 years and below who had colostomy and colostomy closure in the past 5 years.

Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer.

Background: Predicting relapse and overall survival (OS) in early-stage non-small-cell lung cancer (NSCLC) patients remains challenging. Therefore, we hypothesized that detection of circulating tumor DNA (ctDNA) can identify patients with increased risk of relapse and that integrating radiological tumor volume measurement along with ctDNA detectability improves prediction of outcome.

Patients And Methods: We analyzed 366 serial plasma samples from 85 patients who underwent surgical resections and assessed ctDNA using a next-generation sequencing liquid biopsy assay, and measured tumor volume using a computed tomography-based three-dimensional annotation.

Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept.

Background: Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers.

Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis.

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels.

Optimal sites for agricultural and forest residues energy conversion plant using geographic information system.

The Federal Government of Nigeria (FGN) has committed to net-zero emission development pathways to respond to the Paris Agreement adopted in 2015. However, the country is in dire need of energy to support its developmental ambitions. Therefore, it is necessary to consider green energy technologies to support both socioeconomic development and to meet the FGN's emission reduction target.

Human papillomavirus spectrum of HPV-infected women in Nigeria: an analysis by next-generation sequencing and type-specific PCR.

Background: Human papillomavirus (HPV) infection and cervical cancer are leading health problems and causes of death in many parts of the world. There are ~ 200 HPV types that can infect humans. This study aims to understand the spectrum of HPV infections in Nigerian women with normal or abnormal cytology.

Molecular detection of SARS-CoV-2 infection in three geo-political zones of Nigeria: a cross-sectional study.

Introduction: sequel to the emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and its subsequent spread to all continents of the world, humans have continued to experience severe devastation to their health and economies. To control the spread of this virus, it is important to detect the infection in recently infected and asymptomatic individuals who are capable of infecting others. This study was designed to detect ongoing SARS-CoV-2 Infection among asymptomatic individuals in open markets across three geopolitical zones in Nigeria.

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

Background: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context.

Poxvirus Infections in Dairy Farms and Transhumance Cattle Herds in Nigeria.

Lumpy Skin disease (LSD) is an economically important disease in cattle caused by the LSD virus (LSDV) of the genus , while pseudocowpox (PCP) is a widely distributed zoonotic cattle disease caused by the PCP virus (PCPV) of the genus . Though both viral pox infections are reportedly present in Nigeria, similarities in their clinical presentation and limited access to laboratories often lead to misdiagnosis in the field. This study investigated suspected LSD outbreaks in organized and transhumance cattle herds in Nigeria in 2020.

Resource availability and barriers to delivering quality care for newborns in hospitals in the southern region of Malawi: A multisite observational study.

Facility-based births have increased in low and middle-income countries, but babies still die due to poor care. Improving care leads to better newborn outcomes. However, data are lacking on how well facilities are prepared to support.

Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer.

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy.

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.

Objectives: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients.

Factors impacting-stillbirth and neonatal death audit in Malawi: a qualitative study.

Background: Over one million babies are stillborn or die within the first 28 days of life each year due to preventable causes and poor-quality care in resource-constrained countries. Death audit may be a valuable tool for improving quality of care and decreasing mortality. However, challenges in implementing audit and their subsequent action plans have been reported, with few successfully implemented and sustained.