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. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples.

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 18F-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. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

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

[18F]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). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

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. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.

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. Materials and methods: We studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters ("habitats"). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value. Results: Habitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E - 9) higher than the clinical and blood model (post-test probability of 22%). Conclusion: Habitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma.

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

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. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. FUNDING: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Phase engineering of nickel-based sulfides toward robust sodium-ion batteries.

Nickel-based sulfides are considered promising materials for sodium-ion batteries (SIBs) anodes due to their abundant resources and attractive theoretical capacity. However, their application is limited by slow diffusion kinetics and severe volume changes during cycling. Herein, we demonstrate a facile strategy for the synthesis of nitrogen-doped reduced graphene oxide (N-rGO) wrapped Ni3S2 nanocrystals composites (Ni3S2-N-rGO-700 °C) through the cubic NiS2 precursor under high temperature (700 ℃). Benefitting from the variation in crystal phase structure and robust coupling effect between the Ni3S2 nanocrystals and N-rGO matrix, the Ni3S2-N-rGO-700 °C exhibits enhanced conductivity, fast ion diffusion kinetics and outstanding structural stability. As a result, the Ni3S2-N-rGO-700 °C delivers excellent rate capability (345.17 mAh g-1 at a high current density of 5 A g-1) and long-term cyclic stability over 400 cycles at 2 A g-1 with a high reversible capacity of 377 mAh g-1 when evaluated as anodes for SIBs. This study open a promising avenue to realize advanced metal sulfide materials with desirable electrochemical activity and stability for energy storage applications.

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. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo.

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering.

Identifying the progression of chronic lymphocytic leukemia (CLL) to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications as it prompts a major change in patient management. However, the differentiation between these disease phases may be challenging in routine practice. Unsupervised learning has gained increased attention because of its substantial potential in data intrinsic pattern discovery. Here, we demonstrate that cellular feature engineering, identifying cellular phenotypes via unsupervised clustering, provides the most robust analytic performance in analyzing digitized pathology slides (accuracy = 0.925, AUC = 0.978) when compared to alternative approaches, such as mixed features, supervised features, unsupervised/mixed/supervised feature fusion and selection, as well as patch-based convolutional neural network (CNN) feature extraction. We further validate the reproducibility and robustness of unsupervised feature extraction via stability and repeated splitting analysis, supporting its utility as a diagnostic aid in identifying CLL patients with histologic evidence of disease progression. The outcome of this study serves as proof of principle using an unsupervised machine learning scheme to enhance the diagnostic accuracy of the heterogeneous histology patterns that pathologists might not easily see.

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. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology.

We present CellSpatialGraph, an integrated clustering and graph-based framework, to investigate the cellular spatial structure. Due to the lack of a clear understanding of the cell subtypes in the tumor microenvironment, unsupervised learning is applied to uncover cell phenotypes. Then, we build local cell graphs, referred to as supercells, to model the cell-to-cell relationships at a local scale. After that, we apply clustering again to identify the subtypes of supercells. In the end, we build a global graph to summarize supercell-to-supercell interactions, from which we extract features to classify different disease subtypes.

Nigeria's cocoa exports: a gravity model approach / Exportações de cacau da Nigéria: uma abordagem do modelo gravitacional

ABSTRACT: What are the major factors affecting Nigeria's cocoa export flows? In answering this question, the authors suggest a commodity-specific gravity model with three different analytical approaches, (the Heckman Sample Selection Model, the Generalised Least Square, and the Poisson Pseudo Maximum Likelihood), based on a period of 24 years of panel data for Nigeria and it's 36 importing partners to estimate the models. The results showed that GDP, exchange rate policy, WTO, EU, and colonial link are positively associated with the Nigerian cocoa export flows. Further, the negative impact of the GDP per capita, landlocked, distance, AU, and ECOWAS are observed. The need for the expansion of exports to the trading partners, especially the EU members (Netherlands, Germany, France, United Kingdom, Belgium, Spain, etc.), Canada, Malaysia, and the USA is particularly highlighted. These results are important for the formulation of future trade policy that could boost up the Nigerian cocoa exports. This would eventually contribute to the diversification of the Nigerian exports and also enhance the country's foreign earnings.

RESUMO: Quais são os principais fatores que afetam os fluxos de exportação de cacau da Nigéria? Ao responder a esta pergunta, os autores sugerem um modelo de gravidade específica de mercadoria com três abordagens analíticas diferentes (o Modelo de Seleção de Amostras de Heckman, o Mínimo Quadrado Generalizado e a Pseudo Máxima Verossimilhança de Poisson), com base em um período de 24 anos de dados em painel para a Nigéria e seus parceiros importadores para estimar os modelos. Os resultados mostram que o PIB, a política cambial, a OMC, a UE e a ligação colonial estão positivamente associados aos fluxos de exportação de cacau da Nigéria. Além disso, é observado o impacto negativo do PIB per capita, sem litoral, distância, UA e CEDEAO. Destaca-se a necessidade de ampliação das exportações para os parceiros comerciais, especialmente os membros da UE (Holanda, Alemanha, França, Reino Unido, Bélgica, Espanha, etc.), Canadá, Malásia e Estados Unidos. Esses resultados são importantes para a formulação de uma política comercial futura que possa impulsionar as exportações de cacau nigeriano. Isso acabaria por contribuir para a diversificação das exportações nigerianas e também aumentar as receitas externas do país.

Clinical Examination among Medical Students: Assessment and Comparison of the Strengths and Weaknesses of Objective Structured Clinical Examination and Conventional Examination.

BACKGROUND: Traditional clinical examination is perceived to be biased and to overcome this, objective structured clinical examination (OSCE) was introduced. AIMS: The aim was to assess the students' exposure and perception of OSCE as well as its strengths and weaknesses in comparison to conventional clinical examination. SUBJECTS AND METHODS: A cross-sectional study of students who had surgical OSCE was conducted from July 2018 to October 2018 in Ahmadu Bello University, Zaria. Using a structured questionnaire, exposure and perception of OSCE as well its strength and weaknesses in comparison to the conventional clinical examination were assessed with dichotomous questions and a 5-point Likert scale. Data were analyzed with SPSS Version 20. RESULTS: One hundred and thirty-four students responded, mean aged 24.1 ± 4.3 years, with a male-to-female ratio of 2.5:1. Although 64.7% of them strongly agreed that OSCE is the standard mode of examination, only 36.1% strongly agreed that it was easier to pass. More than half of the students perceived that the content of the OSCE was appropriate, wide-scoped, unbiased, and brought out specific areas of weakness. Some students (38.1%), however, felt that there was a need for improvement in the OSCE process, especially in time allocation, but most of them (89.5%) prefer it and agree that it was superior to conventional clinical examination. CONCLUSIONS: The perception of the process and structure of OSCE among the medical students was good. Compared to conventional clinical examination, students found OSCE was easier and fairer, had a better examination of structure and level of coordination, and induced lesser anxiety and fatigue. However, the entire process of OSCE can be improved.

Sensitivity of Computed Tomography-guided Transthoracic Biopsies in a Nigerian Tertiary Institution.

INTRODUCTION: The indications for open biopsies for intrathoracic lesions have become almost negligible. This development was made possible by less invasive maneuvers such as computed tomography-guided (CT-guided) biopsy, thoracoscopy or video-assisted thoracoscopy, and bronchoscopy. CT-guided percutaneous lung biopsy was first reported in 1976. AIM OF STUDY: The aim of the study is to report our experience with CT-guided transthoracic biopsy. MATERIALS AND METHODS: Patients with clinical and radiological evidence of intrathoracic mass were counseled and consent obtained for the procedure. They were positioned in the gantry, either supine or prone. A scout scan of the entire chest was taken at 5 mm intervals. The procedure was carried out by the consultants and senior registrar. Following visualization of the lesion, its position in terms of depth and distance from the midline was measured with the machine in centimeter to determine the point of insertion of the trucut needle (14-18-G). The presumed site of the lesion was indicated with a metallic object held in place with two to three strips of plasters after cleaning the site with Povidone-iodine. After insertion, repeat scans were performed to confirm that the needle was within the mass. A minimum of 3 core cuts was taken to be certain that the samples were representative. The results were analyzed by the determination of means and percentages. RESULTS: Twenty-six patients underwent this procedure between 2011 and 2015. There were 15 males and 11 females (M:F = 1.4:1). The age range was between 30 and 99 years with a mean of 55 years. Histological diagnosis was obtained in 24 of the patients giving sensitivity of 92.3%. There were 3 mild complications giving a rate of 11.5%. The complications included a case of mild hemoptysis and two patients who had mild pneumothoraces which did not require tube thoracostomy. CONCLUSION: CT-guided biopsy is a reliable procedure for obtaining deep-seated intrathoracic biopsies with high sensitivity and minimal complication rate.

Vascular surgeries in West Africa: challenges and prospects.

BACKGROUND: The field of vascular surgery is evolving in sub-Saharan Africa but the practice is bedeviled by lack of expertise and infrastructure challenges. The consequences are a low volume of operations and a dearth of data. Available data are not representative of the wider picture, therefore, this study was undertaken to evaluate the practice of vascular surgery in a tertiary institution, in the light of the prevailing challenges. METHODS: Data from all patients with vascular-related pathologies managed in our surgical outpatient clinic and accident and emergency wards were obtained from the clinic and in-patient records from January 2008 to December 2012. Age, sex, diagnosis, treatment, and complications were noted. There were 73 patients comprising 45 (61.6%) males and 28 (38.4%) females. The age range was 1-90 years (mean 43.5 years). RESULTS: The pathologies managed included end-stage renal disease (n = 36, 49.3%), nontraumatic and posttraumatic aneurysms (n = 13, 17.8%), vascular trauma (n = 12, 16.4%), peripheral vascular disease (n = 5, 6.9%), congenital vascular malformations (n = 4, 5.5%), and thrombotic diseases (n = 3, 4.1%). Fifty-four (74.0%) surgeries were performed, with a complication rate of 5.5% and 2.7% mortality. CONCLUSIONS: The practice of vascular surgery in Zaria, Nigeria, is fraught with challenges. The gap created by the dearth of skilled vascular surgeons is filled by competent cardiothoracic surgeons. Infrastructure decay and lack of prostheses limit the number and variety of operable cases. These challenges result in preventable morbidity and mortality.

Indications and complications of tube thoracostomy with improvised underwater seal bottles.

BACKGROUND: Tube thoracostomy is a lifesaving and frequently performed procedure in hospitals where the expertise and necessary tools are available. Where the ideal drainage receptacle is unavailable, the underwater seal device can be improvised with bottled water plastic can especially in emergency situations. AIMS AND OBJECTIVES: To determine the frequencies of the various indications and complications of tube thoracostomy with improvised underwater seal. MATERIALS AND METHODS: A cross-sectional study with a structured proforma was used for assessment over a 3-year period (May 2010-April 2013). The proforma was filled at the time of the procedure by the performing surgeon and patients were followed up with serial chest X-rays until certified cured. A 1.5 L bottled water container was used as the underwater seal receptacle. The data was analysed with SPSS 15 software program. RESULTS: A total of 167 patients were managed. There were 106 (63.5%) males and 61 (36.5%) females. The mean age was 34.85 ± 16.72 with a range of 1-80 years. The most frequent indication was for malignant/paramalignant effusion, 46 (27.5%). Others were trauma, 44 (26.3%), Parapneumonic effusion, 20 (12%), postthoracotomy 14 (8.4%), empyema thoracis 12 (7.2%), heart disease and tuberculous effusion 11 (6.6%) each, pneumothorax 8 (4.8%) and misdiagnosis 1 (0.6%). A hundred and one (60.5%) of the procedures were performed by registrars, 41 (24.6%) by consultants, house officers 15 (9%) and senior registrars 10 (6%). The overall complication rate was 16.8% with the more frequent complications been empyema (5.6%) and pneumothorax (3.6%). The average duration of tube placement was 13.02 ± 12.362 days and range of 1-110 days. CONCLUSION: Tube thoracostomy can be a relatively safe procedure with acceptable complication rates even with improvised underwater seal drainage bottles.

Tubal ectopic gestation associated with genital schistosomiasis: a case report.

Schistosoma are trematode blood flukes of the family Schistosomidae affecting the urinary and gastro-intestinal tracts. Riverine areas of the world such as in Africa, Eastern Mediterranean, Central American and East Asia are endemic for the disease, with S. haematobium accounting for most of the symptomatic genital infection. A case of a 25-year-old woman with 8 weeks amenorrhoea, lower abdominal pain and per vaginal bleeding was managed for ruptured ectopic pregnancy and discovered to have tubal infection by Schistosoma on histological examination is presented.

Zaria universal oxygenator holder phase I.

INTRODUCTION: The conduct of cardiopulmonary bypass surgery requires the use of equipment and devices like the oxygenator. The oxygenator comes in different makes and each manufacturer customizes the carrier or 'holder' of this device specific to their design. AIM: This paper presents an innovation designed to overcome the need to purchase a different holder for every oxygenator thereby cutting the cost. MATERIALS AND METHODS: A sheet of iron measuring 1.9 cm (width) × 0.1 cm (thickness) was used to design the holder circular main frame. Another sheet measuring 2 cm (width) × 0.6 cm (thickness) × 24 cm (length) was used to construct a V-shaped handle with the arms of the V attached to the main frame 7 cm apart. At the narrow base of the handle is a latch requiring two 13-gauge screws to attach the holder to the heart-lung machine. Within the circumference of the main frame are four T-shaped side arms which grip the oxygenator; located at 2, 5, 7 and 11 O'clock positions. The stem of the T consist of a 0.6 cm (thickness) × 13 cm (length) rod drilled through the main frame. The cross of the T consists of variable lengths of the same sheet as the mainframe attached to the stem by a screw mechanism. At the base of the T, is attached a circular handle (4 cm in diameter) made of 0.4 cm iron rod. RESULT: An oxygenator holder which weighs 1.75 kg with a total length of 54 cm (the diameter of the mainframe is 30 cm). Its advantages include (i) affordability, (ii) materials are locally accessible, (iii) versatility (iv) reproducibility. The disadvantages include, (i) it requires some time to fit, (ii) caution is required in fitting the oxygenator to avoid breakage, (iii) a spanner is required to lock the latch. CONCLUSION: The concept of a universal holder is pertinent, especially in resource poor environments to avoid purchasing a new holder whenever the usual oxygenator common to the centre is unavailable. This device is amenable to further modifications to meet the unforeseen challenges.

Arrow injury to the heart.

Arrow injury to the heart is a common cause of penetrating trauma in rural areas of underdeveloped nations. We report such a patient who survived 3 days after such an injury and traveled 1000 km with a pulsating arrow in the chest for treatment. The literature on such injuries is reviewed.