Prevalence of hyperemesis gravidarum and associated risk factors among pregnant women in a tertiary health facility in Northeast, Nigeria

Background: One of the commonest symptoms observed in pregnant women before the 20th week of gestation is nausea and vomiting, an exaggeration of these symptoms hyperemesis gravidarum (HEG) could result in maternal and fetal catastrophes and even death. The objective of this study was to determine the prevalence and associated risk factors for hyperemesis gravidarum among pregnant women at booking.Methods: A prospective institutional based study design was done among 452 pregnant women seen at booking in a tertiary hospital in Northeast Nigeria from the 1st February 2019 to 30th June 2019. Data was summarized using descriptive statistics. OR was used to measure significant risk.Results: The observed prevalence of hyperemesis gravidarum among pregnant women in the study is 44.9%. The Majority (81.4%) of these women were between the age range of 21 and 35 years. Mean age of 27 years. Multiparity (33.4%), previous (44.9%) and family history of HEG (31.6%) were identified as important risk factors for developing HEG. Grand multiparity (11.5%) and gestational age less than 13 weeks (6.64%) were however less likely observed to be risks for HEG.Conclusions: HEG is a common problem in pregnancy with almost half of the number of pregnant women at booking affected. Multiparity and past history of HEG are pointers to developing the condition and it should be looked out for among at risk group of pregnant women, so that early intervention can be instituted to avoid any possible adverse outcome

Gynaecological malignancies in Azare, North-East Nigeria: an assessment of types, stage at presentation and treatment affordability

Background: In many parts of the world, presentations for most gynecological cancers are late; this makes treatment difficult due to the cost of chemotherapy or radiotherapy which form the bedrock for cure or palliation. Objective of this study was to determine the types, stage at diagnosis, affordability of care and outcome of treatment of gynaecological cancers in Federal Medical Centre Azare, Bauchi State, Nigeria.Methods: All cases of gynaecological cancers seen over a ten-year period, from 1st January, 2003 to 31st December, 2012 were reviewed retrospectively. The number of all gynaecological cases seen during the period was also extracted.Results: Gynaecological cancer cases accounted for 11.84 % of 8,642 gynaecological cases seen during the period of study.  The mean age and parity of the women were 42±5 SD years and 5±1 SD respectively. Cervical cancer accounted for 55 %, ovarian cancer 30%, endometrial cancer 6%, choriocarcinoma 5%, secondaries/ cancers of undetermined origin were 4%.  Ninety-two percent presented with advanced stage of diseases. Only 25.3% could afford the cost of full treatment, and 8.4% attained cure of their disease. The modalities of treatment available were surgery and chemotherapy.Conclusions: Cervical and Ovarian Cancers remain the leading types of gynaecological cancers in our environment and late presentations are frequent occurrence. Late presentation and unaffordability of treatments are major challenges associated with the management of these patients. Early presentation and funding mechanisms for gynaecological cancers are keys to improved cure rate and reduced mortality

SOSIALISASI STOP BULLYING DI SDN 1 KATILOMBU

Bullying di sekolah dasar merupakan isu yang serius yang harus ditangani dengan cepat dan efektif. Kasus senacam ini membawa banyak kekhawatiran tidak hanya pada pihak sekolah tetapi juga pada orang tua, oleh karena itu perlunya dilakukan sosialisasi khususnya di sekolah dasar agar seluruh pihak harus lebih memahami segala hal tentang bullying, sehingga akan meminimalisir bahkan menghilangkan perilaku bullying. Metode yang dilakukan yaitu metode ceramah, pemateri menjelaskan tentang bahaya bullying selain itu metode yang digunakan metode tanya jawab tentang keseharian siswa-siswi di sekolah. Hasil dari kegiatan ini, siswa memahami dan mengetahui bahaya bullying dan pencegahannya

Yield and Yield Attributes of Extra-early Maize (Zea Mays L.) as Affected by Rates of Npk Fertilizer Succeeding Chilli Pepper (Capsicum Frutescens) Supplied with Different Rates Sheep Manure

Field experiment was conducted in 2005 and 2006 to study response of extra-early maize variety (95TZEE-Y1) to rates of NPK (0, 40:20:20, 80:40:40 and 120:60:60 kg N:P2O5:K2O ha-1) and residual FYM (0, 5, 10 and 15 t ha-1 applied to chilli pepper the previous season) in the semi-arid zone of Nigeria. Randomized complete block design with three replicates was used. Higher values for soil physical and chemical properties were obtained in plots supplied with manure the previous season with soil from 2006 experiment more fertile than for the first year, hence produced 21% more grain yield. All the applied NPK rates in 2005 and except 40:20:20 ha1 in 2006 had resulted in early maize crop as compared to control. Husked and de-husked cob and 100-grain weights and grain yield/ha were higher at 120:60:60 kg NPK ha-1. Maize grown in plot supplied with 15 t FYM ha1 the previous year matured earlier. Cobs and 100-grain weights and grain yield were highest in plot supplied with 10 t FYM ha1. The 10t FYM ha-1 had 69% and 68% more grain yield than the control in 2005 and 2006, respectively. Highest maize yield was obtained at 120:60:60 kg NPK ha-1 or 10t FYM ha-1. All the parameters measured significantly and positively related to each other when the two years data were combined

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 ( 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

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

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

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

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